A persistent problem in machine planning is that of repairing plans that fail. Two solutions have been suggested to deal with this problem: planning critics and met a-planning techniques. Unfortunately, both of these suggestions suffer from lack of flexibility due to an extremely restricted view of how to describe planning failures. This paper presents an alternative approach in which plan failures are described in terms of causal explanations of why they occurred. These explanations are used to access different abstract replanning strategies, which are then turned into specific changes to the faulty plans. The approach is demonstrated using examples from CHEF, a case-based planner that creates and debugs plans in the domain of Szee hwan cooking. I. THE PROBLEM OF PLAN FAILURE.

This paper presents a view of planning as a task supported by a dynamic memory. This view attempts to integrate models of memory, learning and planning into a single system that learns about planning by creating new plans and analyzing how they interact with the world. We call this view of planning Case-Based Planning. A case-based planner makes use of its own past experience in developing new plans. It relies on its memory of observed effects, rather than a set of causal rules, to create and modify new plans. Memories of past successes are accessed and modified to create new plans. Memories of past failures are used to warn the planner of impending problems, and memories of past repairs are called upon to tell the planner how to how to deal with them. This view of planning from experience supports and is supported by a learning system that incorporates new experiences into the planner's episodic memory. This learning algorithm gains from the planner's failures as well as its successe...

This chapter provides an introduction to case-based reasoning, discusses motivations for CBR, and describes the central steps in the CBR process. It examines the relationship of CBR to other approaches and discusses major research areas, open issues, and promising opportunities for CBR. It surveys and relates numerous approaches within CBR and provides more than 150 references to international CBR research.

Planning Cases by Change of Representation Language Ralph Bergmann bergmann@informatik.uni-kl.de Wolfgang Wilke wilke@informatik.uni-kl.de Centre for Learning Systems and Applications (LSA) University of Kaiserslautern, P.O.-Box 3049, D-67653 Kaiserslautern, Germany Abstract 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 th...

. In current CBR systems, case adaptation is usually performed by rule-based methods that use task-specific rules hand-coded by the system developer. The ability to define those rules depends on knowledge of the task and domain that may not be available a priori, presenting a serious impediment to endowing CBR systems with the needed adaptation knowledge. This paper describes ongoing research on a method to address this problem by acquiring adaptation knowledge from experience. The method uses reasoning from scratch, based on introspective reasoning about the requirements for successful adaptation, to build up a library of adaptation cases that are stored for future reuse. We describe the tenets of the approach and the types of knowledge it requires. We sketch initial computer implementation, lessons learned, and open questions for further study. 1 Introduction Case-based reasoning (CBR) systems solve new problems by retrieving prior solutions of similar previous problems and performi...

Today’s software developments are faced with steadily increasing expectations: software has to be developed faster, better, and cheaper. At the same time, application complexity increases. Meeting these demands requires fast, continuous learning and the reuse of past experience on the part of the project teams. Thus, learning and reuse should be supported by well-defined processes applicable to all kinds of experience which are stored in an organizational memory. In this paper, we introduce a tool architecture supporting continuous learning and reuse of all kinds of experience from the software engineering domain and present the underlying methodology. 1.

Combinatorial explosion of inferences has always been a central problem in artificial intelligence. Although the inferences that can be drawn from a reasoner's knowledge and from available inputs is very large (potentially infinite), the inferential resources available to any reasoning system are limited. With limited inferential capacity and very many potential inferences, reasoners must somehow control the process of inference. Not all inferences are equally useful to a given reasoning system. Any reasoning system that has goals (or any form of a utility function) and acts based on its beliefs indirectly assigns utility to its beliefs. Given limits on the process of inference, and variation in the utility of inferences, it is clear that a reasoner ought to draw the inferences that will be most valuable to it. This paper presents an approach to this problem that makes the utility of a (potential) belief an explicit part of the inference process. The method is to generate exp...

ion in Case-Based Reasoning Ralph Bergmann and Wolfgang Wilke University of Kaiserslautern, Centre for Learning Systems and Applications (LSA) Dept. of Computer Science, P.O.-Box 3049, D-67653 Kaiserslautern, Germany E-Mail: fbergmann,wilkeg@informatik.uni-kl.de Abstract. This paper addresses the role of abstraction in case-based reasoning. We develop a general framework for reusing cases at several levels of abstraction, which is particularly suited for describing and analyzing existing and designing new approaches of this kind. We argue that in synthetic tasks (e.g. configuration, design, and planning), abstraction can be successfully used to improve the efficiency of similarity assessment, retrieval, and adaptation. Furthermore, a case-based planning system, called Paris, is described and analyzed in detail using this framework. An empirical study done with Paris demonstrates significant advantages concerning retrieval and adaptation efficiency as well as flexibility of adaptation....

The ability of case-based reasoning (CBR) systems to apply cases to novel situations depends on their case adaptation knowledge. However, endowing CBR systems with adequate adaptation knowledge has proven to be a very di cult task. This paper describes a hybrid method for performing case adaptation, using a combination of rule-based and case-based reasoning. It shows how this approach provides a framework for acquiring exible adaptation knowledge from experiences with autonomous adaptation and suggests its potential as a basis for acquisition of adaptation knowledge from interactive user guidance. It also presents initial experimental results examining the bene ts of the approach and comparing the relative contributions of case learning and adaptation learning to reasoning performance.

The distinction between rule-based and similarity-based processes in cognition is of fundamental importance for cognitive science, and has been the focus of a large body of empirical research. However, intuitive uses of the distinction are subject to theoretical difficulties and their relation to empirical evidence is not clear. We propose a ‘core ’ distinction between ruleand similarity-based processes, in terms of the way representations of stored information are ‘matched ’ with the representation of a novel item. This explication captures the intuitively clear-cut cases of processes of each type, and resolves apparent problems with the rule/ similarity distinction. Moreover, it provides a clear target for assessing the psychological and AI literatures. We show that many lines of psychological evidence are less conclusive than sometimes assumed, but suggest that converging lines of evidence may be persuasive. We then argue that the AI literature suggests that approaches which combine rules and similarity are an important new focus for empirical work. © 1998 Elsevier Science B.V. Keywords: Similarity-based process; Rule-based process 1.