Die Entwicklung des Zusammenlebens der Menschen geht immer mehr den Weg zur Informations- und Mediengesellschaft. Nicht zuletzt aufgrund der weltweiten Vernetzung ist es uns in minutenschnelle möglich, fast alle erdenklichen Informationen zu Hause auf den Bildschirm geliefert zu bekommen. Es findet sich so jeder zwar in einer gewissen schützenden Anonymität, aber dennoch einer genauso gewollten, wie erschreckenden Transparenz wieder. Jeder klassifiziert in gewisser Weise Informationen, die er preisgibt etwa in öffentliche, persönliche und vertrauliche Nachrichten. Gerade hier müssen Techniken und Methoden bereitstehen, um in dieser anonymen Transparenz Informationen, die nur für spezielle Empfänger gedacht sind vor unbefugtem Zugriff zu schützen und nur denjenigen zugänglich zu machen, die dazu berechtigt sind. Diesen Wunsch hat nicht nur allgemein die Gesellschaft, sondern im speziellen wird die Entwicklung auf diesem Gebiet gerade von staatlichen und militärischen Einrichtungen gefordert und gefördert. So sind häufig eingesetzte Werkzeuge die Methoden der Kryptologie, aber solange es geheime Nachrichten gibt, wird es Angreifer geben, die versuchen, sich unberechtigten Zugang zu diesen Informationen zu verschaffen. Da die ständig wachsende Leistung von EDV-Anlagen das "Knacken" von Verschlüsselungsmethoden begünstigt, muß zu immer sichereren Chiffrierverfahren übergegangen werden. Dieser Umstand macht das Thema Kryptologie für den Moment hochaktuell und auf lange Sicht zu einem zeitlosen Forschungsgebiet der Mathematik und Informatik.
The paper shows that characterizing the causal relationship between significant events is an important but non-trivial aspect for understanding the behavior of distributed programs. An introduction to the notion of causality and its relation to logical time is given; some fundamental results concerning the characterization of causality are pre- sented. Recent work on the detection of causal relationships in distributed computations is surveyed. The relative merits and limitations of the different approaches are discussed, and their general feasibility is analyzed.
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.
Freivalds, Karpinski and Smith  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
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).
This report presents the properties of a specification of the domain of process planning for rotary symmetrical workpieces. The specification results from a model for problem solving in this domain that involves different reasoners, one of which is an AI planner that achieves goals corresponding to machining workpieces by considering certain operational restrictions of the domain. When planning with SNLP (McAllester and Rosenblitt, 1991), we will show that the resulting plans have the property of minimizing the use of certain key operations. Further, we will show that, for elastic protected plans (Kambhampati et al., 1996) such as the ones produced by SNLP, the goals corresponding to machining parts of a workpiece are OE-constrained trivial serializable, a special form of trivial serializability (Barrett and Weld, 1994). However, we will show that planning with SNLP in this domain can be very difficult: elastic protected plans for machining parts of a workpiece are nonmergeable. Finally, we will show that, for sufix, prefix or sufix and prefix plans such as the ones produced by state-space planners, it is not possible to have both properties, being OEconstrained trivial serializable and minimizing the use of the key operations, at the same time.
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.
Problem specifications for classical planners based on a STRIPS-like representation typically consist of an initial situation and a partially defined goal state. Hierarchical planning approaches, e.g., Hierarchical Task Network (HTN) Planning, have not only richer representations for actions but also for the representation of planning problems. The latter are defined by giving an initial state and an initial task network in which the goals can be ordered with respect to each other. However, studies with a specification of the domain of process planning for the plan-space planner CAPlan (an extension of SNLP) have shown that even without hierarchical domain representation typical properties called goal orderings can be identified in this domain that allow more efficient and correct case retrieval strategies for the case-based planner CAPlan/CbC. Motivated by that, this report describes an extension of the classical problem specifications for plan-space planners like SNLP and descendants. These extended problem specifications allow to define a partial order on the planning goals which can interpreted as an order in which the solution plan should achieve the goals. These goal ordering can theoretically and empirically be shown to improve planning performance not only for case-based but also for generative planning. As a second but different way we show how goal orderings can be used to address the control problem of partial order planners. These improvements can be best understood with a refinement of Barrett's and Weld's extended taxonomy of subgoal collections.
Abstraction is one of the most promising approaches to improve the performance of problem solvers. In several domains abstraction by dropping sentences of a domain description - as used in most hierarchical planners - has proven useful. In this paper we present examples which illustrate significant drawbacks of abstraction by dropping sentences. 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. However, to achieve a powerful change of the representation language, the abstract language itself as well as rules which describe admissible ways of abstracting states must be provided in the domain model. This new abstraction approach is the core of PARIS (Plan Abstraction and Refinement in an Integrated System), a system in which abstract planning cases are automatically learned from given concrete cases. An empirical study in the domain of process planning in mechanical engineering shows significant advantages of the proposed reasoning from abstract cases over classical hierarchical planning.^
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.