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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.

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.

Although work processes, like software processes, include a number of process aspects such as defined phases and deadlines, they are not plannable in detail. However, the advantages of today's process management, such as effective document routing and timeliness, can only be achieved with detailed models of work processes. This paper suggests a concept that uses detailed process models in conjunction with the possibility of defining the way a process model determines the work of individuals. Based on the WAM approach1, which allows workers to choose methods for their tasks according to the situation, we describe features to carry out planned parts of a process with workers always being able to start exceptional mechanisms. These mechanisms are based on the modelling paradigm of linked abstraction workflows (LAWs) that describe workflows at different levels of abstraction and classify refinements of tasks by the way lower tasks can be used.

We present a way to describe Reason Maintenance Systems using the sameformalism for justification based as well as for assumption based approaches.This formalism uses labelled formulae and thus is a special case of Gabbay'slabelled deductive systems. Since our approach is logic based, we are able toget a semantics oriented description of the systems in question.Instead of restricting ourselves to e.g. propositional Horn formulae, as wasdone in the past, we admit arbitrary logics. This enables us to characterizesystems as a whole, including both the reason maintenance component and theproblem solver, nevertheless maintaining a separation between the basic logicand the part that describes the label propagation. The possibility to freely varythe basic logic enables us to not only describe various existing systems, but canhelp in the design of completely new ones.We also show, that it is possible to implement systems based directly on ourlabelled logic and plead for "incremental calculi" crafted to attack undecidablelogics.Furthermore it is shown that the same approach can be used to handledefault reasoning, if the propositional labels are upgraded to first order.

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.

Learning from examples is a field of research in machine learning where class descriptions, like decision trees or implications (production rules or horn clauses) are produced using positive and negative examples as information. To solve this task many different heuristic search strategies have been developed, so far. The search by specialization is the most widely used search strategy, whereas other approaches use a search by generalization only. JoJo is an algorithm that combines both search directions into one search procedure. According to the estimated quality of the currently regarded rule either a generalization or specialization step is carried out by deleting or adding one premise to the conjunction part of the rule. But, to create an even more flexible (and faster) algorithm, it should be possible to delete or add more than just one premise at a time. Relaxing this restriction of JoJo led to the new highly flexible algorithm Frog that additionally uses a third search direction.

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.

This paper outlines the microplanner of PROVERB , a system that generates multilingual text from machine-found mathematical proofs. The main representational vehicle is the text structure proposed by Meteer. Following Panaget, we also distinguish between the ideational and the textual semantic categories, and use the upper model to replace the former. Based on this, a further extension is made to support aggregation before realization decisions are made. While our the framework of our macroplanner is kept languageindependent, our microplanner draws on language specific linguistic sources such as realization classes and lexicon. Since English and German are the first two languages to be generated and because the sublanguage of our mathematical domain is relatively limited, the upper model and the textual semantic categories are designed to cope with both languages. Since the work reported is still in progress, we also discuss open problems we are facing.

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.

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 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.

Im Rahmen des Sonderforschungsbereichs SFB314, Projekt X9 "Lernen und Analogie in technischen Expertensystemen", wurde die Verwendbarkeit von Techniken des fallbasierten Schliessens in wissens- basierten Systemen untersucht. Als prototypische Anwendungsdomäne wurde die Arbeitsplanerstellung rotationssymmetrischer Werkstücke gewählt. Im vorliegenden Beitrag wird ein Modell der Arbeits- planerstellung unter Berücksichtigung der verschiedenen, bisher als unabhängig behandelten Planungsmethoden beschrieben. Auf der Basis einer modelbasierte Wissensakquistion aus in Unternehmen verfügbaren Arbeitsplänen wird ein Ausschnitt der Arbeitsplanerstellung, die Aufspannplanung, detailliert. Die Anwendbarkeit wurde durch eine prototypische Realisierung nachgewiesen.

Das Ziel dieses Projekts war es, anhand von empirischen Untersuchungen klassische statistische Verfahren und aktuelle Methoden des Maschinellen Lernens mit einem Ansatz zu vergleichen, der in der Arbeitsgruppe entworfen und theoretisch analysiert wurde. Implementiert wurden f"unf Verfahren, einige davon in verschiedenen Varianten: FeedForward Neuronale Netze, Entscheidungsbäume, Bayes Entscheidungen, die auf Chow-Expansionen beruhen, Harmonische Analyse und die Methode des N"achsten Nachbarn. Als Referenzmassstab wurden Vorhersagen herangezogen, die den Trend oder den Mittelwert der letzten letzten Beobachtungen vorhersagten. Als Daten standen 16 Zeitreihen von Aktien- und Devisenkursen zur Verf"ugung. Jede der Zeitreihen bestand aus 2000 Daten, von denen die ersten 1500 zum Training und die restlichen 500 für den Vergleich der Verfahren dienten. Dabei zeigte es sich, dass die naiven Referenzverfahren einen recht guten Pr"ufstein darstellten. Die Bayes-Entscheidungen und die Entscheidungsbäume erwiesen sich als besonders stark und übertrafen die Referenzmethoden fast immer. Neuronale Netze und die Methode des n"achsten Nachbarn waren etwa genausogut, während die Harmonische Analyse für kurzfristige Vorhersagen schlechter und für langfristige besser war. Bei Entscheidungsbäumen und Neuronalen Netzen fiel auf, dass kleine B"aume bzw. Netze bessere Ergebnisse lieferten als grosse.

Concept mapping is a simple and intuitive visual form of knowledge representation. Concept maps can be categorized as informal or formal, where the latter is characterized by implementing a semantics model constraining their components. Software engineering is a domain that has successfully adopted formal concept maps to visualize and specify complex systems. Automated tools have been implemented to support these models although their semantic constraints are hardcoded within the systems and hidden from users. This paper presents the Constraint Graphs and jKSImapper systems. Constraint Graphs is a flexible and powerful graphical system interface for specifying concept mapping notations. In addition, jKSImapper is a multi-user concept mapping editor for the Internet and the World Wide Web. Together, these systems aim to support user-definable formal concept mapping notations and distributed collaboration on the Internet and the World Wide Web.

In diesem Beitrag werden konnektionistische Lernverfahren für die wissensbasierte Diagnose technischer Systeme vorgestellt. Es werden zwei Problemstellungen untersucht: die Prognose von Signalverläufen technischer Zustandsgrössen sowie die diagnostische Klassifikation von Systemzuständen und die Ergebnisse der Untersuchungen dargestellt.

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]).

The CBR team of the LISA is involved in several applied research projects based on the CBR paradigm. These applications use adaptation to solve the specific problems they face. So, we have capitalized some experience about how can be expressed and formalized adaptation processes. The bibliography on the subject is quite important but demonstrates a lake of formalism. At most, there exists some classifications about different types of adaptation.

Top-down and bottom-up theorem proving approaches have each specific ad-vantages and disadvantages. Bottom-up provers profit from strong redundancycontrol and suffer from the lack of goal-orientation, whereas top-down provers aregoal-oriented but have weak calculi when their proof lengths are considered. Inorder to integrate both approaches our method is to achieve cooperation betweena top-down and a bottom-up prover: The top-down prover generates subgoalclauses, then they are processed by a bottom-up prover. We discuss theoreticaspects of this methodology and we introduce techniques for a relevancy-basedfiltering of generated subgoal clauses. Experiments with a model eliminationand a superposition-based prover reveal the high potential of our cooperation approach.The author was supported by the Deutsche Forschungsgemeinschaft (DFG).

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).

We examine different possibilities of coupling saturation-based theorem pro-vers by exchanging positive/negative information. We discuss which positive ornegative information is well-suited for cooperative theorem proving and show inan abstract way how this information can be used. Based on this study, we in-troduce a basic model for cooperative theorem proving. We present theoreticalresults regarding the exchange of positive/negative information as well as practi-cal methods and heuristics that allow for a gain of efficiency in comparison withsequential provers. Finally, we report on experimental studies conducted in theareas condensed detachment, unfailing completion, and superposition.The author was supported by the Deutsche Forschungsgemeinschaft (DFG).

We investigate the usage of so-called inference rights. We point out the prob-lems arising from the inflexibility of existing approaches to heuristically controlthe search of automated deduction systems, and we propose the application ofinference rights that are well-suited for controlling the search more flexibly. More-over, inference rights allow for a mechanism of "partial forgetting" of facts thatis not realizable in the most controlling aproaches. We study theoretical founda-tions of inference rights as well as the integration of inference rights into alreadyexisting inference systems. Furthermore, we present possibilities to control suchmodified inference systems in order to gain efficiency. Finally, we report onexperimental results obtained in the area of condensed detachment.The author was supported by the Deutsche Forschungsgemeinschaft (DFG).

We present a cooperation concept for automated theorem provers that isbased on a periodical interchange of selected results between several incarnationsof a prover. These incarnations differ from each other in the search heuristic theyemploy for guiding the search of the prover. Depending on the strengths' andweaknesses of these heuristics different knowledge and different communicationstructures are used for selecting the results to interchange.Our concept is easy to implement and can easily be integrated into alreadyexisting theorem provers. Moreover, the resulting cooperation allows the dis-tributed system to find proofs much faster than single heuristics working alone.We substantiate these claims by two case studies: experiments with the DiCoDesystem that is based on the condensed detachment rule and experiments with theSPASS system, a prover for first order logic with equality based on the super-position calculus. Both case studies show the improvements by our cooperationconcept.

We present a methodology for coupling several saturation-based theoremprovers (running on different computers). The methodology is well-suited for re-alizing cooperation between different incarnations of one basic prover. Moreover,also different heterogeneous provers - that differ from each other in the calculusand in the heuristic they employ - can be coupled. Cooperation between the dif-ferent provers is achieved by periodically interchanging clauses which are selectedby so-called referees. We present theoretic results regarding the completeness ofthe system of cooperating provers as well as describe concrete heuristics for de-signing referees. Furthermore, we report on two experimental studies performedwith homogeneous and heterogeneous provers in the areas superposition and un-failing completion. The results reveal that the occurring synergetic effects leadto a significant improvement of performance.

We present a general framework for developing search heuristics for au-tomated theorem provers. This framework allows for the construction ofheuristics that are on the one hand able to replay (parts of) a given prooffound in the past but are on the other hand flexible enough to deviate fromthe given proof path in order to solve similar proof problems. We substanti-ate the abstract framework by the presentation of three distinct techniquesfor learning appropriate search heuristics based on soADcalled features. Wedemonstrate the usefulness of these techniques in the area of equational de-duction. Comparisons with the renowned theorem prover Otter validatethe applicability and strength of our approach.

We present an approach to automating the selection of search-guiding heuris-tics that control the search conducted by a problem solver. The approach centerson representing problems with feature vectors that are vectors of numerical val-ues. Thus, similarity between problems can be determined by using a distancemeasure on feature vectors. Given a database of problems, each problem beingassociated with the heuristic that was used to solve it, heuristics to be employedto solve a novel problem are suggested in correspondence with the similaritybetween the novel problem and problems of the database.Our approach is strongly connected with instance-based learning and nearest-neighbor classification and therefore possesses incremental learning capabilities.In experimental studies it has proven to be a viable tool for achieving the finaland crucial missing piece of automation of problem solving - namely selecting anappropriate search-guiding heuristic - in a flexible way.This work was supported by the Deutsche Forschungsgemeinschaft (DFG).

We are going to present two methods that allow to exploit previous expe-rience in the area of automated deduction. The first method adapts (learns)the parameters of a heuristic employed for controlling the application of infer-ence rules in order to find a known proof with as little redundant search effortas possible. Adaptation is accomplished by a genetic algorithm. A heuristiclearned that way can then be profitably used to solve similar problems. Thesecond method attempts to re-enact a known proof in a flexible manner in orderto solve an unknown problem whose proof is believed to lie in (close) vicinity.The experimental results obtained with an equational theorem prover show thatthese methods not only allow for impressive speed-ups, but also make it possibleto handle problems that were out of reach before.

Problems stemming from the study of logic calculi in connection with an infer-ence rule called "condensed detachment" are widely acknowledged as prominenttest sets for automated deduction systems and their search guiding heuristics. Itis in the light of these problems that we demonstrate the power of heuristics thatmake use of past proof experience with numerous experiments.We present two such heuristics. The first heuristic attempts to re-enact aproof of a proof problem found in the past in a flexible way in order to find a proofof a similar problem. The second heuristic employs "features" in connection withpast proof experience to prune the search space. Both these heuristics not onlyallow for substantial speed-ups, but also make it possible to prove problems thatwere out of reach when using so-called basic heuristics. Moreover, a combinationof these two heuristics can further increase performance.We compare our results with the results the creators of Otter obtained withthis renowned theorem prover and this way substantiate our achievements.

We present a method for learning heuristics employed by an automated proverto control its inference machine. The hub of the method is the adaptation of theparameters of a heuristic. Adaptation is accomplished by a genetic algorithm.The necessary guidance during the learning process is provided by a proof prob-lem and a proof of it found in the past. The objective of learning consists infinding a parameter configuration that avoids redundant effort w.r.t. this prob-lem and the particular proof of it. A heuristic learned (adapted) this way canthen be applied profitably when searching for a proof of a similar problem. So,our method can be used to train a proof heuristic for a class of similar problems.A number of experiments (with an automated prover for purely equationallogic) show that adapted heuristics are not only able to speed up enormously thesearch for the proof learned during adaptation. They also reduce redundancies inthe search for proofs of similar theorems. This not only results in finding proofsfaster, but also enables the prover to prove theorems it could not handle before.

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".

We present first steps towards fully automated deduction that merely requiresthe user to submit proof problems and pick up results. Essentially, this necessi-tates the automation of the crucial step in the use of a deduction system, namelychoosing and configuring an appropriate search-guiding heuristic. Furthermore,we motivate why learning capabilities are pivotal for satisfactory performance.The infrastructure for automating both the selection of a heuristic and integra-tion of learning are provided in form of an environment embedding the "core"deduction system.We have conducted a case study in connection with a deduction system basedon condensed detachment. Our experiments with a fully automated deductionsystem 'AutoCoDe' have produced remarkable results. We substantiate Au-toCoDe's encouraging achievements with a comparison with the renowned the-orem prover Otter. AutoCoDe outperforms Otter even when assuming veryfavorable conditions for Otter.

Evolving Combinators
(1996)

One of the many abilities that distinguish a mathematician from an auto-mated deduction system is to be able to offer appropriate expressions based onintuition and experience that are substituted for existentially quantified variablesso as to simplify the problem at hand substantially. We propose to simulate thisability with a technique called genetic programming for use in automated deduc-tion. We apply this approach to problems of combinatory logic. Our experimen-tal results show that the approach is viable and actually produces very promisingresults. A comparison with the renowned theorem prover Otter underlines theachievements.This work was supported by the Deutsche Forschungsgemeinschaft (DFG).

We present a method for making use of past proof experience called flexiblere-enactment (FR). FR is actually a search-guiding heuristic that uses past proofexperience to create a search bias. Given a proof P of a problem solved previouslythat is assumed to be similar to the current problem A, FR searches for P andin the "neighborhood" of P in order to find a proof of A.This heuristic use of past experience has certain advantages that make FRquite profitable and give it a wide range of applicability. Experimental studiessubstantiate and illustrate this claim.This work was supported by the Deutsche Forschungsgemeinschaft (DFG).

We present a novel approach to classification, based on a tight coupling of instancebased learning and a genetic algorithm. In contrast to the usual instance-based learning setting, we do not rely on (parts of) the given training set as the basis of a nearestneighbor classifier, but we try to employ artificially generated instances as concept prototypes. The extremely hard problem of finding an appropriate set of concept prototypes is tackled by a genetic search procedure with the classification accuracy on the given training set as evaluation criterion for the genetic fitness measure. Experiments with artificial datasets show that - due to the ability to find concise and accurate concept descriptions that contain few, but typical instances - this classification approach is considerably robust against noise, untypical training instances and irrelevant attributes. These favorable (theoretical) properties are corroborated using a number of hard real-world classification problems.

We investigate in how far interpolation mechanisms based on the nearest-neighbor rule (NNR) can support cancer research. The main objective is to usethe NNR to predict the likelihood of tumorigenesis based on given risk factors.By using a genetic algorithm to optimize the parameters of the nearest-neighbourprediction, the performance of this interpolation method can be improved sub-stantially. Furthermore, it is possible to detect risk factors which are hardly ornot relevant to tumorigenesis. Our preliminary studies demonstrate that NNR-based interpolation is a simple tool that nevertheless has enough potential to beseriously considered for cancer research or related research.

A translation contract is a binary predicate corrTransl(S,T) for source programs S and target programs T. It precisely specifies when T is considered to be a correct translation of S. A certifying compiler generates --in addittion to the target T-- a proof for corrTransl(S,T). Certifying compilers are important for the development of safety critical systems to establish the behavioral equivalence of high-level programs with their compiled assembler code. In this paper, we report on a certifying compiler, its proof techniques, and the underlying formal framework developed within the proof assistent Isabelle/HOL. The compiler uses a tiny C-like language as input, has an optimization phase, and generates MIPS code. The underlying translation contract is based on a trace semantics. We investigate design alternatives and discuss our experiences.

Today's communication systems are typically structured into several layers, where each layer realizes a fixed set of protocol functionalities. These functionalities have been carefully chosen such that a wide range of applications can be supported and protocols work in a general environment of networks. However, due to evolving network technologies as well as increased and varying demands of modern applications general-purpose protocol stacks are not always adequate. To improve this situation new flexible communication architectures have been developed which enable the configuration of customized communication subsystems by composing a proper set of reusable building blocks. In particular, several approaches to automatic configuration of communication subsystems have been reported in the literature. This report gives an overview of theses approaches (F-CCS, Da CaPo, x-Kernel, and ADAPTIVE) and, in particular, defines a framework, which identifies common architectural issues and configuration tasks.

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.

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.

Within the present paper we investigate case-based representability as well as case-based learnability of indexed families of uniformly recursive languages. Since we are mainly interested in case-based learning with respect to an arbitrary fixed similarity measure, case-based learnability of an indexed family requires its representability, first. We show that every indexed family is case- based representable by positive and negative cases. If only positive cases are allowed the class of representable families is comparatively small. Furthermore, we present results that provide some bounds concerning the necessary size of case bases. We study, in detail, how the choice of a case selection strategy influences the learning capabilities of a case-based learner. We define different case selection strategies and compare their learning power to one another. Furthermore, we elaborate the relations to Gold-style language learning from positive and both positive and negative examples.

Contrary to symbolic learning approaches, that represent a learned concept explicitly, case-based approaches describe concepts implicitly by a pair (CB; sim), i.e. by a measure of similarity sim and a set CB of cases. This poses the question if there are any differences concerning the learning power of the two approaches. In this article we will study the relationship between the case base, the measure of similarity, and the target concept of the learning process. To do so, we transform a simple symbolic learning algorithm (the version space algorithm) into an equivalent case-based variant. The achieved results strengthen the hypothesis of the equivalence of the learning power of symbolic and casebased methods and show the interdependency between the measure used by a case-based algorithm and the target concept.

In den letzten Jahren wurden Methoden des fallbasierten Schliessens häufig in Bereichen verwendet, in denen traditionell symbolische Verfahren zum Einsatz kommen, beispielsweise in der Klassifikation. Damit stellt sich zwangsläufig die Frage nach den Unterschieden bzw. der Mächtigkeit dieser Lernverfahren. Jantke [Jantke, 1992] hat bereits Gemeinsamkeiten von Induktiver Inferenz und fallbasierter Klassifikation untersucht. In dieser Arbeit wollen wir einige Zusammenhänge zwischen der Fallbasis, dem Ähnlichkeitsmass und dem zu erlernenden Begriff verdeutlichen. Zu diesem Zweck wird ein einfacher symbolischer Lernalgorithmus (der Versionenraum nach [Mitchell, 1982]) in eine äquivalente, fallbasiert arbeitende Variante transformiert. Die vorgestellten Ergebnisse bestätigen die Äquivalenz von symbolischen und fallbasierten Ansätzen und zeigen die starke Abhängigkeit zwischen dem im System verwendeten Mass und dem zu lernenden Begriff.

While symbolic learning approaches encode the knowledge provided by the presentation of the cases explicitly into a symbolic representation of the concept, e.g. formulas, rules, or decision trees, case-based approaches describe learned concepts implicitly by a pair (CB; d), i.e. by a set CB of cases and a distance measure d. Given the same information, symbolic as well as the case-based approach compute a classification when a new case is presented. This poses the question if there are any differences concerning the learning power of the two approaches. In this work we will study the relationship between the case base, the measure of distance, and the target concept of the learning process. To do so, we transform a simple symbolic learning algorithm (the version space algorithm) into an equivalent case-based variant. The achieved results strengthen the conjecture of the equivalence of the learning power of symbolic and casebased methods and show the interdependency between the measure used by a case-based algorithm and the target concept.

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.

Correctness and runtime efficiency are essential properties of software ingeneral and of high-speed protocols in particular. Establishing correctnessrequires the use of FDTs during protocol design, and to prove the protocolcode correct with respect to its formal specification. Another approach toboost confidence in the correctness of the implementation is to generateprotocol code automatically from the specification. However, the runtimeefficiency of this code is often insufficient. This has turned out to be amajor obstacle to the use of FDTs in practice.One of the FDTs currently applied to communication protocols is Es-telle. We show how runtime efficiency can be significantly improved byseveral measures carried out during the design, implementation and run-time of a protocol. Recent results of improvements in the efficiency ofEstelle-based protocol implementations are extended and interpreted.

The purpose of this expose is to explain the generic design of a customized communication subsystem. The expose addresses both functional and non-functional aspects. Starting point is a real-time requirement from the application area building automation. We show how this application requirement and some background information about the application area lead to a system architecture, a communication service, a protocol architecture and to the selection, adaptation, and composition of protocol functionalities. The reader will probably be surprised how much effort is necessary in order to implement the innocuous, innocent, inconspicuous looking application requirement. Formal description techniques (FDTs) will be used in all design phases.

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.

We present a new criterion for confluence of (possibly) non-terminating left-linear term rewriting systems. The criterion is based on certain strong joinabil-ity properties of parallel critical pairs . We show how this criterion relates toother well-known results, consider some special cases and discuss some possibleextensions.

The well-known and powerful proof principle by well-founded induction says that for verifying \(\forall x : P (x)\) for some property \(P\) it suffices to show \(\forall x : [[\forall y < x :P (y)] \Rightarrow P (x)] \) , provided \(<\) is a well-founded partial ordering on the domainof interest. Here we investigate a more general formulation of this proof principlewhich allows for a kind of parameterized partial orderings \(<_x\) which naturallyarises in some cases. More precisely, we develop conditions under which theparameterized proof principle \(\forall x : [[\forall y <_x x : P (y)] \Rightarrow P (x)]\) is sound in thesense that \(\forall x : [[\forall y <_x x : P (y)] \Rightarrow P (x)] \Rightarrow \forall x : P (x)\) holds, and givecounterexamples demonstrating that these conditions are indeed essential.