Kaiserslautern - Fachbereich Informatik
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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.
We present an inference system for clausal theorem proving w.r.t. various kinds of inductivevalidity in theories specified by constructor-based positive/negative-conditional equations. The reductionrelation defined by such equations has to be (ground) confluent, but need not be terminating. Our con-structor-based approach is well-suited for inductive theorem proving in the presence of partially definedfunctions. The proposed inference system provides explicit induction hypotheses and can be instantiatedwith various wellfounded induction orderings. While emphasizing a well structured clear design of theinference system, our fundamental design goal is user-orientation and practical usefulness rather thantheoretical elegance. The resulting inference system is comprehensive and relatively powerful, but requiresa sophisticated concept of proof guidance, which is not treated in this paper.This research was supported by the Deutsche Forschungsgemeinschaft, SFB 314 (D4-Projekt)
INRECA offers tools and methods for developing, validating, and maintaining classification, diagnosis and decision support systems. INRECA's basic technologies are inductive and case-based reasoning [9]. INRECA fully integrates [2] both techniques within one environment and uses the respective advantages of both technologies. Its object-oriented representation language CASUEL [10, 3] allows the definition of complex case structures, relations, similarity measures, as well as background knowledge to be used for adaptation. The objectoriented representation language makes INRECA a domain independent tool for its destined kind of tasks. When problems are solved via case-based reasoning, the primary kind of knowledge that is used during problem solving is the very specific knowledge contained in the cases. However, in many situations this specific knowledge by itself is not sufficient or appropriate to cope with all requirements of an application. Very often, background knowledge is available and/or necessary to better explore and interpret the available cases [1]. Such general knowledge may state dependencies between certain case features and can be used to infer additional, previously unknown features from the known ones.
Die Verwendung von existierenden Planungsansätzen zur Lösung von realen Anwendungs- problemen führt meist schnell zur Erkenntnis, dass eine vorliegende Problemstellung im Prinzip zwar lösbar ist, der exponentiell anwachsende Suchraum jedoch nur die Behandlung relativ kleiner Aufgabenstellungen erlaubt. Beobachtet man jedoch menschliche Planungsexperten, so sind diese in der Lage bei komplexen Problemen den Suchraum durch Abstraktion und die Verwendung bekannter Fallbeispiele als Heuristiken, entscheident zu verkleinern und so auch für schwierige Aufgabenstellungen zu einer akzeptablen Lösung zu gelangen. In dieser Arbeit wollen wir am Beispiel der Arbeitsplanung ein System vorstellen, das Abstraktion und fallbasierte Techniken zur Steuerung des Inferenzprozesses eines nichtlinearen, hierarchischen Planungssystems einsetzt und so die Komplexität der zu lösenden Gesamtaufgabe reduziert.
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.
We describe a hybrid case-based reasoning system supporting process planning for machining workpieces. It integrates specialized domain dependent reasoners, a feature-based CAD system and domain independent planning. The overall architecture is build on top of CAPlan, a partial-order nonlinear planner. To use episodic problem solving knowledge for both optimizing plan execution costs and minimizing search the case-based control component CAPlan/CbC has been realized that allows incremental acquisition and reuse of strategical problem solving experience by storing solved problems as cases and reusing them in similar situations. For effective retrieval of cases CAPlan/CbC combines domain-independent and domain-specific retrieval mechanisms that are based on the hierarchical domain model and problem representation.
While most approaches to similarity assessment are oblivious of knowledge and goals, there is ample evidence that these elements of problem solving play an important role in similarity judgements. This paper is concerned with an approach for integrating assessment of similarity into a framework of problem solving that embodies central notions of problem solving like goals, knowledge and learning.
Contrary to symbolic learning approaches, which 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 case-based methods and show the interdependency between the measure used by a case-based algorithm and the target concept.