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We have developed a middleware framework for workgroup environments that can support distributed software development and a variety of other application domains requiring document management and change management for distributed projects. The framework enables hypermedia-based integration of arbitrary legacy and new information resources available via a range of protocols, not necessarily known in advance to us as the general framework developers nor even to the environment instance designers. The repositories in which such information resides may be dispersed across the Internet and/or an organizational intranet. The framework also permits a range of client models for user and tool interaction, and applies an extensible suite of collaboration services, including but not limited to multi-participant workflow and coordination, to their information retrievals and updates. That is, the framework is interposed between clients, services and repositories - thus "middleware". We explain how our framework makes it easy to realize a comprehensive collection of workgroup and workflow features we culled from a requirements survey conducted by NASA.

In recent years several computational systems and techniques fortheorem proving by analogy have been developed. The obvious prac-tical question, however, as to whether and when to use analogy hasbeen neglected badly in these developments. This paper addresses thisquestion, identifies situations where analogy is useful, and discussesthe merits of theorem proving by analogy in these situations. Theresults can be generalized to other domains.

We study the global solution of Fredholm integral equations of the second kind by the help of Monte Carlo methods. Global solution means that we seek to approximate the full solution function. This is opposed to the usual applications of Monte Carlo, were one only wants to approximate a functional of the solution. In recent years several researchers developed Monte Carlo methods also for the global problem. In this paper we present a new Monte Carlo algorithm for the global solution of integral equations. We use multiwavelet expansions to approximate the solution. We study the behaviour of variance on increasing levels, and based on this, develop a new variance reduction technique. For classes of smooth kernels and right hand sides we determine the convergence rate of this algorithm and show that it is higher
than those of previously developed algorithms for the global problem. Moreover, an information-based complexity analysis shows that our algorithm is optimal among all stochastic algorithms of the same computational
cost and that no deterministic algorithm of the same cost can reach its convergence rate.

Die Verfahren der Induktiven Logischen Programmierung (ILP) [Mug93] haben die Aufgabe, aus einer Menge von positiven Beispielen E+, einer Menge von negativen Beispielen E und dem Hintergrundwissen B ein logisches Programm P zu lernen, das aus einer Menge von definiten Klauseln C : l0 l1, : : : ,ln besteht. Da der Hypothesenraum für Hornlogik unendlich ist, schränken viele Verfahren die Hypothesensprache auf eine endliche ein. Auch wird oft versucht, die Hypothesensprache so einzuschränken, dass nur Programme gelernt werden können, für die die Konsistenz entscheidbar ist. Eine andere Motivation, die Hypothesensprache zu beschränken, ist, dass das Wissen über das Zielprogramm, das schon vorhanden ist, ausgenutzt werden soll. So sind für bestimmte Anwendungen funktionsfreie Hypothesenklauseln ausreichend, oder es ist bekannt, dass das Zielprogramm funktional ist.

Rules are an important knowledge representation formalism in constructive problem solving. On the other hand, object orientation is an essential key technology for maintaining large knowledge bases as well as software applications. Trying to take advantage of the benefits of both paradigms, we integrated Prolog and Smalltalk to build a common base architecture for problem solving. This approach has proven to be useful in the development of two knowledge-based systems for planning and configuration design (CAPlan and Idax). Both applications use Prolog as an efficient computational source for the evaluation of knowledge represented as rules.

We present the adaptation process in a CBR application for decision support in the domain of industrial supervision. Our approach uses explanations to approximate relations between a problem description and its solution, and the adaptation process is guided by these explanations (a more detailed presentation has been done in [4]).

Typical instances, that is, instances that are representative for a particular situ-ation or concept, play an important role in human knowledge representationand reasoning, in particular in analogical reasoning. This wellADknown obser-vation has been a motivation for investigations in cognitive psychology whichprovide a basis for our characterization of typical instances within conceptstructures and for a new inference rule for justified analogical reasoning withtypical instances. In a nutshell this paper suggests to augment the proposi-tional knowledge representation system by a non-propositional part consistingof concept structures which may have directly represented instances as ele-ments. The traditional reasoning system is extended by a rule for justifiedanalogical inference with typical instances using information extracted fromboth knowledge representation subsystems.

The paper explores the role of artificial intelligence techniques in the development of an enhanced software project management tool, which takes account of the emerging requirement for support systems to address the increasing trend towards distributed multi-platform software development projects. In addressing these aims this research devised a novel architecture and framework for use as the basis of an intelligent assistance system for use by software project managers, in the planning and managing of a software project. This paper also describes the construction of a prototype system to implement this architecture and the results of a series of user trials on this prototype system.

Requirements engineering (RE) is a necessary part of the software development process, as it helps customers and designers identify necessary system requirements. If these stakeholders are separated by distance, we argue that a distributed groupware environment supporting a cooperative requirements engineering process must be supplied that allows them to negotiate software requirements. Such a groupware environment must support aspects of joint work relevant to requirements negotiation: synchronous and asynchronous collaboration, telepresence, and teledata. It should also add explicit support for a structured RE process, which includes the team's ability to discuss multiple perspectives during requirements acquisition and traceability. We chose the TeamWave software platform as an environment that supplied the basic collaboration capabilities, and tailored it to fit the specific needs of RE.

To prove difficult theorems in a mathematical field requires substantial know-ledge of that field. In this paper a frame-based knowledge representation formalismis presented, which supports a conceptual representation and to a large extent guar-antees the consistency of the built-up knowledge bases. We define a semantics ofthe representation by giving a translation into the underlaying logic.

We tested the GYROSTAR ENV-05S. This device is a sensor for angular velocity. There- fore the orientation must be calculated by integration of the angular velocity over time. The devices output is a voltage proportional to the angular velocity and relative to a reference. The test where done to find out under which conditions it is possible to use this device for estimation of orientation.

Unification in an Extensional Lambda Calculus with Ordered Function Sorts and Constant Overloading
(1999)

We develop an order-sorted higher-order calculus suitable forautomatic theorem proving applications by extending the extensional simplytyped lambda calculus with a higher-order ordered sort concept and constantoverloading. Huet's well-known techniques for unifying simply typed lambdaterms are generalized to arrive at a complete transformation-based unificationalgorithm for this sorted calculus. Consideration of an order-sorted logicwith functional base sorts and arbitrary term declarations was originallyproposed by the second author in a 1991 paper; we give here a correctedcalculus which supports constant rather than arbitrary term declarations, aswell as a corrected unification algorithm, and prove in this setting resultscorresponding to those claimed there.

This paper addresses two modi of analogical reasoning. Thefirst modus is based on the explicit representation of the justificationfor the analogical inference. The second modus is based on the repre-sentation of typical instances by concept structures. The two kinds ofanalogical inferences rely on different forms of relevance knowledge thatcause non-monotonicity. While the uncertainty and non-monotonicity ofanalogical inferences is not questioned, a semantic characterization ofanalogical reasoning has not been given yet. We introduce a minimalmodel semantics for analogical inference with typical instances.

Most automated theorem provers suffer from the problemthat the resulting proofs are difficult to understand even for experiencedmathematicians. An effective communication between the system andits users, however, is crucial for many applications, such as in a mathematical assistant system. Therefore, efforts have been made to transformmachine generated proofs (e.g. resolution proofs) into natural deduction(ND) proofs. The state-of-the-art procedure of proof transformation fol-lows basically its completeness proof: the premises and the conclusionare decomposed into unit literals, then the theorem is derived by mul-tiple levels of proofs by contradiction. Indeterminism is introduced byheuristics that aim at the production of more elegant results. This inde-terministic character entails not only a complex search, but also leads tounpredictable results.In this paper we first study resolution proofs in terms of meaningful op-erations employed by human mathematicians, and thereby establish acorrespondence between resolution proofs and ND proofs at a more ab-stract level. Concretely, we show that if its unit initial clauses are CNFsof literal premises of a problem, a unit resolution corresponds directly toa well-structured ND proof segment that mathematicians intuitively un-derstand as the application of a definition or a theorem. The consequenceis twofold: First it enhances our intuitive understanding of resolutionproofs in terms of the vocabulary with which mathematicians talk aboutproofs. Second, the transformation process is now largely deterministicand therefore efficient. This determinism also guarantees the quality ofresulting proofs.

In order to improve the quality of software systems and to set up a more effective process for their development, many attempts have been made in the field of software engineering. Reuse of existing knowledge is seen as a promising way to solve the outstanding problems in this field. In previous work we have integrated the design pattern concept with the formal design language SDL, resulting in a certain kind of pattern formalization. For the domain of communication systems we have also developed a pool of SDL patterns with an accompanying process model for pattern application. In this paper we present an extension that combines the SDL pattern approach with the experience base concept. This extension supports a systematic method for empirical evaluation and continuous improvement of the SDL pattern approach. Thereby the experience base serves as a repository necessary for effective reuse of the captured knowledge. A comprehensive usage scenario is described which shows the advantages of the combined approach. To demonstrate its feasibility, first results of a research case study are given.

This paper focuses on the issues involved when multiple mobile agents interact in multiagent systems. The application is an intelligent agent market place, where buyer and seller agents cooperate and compete to process sales transactions for their owners. The market place manager acts as afacilitator by giving necessary information to agents and managing communication between agents, and also as a mediator by proposing solutions to agents or stopping them to get into infinite loops bargaining back and forth.The buyer and seller agents range from using hardcoded logic to rule-based inferencing in their negotiation strategies. However these agents must support some communication skills using KQML or FIPA-ACL.So in contrast with other approaches to multiagent negotiation, we introduce an explicit mediator (market place manager) into the negotiation, and we propose a negotiation strategy based on dependence theory [1] implemented by our best buyers and best sellers.

Using an experience factory is one possible concept for supporting and improving reuse in software development. (i.e., reuse of products, processes, quality models, ...). In the context of the Sonderforschungsbereich 501: "Development of Large Systems with Generic methods" (SFB501), the Software Engineering Laboratory (SE Lab) runs such an experience factory as part of the infrastructure services it offers. The SE Lab also provides several tools to support the planning, developing, measuring, and analyzing activities of software development processes. Among these tools, the SE Lab runs and maintains an experience base, the SFB-EB. When an experience factory is utilized, support for experience base maintenance is an important issue. Furthermore, it might be interesting to evaluate experience base usage with regard to the number of accesses to certain experience elements stored in the database. The same holds for the usage of the tools provided by the SE LAB. This report presents a set of supporting tools that were designed to aid in these tasks. These supporting tools check the experience base's consistency and gather information on the usage of SFB-EB and the tools installed in the SE Lab. The results are processed periodically and displayed as HTML result reports (consistency checking) or bar charts (usage profiles).

In this paper we are interested in an algebraic specification language that (1) allowsfor sufficient expessiveness, (2) admits a well-defined semantics, and (3) allows for formalproofs. To that end we study clausal specifications over built-in algebras. To keep thingssimple, we consider built-in algebras only that are given as the initial model of a Hornclause specification. On top of this Horn clause specification new operators are (partially)defined by positive/negative conditional equations. In the first part of the paper wedefine three types of semantics for such a hierarchical specification: model-theoretic,operational, and rewrite-based semantics. We show that all these semantics coincide,provided some restrictions are met. We associate a distinguished algebra A spec to ahierachical specification spec. This algebra is initial in the class of all models of spec.In the second part of the paper we study how to prove a theorem (a clause) valid in thedistinguished algebra A spec . We first present an abstract framework for inductive theoremprovers. Then we instantiate this framework for proving inductive validity. Finally wegive some examples to show how concrete proofs are carried out.This report was supported by the Deutsche Forschungsgemeinschaft, SFB 314 (D4-Projekt)

This paper shows how a new approach to theorem provingby analogy is applicable to real maths problems. This approach worksat the level of proof-plans and employs reformulation that goes beyondsymbol mapping. The Heine-Borel theorem is a widely known result inmathematics. It is usually stated in R 1 and similar versions are also truein R 2 , in topology, and metric spaces. Its analogical transfer was proposedas a challenge example and could not be solved by previous approachesto theorem proving by analogy. We use a proof-plan of the Heine-Boreltheorem in R 1 as a guide in automatically producing a proof-plan of theHeine-Borel theorem in R 2 by analogy-driven proof-plan construction.

Most automated theorem provers suffer from the problem that theycan produce proofs only in formalisms difficult to understand even forexperienced mathematicians. Efforts have been made to transformsuch machine generated proofs into natural deduction (ND) proofs.Although the single steps are now easy to understand, the entire proofis usually at a low level of abstraction, containing too many tedioussteps. Therefore, it is not adequate as input to natural language gen-eration systems.To overcome these problems, we propose a new intermediate rep-resentation, called ND style proofs at the assertion level . After illus-trating the notion intuitively, we show that the assertion level stepscan be justified by domain-specific inference rules, and that these rulescan be represented compactly in a tree structure. Finally, we describea procedure which substantially shortens ND proofs by abstractingthem to the assertion level, and report our experience with furthertransformation into natural language.

Comprehensive reuse and systematic evolution of reuse artifacts as proposed by the Quality Improvement Paradigm (QIP) do not only require tool support for mere storage and retrieval. Rather, an integrated management of (potentially reusable) experience data as well as project-related data is needed. This paper presents an approach exploiting object-relational database technology to implement the QIP-driven reuse repository of the SFB 501. Requirements, concepts, and implementational aspects are discussed and illustrated through a running example, namely the reuse and continuous improvement of SDL patterns for developing distributed systems. Based on this discussion, we argue that object-relational database management systems (ORDBMS) are best suited to implement such a comprehensive reuse repository. It is demonstrated how this technology can be used to support all phases of a reuse process and the accompanying improvement cycle. Although the discussions of this paper are strongly related to the requirements of the SFB 501 experience base, the basic realization concepts, and, thereby, the applicability of ORDBMS, can easily be extended to similar applications, i. e., reuse repositories in general.

Approximation properties of the underlying estimator are used to improve the efficiency of the method of dependent tests. A multilevel approximation procedure is developed such that in each level the number of samples is balanced with the level-dependent variance, resulting in a considerable reduction of the overall computational cost. The new technique is applied to the Monte Carlo estimation of integrals depending on a parameter.

We present an empirical study of mathematical proofs by diagonalization, the aim istheir mechanization based on proof planning techniques. We show that these proofs canbe constructed according to a strategy that (i) finds an indexing relation, (ii) constructsa diagonal element, and (iii) makes the implicit contradiction of the diagonal elementexplicit. Moreover we suggest how diagonal elements can be represented.

In this report we present a case study of employing goal-oriented heuristics whenproving equational theorems with the (unfailing) Knut-Bendix completion proce-dure. The theorems are taken from the domain of lattice ordered groups. It will bedemonstrated that goal-oriented (heuristic) criteria for selecting the next critical paircan in many cases significantly reduce the search effort and hence increase per-formance of the proving system considerably. The heuristic, goalADoriented criteriaare on the one hand based on so-called "measures" measuring occurrences andnesting of function symbols, and on the other hand based on matching subterms.We also deal with the property of goal-oriented heuristics to be particularly helpfulin certain stages of a proof. This fact can be addressed by using them in a frame-work for distributed (equational) theorem proving, namely the "teamwork-method".

Orderings on polynomial interpretations of operators represent a powerful technique for proving thetermination of rewriting systems. One of the main problems of polynomial orderings concerns thechoice of the right interpretation for a given rewriting system. It is very difficult to develop techniquesfor solving this problem. Here, we present three new heuristic approaches: (i) guidelines for dealingwith special classes of rewriting systems, (ii) an algorithm for choosing appropriate special polynomialsas well as (iii) an extension of the original polynomial ordering which supports the generation ofsuitable interpretations. All these heuristics will be applied to examples in order to illustrate theirpractical relevance.

We transform a user-friendly formulation of aproblem to a machine-friendly one exploiting the variabilityof first-order logic to express facts. The usefulness of tacticsto improve the presentation is shown with several examples.In particular it is shown how tactical and resolution theoremproving can be combined.

Versions- und Konfigurationsmanagement sind zentrale Instrumente zur intellektuellen Beherrschung komplexer Softwareentwicklungen. In stark wiederverwendungsorientierten Softwareentwicklungsansätzen -wie vom SFB bereitgestellt- muß der Begriff der Konfiguration von traditionell produktorientierten Artefakten auf Prozesse und sonstige Entwicklungserfahrungen erweitert werden. In dieser Veröffentlichung wird ein derartig erweitertes Konfigurationsmodell vorgestellt. Darüberhinau wird eine Ergänzung traditioneller Projektplanungsinformationen diskutiert, die die Ableitung maßgeschneiderter Versions- und Konfigurationsmanagementmechanismen vor Projektbeginn ermöglichen.

Verfahren des Maschinellen Lernens haben heute eine Reife erreicht, die zu ersten erfolgreichen industriellen Anwendungen geführt hat. In der Prozessdiagnose und -steuerung ermöglichen Lernverfahren die Klassifikation und Bewertung von Betriebszuständen, d.h. eine Grobmodellierung eines Prozesses, wenn dieser nicht oder nur teilweise mathematisch beschreibbar ist. Ausserdem gestatten Lernverfahren die automatische Generierung von Klassifizierungsprozeduren, die deterministisch abgearbeitet werden und daher für die Belange der Echtzeitdiagnose und -steuerung u.U. zeiteffektiver als Inferenzmechanismen auf logischer bzw. Produktionsregelbasis sind, da letztere immer mit zeitaufwendigen Suchprozessen verbunden sind.

A growing share of all software development project work is being done by geographically distributed teams. To satisfy shorter product design cycles, expert team members for a development project may need to be r ecruited globally. Yet to avoid extensive travelling or r eplacement costs, distributed project work is preferred. Current-generation software engineering tools and ass ociated systems, processes, and methods were for the most part developed to be used within a single enterprise. Major innovations have lately been introduced to enable groupware applications on the Internet to support global collaboration. However, their deployment for distributed software projects requires further research. In partic ular, groupware methods must seamlessly be integrated with project and product management systems to make them attractive for industry. In this position paper we outline the major challenges concerning distributed (virtual) software projects. Based on our experiences with software process modeling and enactment environments, we then propose approaches to solve those challenges.

Struktur und Werkzeuge des experiment-spezifischen Datenbereichs der SFB501 Erfahrungsdatenbank
(1999)

Software-Entwicklungsartefakte müssen zielgerichtet während der Durchführung eines Software- Projekts erfasst werden, um für die Wiederverwendung aufbereitet werden zu können. Die methodische Basis hierzu bildet im Sonderforschungsbereich 501 das Konzept der Erfahrungsdatenbank. In ihrem experiment-spezifischen Datenbereich werden für jedes Entwicklungsprojekt alle Software-Entwicklungsartefakte abgelegt, die während des Lebenszyklus eines Projektes anfallen. In ihrem übergreifenden Datenbereich werden all die jenigen Artefakte aus dem experiment-spezifischen Datenbereich zusammengefasst, die für eine Wiederverwendung in nachfolgenden Projekten in Frage kommen. Es hat sich gezeigt, dass bereits zur Nutzung der Datenmengen im experiment- spezifischen Datenbereich der Erfahrungsdatenbank ein systematischer Zugriff notwendig ist. Ein systematischer Zugriff setzt jedoch eine normierte Struktur voraus. Im experiment-spezifischen Bereich werden zwei Arten von Experimenttypen unterschieden: "Kontrollierte Experimente" und "Fallstudien". Dieser Bericht beschreibt die Ablage- und Zugriffsstruktur für den Experimenttyp "Fallstudien". Die Struktur wurde aufgrund der Erfahrungen in ersten Fallstudien entwickelt und evaluiert.

About the approach The approach of TOPO was originally developed in the FABEL project1[1] to support architects in designing buildings with complex installations. Supplementing knowledge-based design tools, which are available only for selected subtasks, TOPO aims to cover the whole design process. To that aim, it relies almost exclusively on archived plans. Input to TOPO is a partial plan, and output is an elaborated plan. The input plan constitutes the query case and the archived plans form the case base with the source cases. A plan is a set of design objects. Each design object is defined by some semantic attributes and by its bounding box in a 3-dimensional coordinate system. TOPO supports the elaboration of plans by adding design objects.

Zur schnellen Kommunikation zwischen Rechnern werden laufzeiteffiziente Implementationen von Protokoll-Spezifikationen benötigt. Die herkömmliche Schichten-Aufteilung verursacht hohe Kosten. In dieser Projektarbeit wurde eine andere Spezifikationsform, die Methode des strukturierten Produktautomaten, am Beispiel der OSI-Schichten 5 und 6 untersucht. Der Aufwand zur Erstellung und Wartung der Spezifikation und die Laufzeiteffizienz der daraus entstandenen Inplementation wurden mit mehreren anderen Spezifikationsformen verglichen und bewertet. Die Methode des strukturierten Produktautomaten erwies sich dabei als ein geeigneter Spezifikationsstil.

In order to reduce the elapsed time of a computation, a pop-ular approach is to decompose the program into a collection of largelyindependent subtasks which are executed in parallel. Unfortunately, it isoften observed that tightly-coupled parallel programs run considerablyslower than initially expected. In this paper, a framework for the anal-ysis of parallel programs and their potential speedup is presented. Twoparameters which strongly affect the scalability of parallelism are iden-tified, namely the grain of synchronization, and the degree to which thetarget hardware is available. It is shown that for certain classes of appli-cations speedup is inherently poor, even if the program runs under theidealized conditions of perfect load balance, unbounded communicationbandwidth and negligible communication and parallelization overhead.Upper bounds are derived for the speedup that can be obtained in threedifferent types of computations. An example illustrates the main find-ings.

A non-trivial real-time requirement obeying a pattern that can be foundin various instantiations in the application domain building automation, and which is therefore called generic, is investigated in detail. Starting point is a description of a real-time problem in natural language augmented by a diagram, in a style often found in requirements documents. Step by step, this description is made more precise and finally transformed into a surprisingly concise formal specification, written in real-time temporal logic with customized operators. Wereason why this formal specification precisely captures the original description- as far as this is feasible due to the lack of precision of natural language.

Extending existing calculi by sorts is astrong means for improving the deductive power offirst-order theorem provers. Since many mathemat-ical facts can be more easily expressed in higher-orderlogic - aside the greater power of higher-order logicin principle - , it is desirable to transfer the advant-ages of sorts in the first-order case to the higher-ordercase. One possible method for automating higher-order logic is the translation of problem formulationsinto first-order logic and the usage of first-order the-orem provers. For a certain class of problems thismethod can compete with proving theorems directlyin higher-order logic as for instance with the TPStheorem prover of Peter Andrews or with the Nuprlproof development environment of Robert Constable.There are translations from unsorted higher-order lo-gic based on Church's simple theory of types intomany-sorted first-order logic, which are sound andcomplete with respect to a Henkin-style general mod-els semantics. In this paper we extend correspond-ing translations to translations of order-sorted higher-order logic into order-sorted first-order logic, thus weare able to utilize corresponding first-order theoremprover for proving higher-order theorems. We do notuse any (lambda)-expressions, therefore we have to add so-called comprehension axioms, which a priori makethe procedure well-suited only for essentially first-order theorems. However, in practical applicationsof mathematics many theorems are essentially first-order and as it seems to be the case, the comprehen-sion axioms can be mastered too.

An important research problem is the incorporation of "declarative" knowledge into an automated theorem prover that can be utilized in the search for a proof. An interesting pro-posal in this direction is Alan Bundy's approach of using explicit proof plans that encapsulatethe general form of a proof and is instantiated into a particular proof for the case at hand. Wegive some examples that show how a "declarative" highlevel description of a proof can be usedto find proofs of apparently "similiar" theorems by analogy. This "analogical" information isused to select the appropriate axioms from the database so that the theorem can be proved.This information is also used to adjust some options of a resolution theorem prover. In orderto get a powerful tool it is necessary to develop an epistemologically appropriate language todescribe proofs, for which a large set of examples should be used as a testbed. We presentsome ideas in this direction.

The development of software products has become a highly cooperative and distributed activity involving working groups at geographically distinct places. These groups show an increasing mobility and a very flexible organizational structure. Process methodology and technology have to take such evolutions into account. A possible direction for the emergence of new process technology and methodology is to take benefit from recent advances within multiagent systems engineering : innovative methodologies for adaptable and autonomous architectures; they exhibit interesting features to support distributed software processes.

SmallSync, an internet event synchronizer, is intended to provide a monitoring and visualization methodology for permitting simultaneous analysis and control of multiple remote processes on the web. The current SmallSync includes: (1) a mechanism to multicast web window-based commands, message passing events and process execution events among processes; (2) an event synchronizer to allow concurrent execution of some functions on multiple machines; (3) a means to report when these events cause errors in the processes; and (4) ad hoc visualization of process states using existing visualizers.

Patdex is an expert system which carries out case-based reasoning for the fault diagnosis of complex machines. It is integrated in the Moltke workbench for technical diagnosis, which was developed at the university of Kaiserslautern over the past years, Moltke contains other parts as well, in particular a model-based approach; in Patdex where essentially the heuristic features are located. The use of cases also plays an important role for knowledge acquisition. In this paper we describe Patdex from a principal point of view and embed its main concepts into a theoretical framework.

In this paper, we compare the BERKOM globally ac-cessible services project (GLASS) with the well-knownWorld-Wide Web with respect to the ease of development,realization, and distribution of multimedia presentations.This comparison is based on the experiences we gainedwhen implementing a gateway between GLASS and theWorld-Wide Web. Since both systems are shown to haveobvious weaknesses, we are concluding this paper with apresentation of a better way to multimedia document en-gineering and distribution. This concept is based on awell-accepted approach to function-shipping in the Inter-net: the Java language, permitting for example a smoothintegration of GLASS92 MHEG objects and WWW HTMLpages within one common environment.

Reusing Proofs
(1999)

We develop a learning component for a theorem prover designed for verifying statements by mathematical induction. If the prover has found a proof, it is analyzed yielding a so-called catch. The catch provides the features of the proof which are relevant for reusing it in subsequent verification tasks and may also suggest useful lemmata. Proof analysis techniques for computing the catch are presented. A catch is generalized in a certain sense for increasing the reusability of proofs. We discuss problems arising when learning from proofs and illustrate our method by several examples.

This paper describes a declarative approach forencoding the plan operators in proof planning,the so-called methods. The notion of methodevolves from the much studied concept of a tac-tic and was first used by A. Bundy. Signific-ant deductive power has been achieved withthe planning approach towards automated de-duction; however, the procedural character ofthe tactic part of methods hinders mechanicalmodification. Although the strength of a proofplanning system largely depends on powerfulgeneral procedures which solve a large class ofproblems, mechanical or even automated modi-fication of methods is necessary, since methodsdesigned for a specific type of problems willnever be general enough. After introducing thegeneral framework, we exemplify the mechan-ical modification of methods via a particularmeta-method which modifies methods by trans-forming connectives to quantifiers.

We examine an approach for demand-driven cooperative theorem proving.We briefly point out the problems arising from the use of common success-driven cooperation methods, and we propose the application of our approachof requirement-based cooperative theorem proving. This approach allows for abetter orientation on current needs of provers in comparison with conventional co-operation concepts. We introduce an abstract framework for requirement-basedcooperation and describe two instantiations of it: Requirement-based exchangeof facts and sub-problem division and transfer via requests. Finally, we reporton experimental studies conducted in the areas superposition and unfailing com-pletion.The author was supported by the Deutsche Forschungsgemeinschaft (DFG).

Der Wissenserwerb erschwert bisher häufig den Einsatz wissensbasierter Systeme der Arbeitsplanerstellung in der industriellen Praxis. Die meisten Anwendungen gestatten nur das Erfassen und Editieren des durch aufwendige Erhebung, Systematisierung und Formulierung gewonnenen fachspezifischen Planungswissens. Im Rahmen eines DFG-Projektes soll die Anwendbarkeit bekannter maschineller Lernverfahren auf technologische Reihenfolge- und Zuordnungsprobleme im Rahmen der generierenden Arbeitsplanerstellung von Teilefertigungsprozessen im Maschinenbau nachgewiesen werden. Dazu wird ein Prototyp mit Hilfe eines verfügbaren Softwarewerkzeuges entwickelt, der das maschinelle Lernen aus vorgegebenen Beispielen ermöglichen und mit einem existierenden Prototypen der wissensbasierten Arbeistplanung kommunizieren soll. Der folgende Beitrag gibt einen Überblick über das mit Lernverfahren zu behandelnde Planungswissen und stellt mögliche Repräsentationsmöglichkeiten des Wissens zur Diskussion.

Emerging technologies such as the Internet, the World Wide Web, JavaTM technology, and software components, are changing the software business. Activities that have in the past been constrained by the need for intense information management increasingly involve cooperating organizations. Information management tools and techniques do not scale well in the face of this organizational complexity. An informal approach to information sharing, based largely on manual copying of information, cannot meet the demands of the task as size and complexity increase. Formal approaches to sharing information are based on groupware tools, but cooperating organizations do not always enjoy the trust or commonality of sophisticated infrastructure, methods, and skills that this approach requires. Bridging the gap requires a simple, loosely coupled, highly flexible strategy for information sharing. Extensive information relevant to different parts of the software life cycle should be interconnected in a simple, easily described way; such connections should permit selective information sharing by a variety of tools and in a variety of collaboration modes that vary in the amount of organizational coupling they require.

A straightforward formulation of a mathematical problem is mostly not ad-equate for resolution theorem proving. We present a method to optimize suchformulations by exploiting the variability of first-order logic. The optimizingtransformation is described as logic morphisms, whose operationalizations aretactics. The different behaviour of a resolution theorem prover for the sourceand target formulations is demonstrated by several examples. It is shown howtactical and resolution-style theorem proving can be combined.

Most automated theorem provers suffer from the problem thatthey can produce proofs only in formalisms difficult to understand even forexperienced mathematicians. Effort has been made to reconstruct naturaldeduction (ND) proofs from such machine generated proofs. Although thesingle steps in ND proofs are easy to understand, the entire proof is usuallyat a low level of abstraction, containing too many tedious steps. To obtainproofs similar to those found in mathematical textbooks, we propose a newformalism, called ND style proofs at the assertion level , where derivationsare mostly justified by the application of a definition or a theorem. Aftercharacterizing the structure of compound ND proof segments allowing asser-tion level justification, we show that the same derivations can be achieved bydomain-specific inference rules as well. Furthermore, these rules can be rep-resented compactly in a tree structure. Finally, we describe a system calledPROVERB , which substantially shortens ND proofs by abstracting them tothe assertion level and then transforms them into natural language.

The hallmark of traditional Artificial Intelligence (AI) research is the symbolic representation and processing of knowledge. This is in sharp contrast to many forms of human reasoning, which to an extraordinary extent, rely on cases and (typical) examples. Although these examples could themselves be encoded into logic, this raises the problem of restricting the corresponding model classes to include only the intended models.There are, however, more compelling reasons to argue for a hybrid representa-tion based on assertions as well as examples. The problems of adequacy, availability of information, compactness of representation, processing complexity, and last but not least, results from the psychology of human reasoning, all point to the same conclusion: Common sense reasoning requires different knowledge sources and hybrid reasoning principles that combine symbolic as well as semantic-based inference. In this paper we address the problem of integrating semantic representations of examples into automateddeduction systems. The main contribution is a formal framework for combining sentential with direct representations. The framework consists of a hybrid knowledge base, made up of logical formulae on the one hand and direct representations of examples on the other, and of a hybrid reasoning method based on the resolution calculus. The resulting hybrid resolution calculus is shown to be sound and complete.

A lot of the human ability to prove hard mathematical theorems can be ascribedto a problem-specific problem solving know-how. Such knowledge is intrinsicallyincomplete. In order to prove related problems human mathematicians, however,can go beyond the acquired knowledge by adapting their know-how to new relatedproblems. These two aspects, having rich experience and extending it by need, can besimulated in a proof planning framework: the problem-specific reasoning knowledge isrepresented in form of declarative planning operators, called methods; since these aredeclarative, they can be mechanically adapted to new situations by so-called meta-methods. In this contribution we apply this framework to two prominent proofs intheorem proving, first, we present methods for proving the ground completeness ofbinary resolution, which essentially correspond to key lemmata, and then, we showhow these methods can be reused for the proof of the ground completeness of lockresolution.

This paper addresses analogy-driven auto-mated theorem proving that employs a sourceproof-plan to guide the search for a proof-planof the target problem. The approach presen-ted uses reformulations that go beyond symbolmappings and that incorporate frequently usedre-representations and abstractions. Severalrealistic math examples were successfully pro-cessed by our analogy-driven proof-plan con-struction. One challenge example, a Heine-Borel theorem, is discussed here. For this ex-ample the reformulaitons are shown step bystep and the modifying actions are demon-strated.

This paper outlines an implemented system called PROVERB that explains machine -found natural deduction proofs in natural language. Different from earlier works, we pursue a reconstructive approach. Based on the observation that natural deduction proofs are at a too low level of abstraction compared with proofs found in mathematical textbooks, we define first the concept of so-called assertion level inference rules. Derivations justified by these rules can intuitively be understood as the application of a definition or a theorem. Then an algorithm is introduced that abstracts machine-found ND proofs using the assertion level inference rules. Abstracted proofs are then verbalized into natural language by a presentation module. The most significant feature of the presentation module is that it combines standard hierarchical text planning and techniques that locally organize argumentative texts based on the derivation relation under the guidance of a focus mechanism. The behavior of the system is demonstrated with the help of a concrete example throughout the paper.

Gerade in einer von Globalisierungstendenzen geprägten Zeit wie heute - in der sowohl das Angebot als auch die Nachfrage an Informationen exponentiell wächst - gewinnen Publikationsserver an zunehmender Bedeutung: Die weltweite Verfügbarkeit und Recherchierbarkeit von elektronisch gespeicherten Ressourcen (bei gleichzeitig minimalen Kosten) sind zentrale Forderungen der Informationsgesellschaft und Antriebsmotor für internationale Informationsstrukturen. Bei der Konzeption von modernen Publikationsservern sollte daher a priori globales Denken und international anerkannte Konzepte im Vordergrund stehen. Ziel der vorliegenden Arbeit war die Unterstützung des Aufbaus eines Dokumentenservers für die Universität Kaiserslautern, der diesen Ansprüchen genügt. Im Rahmen dieser von Herrn Dr. Wolfgang Lenski (Fachbereich Informatik) betreuten Arbeit habe ich daher im ersten Teil als Orientierungsmöglichkeit eine Studie über aktuelle Bedürfnisse und Lösungsansätze angefertigt und die wesentlichsten Erkenntnisse niedergeschrieben: Nach der Formulierung eines Anforderungskataloges für Server, Ressourcen und Retrieval wird das international verbreitete Konzept der Metadaten als Ressourcenbeschreibung vorgestellt. Dieses dient zur Unterstützung effizienter und qualifizierter Retrievalverfahren die ihre Anwendung in komplexeren Systemen, wie beispielsweise dem, an der University of Colorado entwickelten, Resource-Discovery-System Harvest finden. Aufbauend auf diesen Erkenntnissen zeigt der zweite Teil in exemplarischen Zügen die Applizierung der vorgestellten Konzepte anhand der konkreten Realisation des Dokumentenservers KLUEDO der Universität Kaiserslautern. Ich beschränke mich hierbei im Wesentlichen auf den von mir entworfenen Teil der Systemarchitektur und das entwickelte Metadatenmanagementsystem.

This paper outlines the linguistic part of an implemented system namedPROVERB[3] that transforms, abstracts, and verbalizes machine-found proofs innatural language. It aims to illustrate, that state-of-the-art techniques of natural language processing are necessary to produce coherent texts that resemble those found in typical mathematical textbooks, in contrast to the belief that mathematical texts are only schematic and mechanical.The verbalization module consists of a content planner, a sentence planner, and a syntactic generator. Intuitively speaking, the content planner first decides the order in which proof steps should be conveyed. It also some messages to highlight global proof structures. Subsequently, thesentence planner combines and rearranges linguistic resources associated with messages produced by the content planner in order to produce connected text. The syntactic generat or finally produces the surface text.

This paper describes the linguistic part of a system called PROVERB, which transforms, abstracts,and verbalizes machine-found proofs into formatedtexts. Linguistically, the architecture of PROVERB follows most application oriented systems, and is a pipelined control of three components. Its macroplanner linearizes a proof and plans mediating communicative acts by employing a combination of hierarchical planning and focus-guided navigation. The microplanner then maps communicative acts and domain concepts into linguistic resources, paraphrases and aggregates such resources to producethe final Text Structure. A Text Structure contains all necessary syntactic information, and can be executed by our realizer into grammatical sentences. The system works fully automatically and performs particularly well for textbook size examples.

We first show that ground term-rewriting systems can be completed in apolynomial number of rewriting steps, if the appropriate data structure for termsis used. We then apply this result to study the lengths of critical pair proofs innon-ground systems, and obtain bounds on the lengths of critical pair proofsin the non-ground case. We show how these bounds depend on the types ofinference steps that are allowed in the proofs.

Proof planning is an alternative methodology to classical automated theorem prov-ing based on exhausitve search that was first introduced by Bundy [8]. The goal ofthis paper is to extend the current realm of proof planning to cope with genuinelymathematical problems such as the well-known limit theorems first investigated for au-tomated theorem proving by Bledsoe. The report presents a general methodology andcontains ideas that are new for proof planning and theorem proving, most importantlyideas for search control and for the integration of domain knowledge into a general proofplanning framework. We extend proof planning by employing explicit control-rules andsupermethods. We combine proof planning with constraint solving. Experiments showthe influence of these mechanisms on the performance of a proof planner. For instance,the proofs of LIM+ and LIM* have been automatically proof planned in the extendedproof planner OMEGA.In a general proof planning framework we rationally reconstruct the proofs of limittheorems for real numbers (IR) that were first computed by the special-purpose programreported in [6]. Compared with this program, the rational reconstruction has severaladvantages: It relies on a general-purpose problem solver; it provides high-level, hi-erarchical representations of proofs that can be expanded to checkable ND-proofs; itemploys declarative contol knowledge that is modularly organized.

This paper outlines an implemented system named PROVERBthat transforms and abstracts machine-found proofs to natural deduction style proofs at an adequate level of abstraction and then verbalizesthem in natural language. The abstracted proofs, originally employedonly as an intermediate representation, also prove to be useful for proofplanning and proving by analogy.

Planverfahren
(1999)

This paper deals with the reference choices involved in thegeneration of argumentative text. A piece of argument-ative text such as the proof of a mathematical theoremconveys a sequence of derivations. For each step of de-rivation, the premises (previously conveyed intermediateresults) and the inference method (such as the applica-tion of a particular theorem or definition) must be madeclear. The appropriateness of these references cruciallyaffects the quality of the text produced.Although not restricted to nominal phrases, our refer-ence decisions are similar to those concerning nominalsubsequent referring expressions: they depend on theavailability of the object referred to within a context andare sensitive to its attentional hierarchy . In this paper,we show how the current context can be appropriatelysegmented into an attentional hierarchy by viewing textgeneration as a combination of planned and unplannedbehavior, and how the discourse theory of Reichmann canbe adapted to handle our special reference problem.

In this article we formally describe a declarative approach for encoding plan operatorsin proof planning, the so-called methods. The notion of method evolves from the much studiedconcept tactic and was first used by Bundy. While significant deductive power has been achievedwith the planning approach towards automated deduction, the procedural character of the tacticpart of methods, however, hinders mechanical modification. Although the strength of a proofplanning system largely depends on powerful general procedures which solve a large class ofproblems, mechanical or even automated modification of methods is nevertheless necessary forat least two reasons. Firstly methods designed for a specific type of problem will never begeneral enough. For instance, it is very difficult to encode a general method which solves allproblems a human mathematician might intuitively consider as a case of homomorphy. Secondlythe cognitive ability of adapting existing methods to suit novel situations is a fundamentalpart of human mathematical competence. We believe it is extremely valuable to accountcomputationally for this kind of reasoning.The main part of this article is devoted to a declarative language for encoding methods,composed of a tactic and a specification. The major feature of our approach is that the tacticpart of a method is split into a declarative and a procedural part in order to enable a tractableadaption of methods. The applicability of a method in a planning situation is formulatedin the specification, essentially consisting of an object level formula schema and a meta-levelformula of a declarative constraint language. After setting up our general framework, wemainly concentrate on this constraint language. Furthermore we illustrate how our methodscan be used in a Strips-like planning framework. Finally we briefly illustrate the mechanicalmodification of declaratively encoded methods by so-called meta-methods.

We describe a hybrid architecture supporting planning for machining workpieces. The archi- tecture is built around CAPlan, a partial-order nonlinear planner that represents the plan already generated and allows external control decision made by special purpose programs or by the user. To make planning more efficient, the domain is hierarchically modelled. Based on this hierarchical representation, a case-based control component has been realized that allows incremental acquisition of control knowledge by storing solved problems and reusing them in similar situations.

This paper presents a new way to use planning in automated theorem provingby means of distribution. To overcome the problem that often subtasks fora proof problem can not be detected a priori (which prevents the use of theknown planning and distribution techniques) we use a team of experts that workindependently with different heuristics on the problem. After a certain amount oftime referees judge their results using the impact of the results on the behaviourof the expert and a supervisor combines the selected results to a new startingpoint.This supervisor also selects the experts that can work on the problem inthe next round. This selection is a reactive planning task. We outline whichinformation the supervisor can use to fulfill this task and how this informationis processed to result in a plan or to revise a plan. We also show that the useof planning for the assignment of experts to the team allows the system to solvemany different examples in an acceptable time with the same start configurationand without any consultation of the user.Plans are always subject to changeShin'a'in proverb

This report is a first attempt of formalizing the diagonalization proof technique.We give a strategy how to systematically construct diagonalization proofs: (i) findingan indexing relation, (ii) constructing a diagonal element, and (iii) making the implicitcontradiction of the diagonal element explicit. We suggest a declarative representationof the strategy and describe how it can be realized in a proof planning environment.

Planning Argumentative Texts
(1999)

This paper presents PROVERB a text planner forargumentative texts. PROVERB's main feature isthat it combines global hierarchical planning and un-planned organization of text with respect to local de-rivation relations in a complementary way. The formersplits the task of presenting a particular proof intosubtasks of presenting subproofs. The latter simulateshow the next intermediate conclusion to be presentedis chosen under the guidance of the local focus.

Abstraction is one of the most promising approaches to improve the performance of problem solvers. Abstraction by dropping sentences of a domain description - as used in most hierarchical planners - is known to be very representation dependent. To overcome these drawbacks, we propose a more general view of abstraction involving the change of representation language. We have developed a new abstraction methodology and a related sound and complete learning algorithm that allows the complete change of representation language of planning cases from concrete to abstract.

This paper deals with the problem of picking-up deformable linear workpieces such as cables or ropes with an industrial robot. First, we give a motivation and problem definition. Based on a brief conceptual discussion of possible approaches we derive an algorithm for picking-up hanging deformable linear objects using two light barriers as sensor system. For this hardware, a skill-based approach is described and the parameters and major influence factors are discussed. In an experi- mental study, the feasibility and reliability under diverse conditions are investigated. The algorithm is found to be very reliable, if certain boundary conditions are met.

Due to the large variety of modern applications and evolving network technologies, a small number of general-purpose protocol stacks will no longer be sufficient. Rather, customization of communication protocols will play a major role. In this paper, we present an approach that has the potential to substantially reduce the effort for designing customized protocols. Our approach is based on the concept of design patterns, which is well-established in object oriented software development. We specialize this concept to communication protocols, and - in addition - use formal description techniques (FDTs) to specify protocol design patterns as well as rules for their instantiation and composition. The FDTs of our choice are SDL-92 and MSCs, which offer suitable language support. We propose an SDL pattern description template and relate pattern-based configuring of communication protocols to existing SDL methodologies. Particular SDL patterns and the configuring of a customized resource reservation protocol are presented in detail.

Im Bereich der Expertensysteme ist das Problemlösen auf der Basis von bekannten Fallbeispielen ein derzeit sehr aktuelles Thema. Auch für Diagnoseaufgaben gewinnt der fallbasierte Ansatz immer mehr an Bedeutung. In diesem Papier soll der im Rahmen des Moltke -Projektes1 an der Universität Kaiserslautern entwickelte fallbasierte Problemlöser Patdex/22 vorgestellt werden. Ein erster Prototyp, Patdex/1, wurde bereits 1988 entwickelt.

We argue in this paper that sophisticated mi-croplanning techniques are required even formathematical proofs, in contrast to the beliefthat mathematical texts are only schematicand mechanical. We demonstrate why para-phrasing and aggregation significantly en-hance the flexibility and the coherence ofthe text produced. To this end, we adoptedthe Text Structure of Meteer as our basicrepresentation. The type checking mecha-nism of Text Structure allows us to achieveparaphrasing by building comparable combi-nations of linguistic resources. Specified interms of concepts in an uniform ontologicalstructure called the Upper Model, our se-mantic aggregation rules are more compactthan similar rules reported in the literature.

Distributed systems are an alternative to shared-memorymultiprocessors for the execution of parallel applications.PANDA is a runtime system which provides architecturalsupport for efficient parallel and distributed program-ming. PANDA supplies means for fast user-level threads,and for a transparent and coordinated sharing of objectsacross a homogeneous network. The paper motivates themajor architectural choices that guided our design. Theproblem of sharing data in a distributed environment isdiscussed, and the performance of appropriate mecha-nisms provided by the PANDA prototype implementation isassessed.

PANDA is a run-time package based on a very small operating system kernel which supports distributed applications written in C++. It provides powerful abstractions such as very efficient user-level threads, a uniform global address space, object and thread mobility, garbage collection, and persistent objects. The paper discusses the design ration- ales underlying the PANDA system. The fundamental features of PANDA are surveyed, and their implementation in the current prototype environment is outlined.

Recent studies on planning, comparing plan re-use and plan generation, have shown that both the above tasks may have the same degree of computational complexity, even if we deal with very similar problems. The aim of this paper is to show that the same kind of results apply also for diagnosis. We propose a theoretical complexity analysis coupled with some experimental tests, intended to evaluate the adequacy of adaptation strategies which re-use the solutions of past diagnostic problems in order to build a solution to the problem to be solved. Results of such analysis show that, even if diagnosis re-use falls into the same complexity class of diagnosis generation (they are both NP-complete problems), practical advantages can be obtained by exploiting a hybrid architecture combining case-based and modelbased diagnostic problem solving in a unifying framework.

In most cases higher-order logic is based on the (gamma)-calculus in order to avoid the infinite set of so-called comprehension axioms. However, there is a price to be paid, namelyan undecidable unification algorithm. If we do not use the(gamma) - calculus, but translate higher-order expressions intofirst-order expressions by standard translation techniques, we haveto translate the infinite set of comprehension axioms, too. Ofcourse, in general this is not practicable. Therefore such anapproach requires some restrictions such as the choice of thenecessary axioms by a human user or the restriction to certainproblem classes. This paper will show how the infinite class ofcomprehension axioms can be represented by a finite subclass,so that an automatic translation of finite higher-order prob-lems into finite first-order problems is possible. This trans-lation is sound and complete with respect to a Henkin-stylegeneral model semantics.

We show how to buildup mathematical knowledge bases usingframes. We distinguish three differenttypes of knowledge: axioms, definitions(for introducing concepts like "set" or"group") and theorems (for relating theconcepts). The consistency of such know-ledge bases cannot be proved in gen-eral, but we can restrict the possibilit-ies where inconsistencies may be impor-ted to very few cases, namely to the oc-currence of axioms. Definitions and the-orems should not lead to any inconsisten-cies because definitions form conservativeextensions and theorems are proved to beconsequences.

To prove difficult theorems in a mathematical field requires substantial know-ledge of that field. In this thesis a frame-based knowledge representation formal-ism including higher-order sorted logic is presented, which supports a conceptualrepresentation and to a large extent guarantees the consistency of the built-upknowledge bases. In order to operationalize this knowledge, for instance, in anautomated theorem proving system, a class of sound morphisms from higher-orderinto first-order logic is given, in addition a sound and complete translation ispresented. The translations are bijective and hence compatible with a later proofpresentation.In order to prove certain theorems the comprehension axioms are necessary,(but difficult to handle in an automated system); such theorems are called trulyhigher-order. Many apparently higher-order theorems (i.e. theorems that arestated in higher-order syntax) however are essentially first-order in the sense thatthey can be proved without the comprehension axioms: for proving these theoremsthe translation technique as presented in this thesis is well-suited.

Freivalds, Karpinski and Smith [8] explored a special type of learning in the limit: identification of an unknown concept (function) by eliminating (erasing) all but one possible hypothesis (this type of learning is called co-learning). The motivation behind learning by erasing lies in the process of human and automated computer learning: often we can discard incorrect solutions much easier than to come up with the correct one. In Gödel numberings any learnable family can be learned by an erasing strategy. In this paper we concentrate on co-learning minimal programs. We show that co-learning of minimal programs, as originally defined is significantly weaker than learning minimal programs in Gödel numberings. In order to enhance the learning power

Following Buchberger's approach to computing a Gröbner basis of a poly-nomial ideal in polynomial rings, a completion procedure for finitely generatedright ideals in Z[H] is given, where H is an ordered monoid presented by a finite,convergent semi - Thue system (Sigma; T ). Taking a finite set F ' Z[H] we get a(possibly infinite) basis of the right ideal generated by F , such that using thisbasis we have unique normal forms for all p 2 Z[H] (especially the normal formis 0 in case p is an element of the right ideal generated by F ). As the orderingand multiplication on H need not be compatible, reduction has to be definedcarefully in order to make it Noetherian. Further we no longer have p Delta x ! p 0for p 2 Z[H]; x 2 H. Similar to Buchberger's s - polynomials, confluence criteriaare developed and a completion procedure is given. In case T = ; or (Sigma; T ) is aconvergent, 2 - monadic presentation of a group providing inverses of length 1 forthe generators or (Sigma; T ) is a convergent presentation of a commutative monoid ,termination can be shown. So in this cases finitely generated right ideals admitfinite Gröbner bases. The connection to the subgroup problem is discussed.

This report presents the main ideas underlyingtheOmegaGamma mkrp-system, an environmentfor the development of mathematical proofs. The motivation for the development ofthis system comes from our extensive experience with traditional first-order theoremprovers and aims to overcome some of their shortcomings. After comparing the benefitsand drawbacks of existing systems, we propose a system architecture that combinesthe positive features of different types of theorem-proving systems, most notably theadvantages of human-oriented systems based on methods (our version of tactics) andthe deductive strength of traditional automated theorem provers.In OmegaGamma mkrp a user first states a problem to be solved in a typed and sorted higher-order language (called POST ) and then applies natural deduction inference rules inorder to prove it. He can also insert a mathematical fact from an integrated data-base into the current partial proof, he can apply a domain-specific problem-solvingmethod, or he can call an integrated automated theorem prover to solve a subprob-lem. The user can also pass the control to a planning component that supports andpartially automates his long-range planning of a proof. Toward the important goal ofuser-friendliness, machine-generated proofs are transformed in several steps into muchshorter, better-structured proofs that are finally translated into natural language.This work was supported by the Deutsche Forschungsgemeinschaft, SFB 314 (D2, D3)

Ziel dieser Arbeit ist es, eine Methode zur Verfügung zu stellen, mit der ein Simulator für gebäudespezifische Aufgaben modelliert werden kann. Die Modellierung muß dabei so angelegt sein, daß sowohl einfache als auch sehr komplexe Simulatoren für spezielle Gebäude entworfen werden können. Aus dem erstellten Modell ist es anschließend möglich, mit Hilfe von Generatoren automatisch ein Programm zu erzeugen. Dadurch kann ein Entwerfer ohne spezielle Kenntnisse auf dem Gebiet der Simulation einen Gebäude-Simulator entwickeln. Zur Modellierung wurde ein domänenspezifischer Katalog von Entwurfsmustern erstellt. Dabei können die einzelnen Muster direkt zur Modellierung und Codegenerierung eingesetzt werden.

An important property and also a crucial point ofa term rewriting system is its termination. Transformation or-derings, developed by Bellegarde & Lescanne strongly based on awork of Bachmair & Dershowitz, represent a general technique forextending orderings. The main characteristics of this method aretwo rewriting relations, one for transforming terms and the otherfor ensuring the well-foundedness of the ordering. The centralproblem of this approach concerns the choice of the two relationssuch that the termination of a given term rewriting system can beproved. In this communication, we present a heuristic-based al-gorithm that partially solves this problem. Furthermore, we showhow to simulate well-known orderings on strings by transformationorderings.

We consider the problem of verifying confluence and termination of conditionalterm rewriting systems (TRSs). For unconditional TRSs the critical pair lemmaholds which enables a finite test for confluence of (finite) terminating systems.And for ensuring termination of unconditional TRSs a couple of methods forconstructing appropiate well-founded term orderings are known. If however ter-mination is not guaranteed then proving confluence is much more difficult. Re-cently we have obtained some interesting results for unconditional TRSs whichprovide sufficient criteria for termination plus confluence in terms of restrictedtermination and confluence properties. In particular, we have shown that anyinnermost terminating and locally confluent overlay system is complete, i.e. ter-minating and confluent. Here we generalize our approach to the conditional caseand show how to solve the additional complications due to the presence of con-ditions in the rules. Our main result can be stated as follows: Any conditionalTRS which is an innermost terminating semantical overlay system such that all(conditional) critical pairs are joinable is complete.

The multiple-view modeling of a product in a design context is discussed in this paper. We study the existing approaches for multiple-view modeling of a product and we give a brief analysis of them. Then we propose our approach which incorporates the multiple-model approach in STEP standard current works based on a single model. We propose a meta-model inspired by this approach for a multiple-view design environment. Next, we validate this meta-model with a case study. Finally we conclude and give some perspectives of this work. Keywords: product data modeling, multiple-view modeling, product data integration, STEP, functional model.

In this paper we will present a design model (in the sense of KADS) for the domain of technical diagnosis. Based on this we will describe the fully implemented expert system shell MOLTKE 3.0, which integrates common knowledge acquisition methods with techniques developed in the fields of Model-Based Diagnosis and Machine Learning, especially Case-Based Reasoning.

A new problem for the automated off-line programming of industrial robot application is investigated. The Multi-Goal Path Planning is to find the collision-free path connecting a set of goal poses and minimizing e.g. the total path length. Our solution is based on an earlier reported path planner for industrial robot arms with 6 degrees-of-freedom in an on-line given 3D environment. To control the path planner, four different goal selection methods are introduced and compared. While the Random and the Nearest Pair Selection methods can be used with any path planner, the Nearest Goal and the Adaptive Pair Selection method are favorable for our planner. With the latter two goal selection methods, the Multi-Goal Path Planning task can be significantly accelerated, because they are able to automatically solve the simplest path planning problems first. Summarizing, compared to Random or Nearest Pair Selection, this new Multi-Goal Path Planning approach results in a further cost reduction of the programming phase.

In this paper, we present an approach to support distributed planning and scheduling, as well as the subsequent (also distributed) plan execution, in one system. The system will support the distributed planners and schedulers by providing task agendas for them, stating who needs to plan which tasks, and sending change notifications and warnings, if a planning or scheduling decision needs to be updated. The plan built using these mechanisms is then enacted by a workflow engine in the same system. This approach enables us to support interleaved planning and plan enactment, allowing the user to change the plan and schedule while the project is already under way. Deviations of the actual project enactment from the plan and schedule can automatically be detected, and necessary notifications will be sent to the concerned planner(s). This again facilitates the task of keeping the plan up to date, avoiding the complete invalidation of the plan as is often the case in conventional projects soon after enactment has started.

Die Induktive Logische Programmierung (ILP) ist ein Forschungsgebiet, das Techniken aus dem Maschinellen Lernen und der Logischen Programmierung vereint. Sie untersucht das klassische Problem induktiven Lernens aus klassifizierten Beispielen im Rahmen der Hornlogik erster Stufe. Inzwischen gibt es eine grosse Zahl verschiedener Ansätze für dieses Lernproblem, die sich hauptsächlich in der Suchrichtung im Hypothesenraum, den Generalisierungs- und Spezialisierungsoperatoren und den verwendeten nichtlogischen Beschränkungen (Bias) unterscheiden. Der Vergleich und die Integration dieser verschiedenen Ansätze war die Hauptmotivation für die Entwicklung des Systems MILES. MILES ist eine Programmierumgebung für die ILP, die neben Mechanismen zur Repräsentation und Verwaltung von Beispielen, Hintergrundwissen und Hypothesen einen Werkzeugkasten mit einem Grossteil der bekannten Generalisierungs-, Spezialisierungs- und Reformulierungsoperatoren enthält. Eine generische Kontrolle erlaubt, verschiedene dieser Operatoren in einen spezifischen ILP-Algorithmus zu integrieren. In diesem Beitrag wird ein kurzer Überblick über die Repräsentation, die Operatoren und die Kontrolle von MILES gegeben.

This paper concerns a knowledge structure called method , within a compu-tational model for human oriented deduction. With human oriented theoremproving cast as an interleaving process of planning and verification, the body ofall methods reflects the reasoning repertoire of a reasoning system. While weadopt the general structure of methods introduced by Alan Bundy, we make anessential advancement in that we strictly separate the declarative knowledgefrom the procedural knowledge. This is achieved by postulating some stand-ard types of knowledge we have identified, such as inference rules, assertions,and proof schemata, together with corresponding knowledge interpreters. Ourapproach in effect changes the way deductive knowledge is encoded: A newcompound declarative knowledge structure, the proof schema, takes the placeof complicated procedures for modeling specific proof strategies. This change ofparadigm not only leads to representations easier to understand, it also enablesus modeling the even more important activity of formulating meta-methods,that is, operators that adapt existing methods to suit novel situations. In thispaper, we first introduce briefly the general framework for describing methods.Then we turn to several types of knowledge with their interpreters. Finally,we briefly illustrate some meta-methods.

Even though it is not very often admitted, partial functions do play asignificant role in many practical applications of deduction systems. Kleenehas already given a semantic account of partial functions using three-valuedlogic decades ago, but there has not been a satisfactory mechanization. Recentyears have seen a thorough investigation of the framework of many-valuedtruth-functional logics. However, strong Kleene logic, where quantificationis restricted and therefore not truth-functional, does not fit the frameworkdirectly. We solve this problem by applying recent methods from sorted logics.This paper presents a resolution calculus that combines the proper treatmentof partial functions with the efficiency of sorted calculi.