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This paper contains a brief overview of CasedBased Reasoning (CBR) with an emphasis on European activities in the field. The main objective was to have a balance between brevity and expressiveness and providing helpful pointers to the field. It identifies major open problems of CBR associated withc retrieval/selection, memory organization, matching, adaptation/evaluation, forgetting and, finally, integration with other techniques. It is intended for readers with knowledge in the area and contains a list of more than one hundred references in the field.

Case-Based Reasoning (CBR) can give agents the capability of learning from their own experience and solve new problems, however, in a multi-agent system, the ability of agents to collaborate is also crucial. In this paper we present an argumentation framework (AMAL) designed to provide learning agents with collaborative problem solving (joint deliberation) and information sharing capabilities (learning from communication). We

In this paper we propose an argumentation-based framework for multiagent induction, where two agents learn separately from individual training sets, and then engage in an argumentation process in order to converge to a common hypothesis about the data. The result is a multiagent induction strategy in which the agents minimize the set of cases that they have to exchange (using argumentation) in order to converge to a shared hypothesis. The proposed strategy works for any induction algorithm which expresses the hypothesis as a collection of rules. We show that the strategy converges to a hypothesis indistinguishable in training set accuracy from that learned by a centralized strategy.

We present a new approach lo learn from relational data based on re-representation of the examples. This approach, called property-based re-representation is based on a new analysis of the structure of refinement graphs used in ILP and relational learning in general. This analysis allows the characterization of relational examples by a set of multi-relational patterns called properties. Using them, we perform a property-based re-representation of relational examples that facilitates the development of relational learning techniques.

The construction of data bases containing information concerning scientific libraries requires cooperation between experts of different scientific domains. These experts are the source of knowledge that help the librarian in the process of characterizing the documents by means of relevant features, relating them using a thesaurus and generating useful and informative classifications of the documents to facilitate its retrieval. In this work, we describe an implementation based on a knowledge acquisition process that uses a knowledge engineering methodology: Our system helps the expert in a domain, to elicitate the concepts that he uses and to characterize the documents of the domain. The system also helps the librarian in the design of the thesaurus and the classifications.

Our knowledge elicitation approach is described in the first part of the paper and it is based on the Personal Construct Theory (Kelly, 1955; Shaw & Gaines, 1980). This methodology elicits the personally relevant conceptual constructs of an expert over his domain of expertise ...

Assessing the similarity of structured representation of cases in a natural and powerful way is an open issue in case-based reasoning (CBR). In this paper we use the notion of similitude terms, a symbolic representation of structural similarity proposed in an earlier ...

Empirical experiments have shown that storing every casedoes not automatically improve the accuracy of a CBR system. Therefore, several retain policies have been proposed in order to select whichcases to retain. However, all the research done in case retention strategiesis done in centralized CBR systems. We focus on multiagent CBRsystems, where each agent has a local case base, and where each agentcan interact with other agents in the system to solve problems in a collaborativeway. We propose several case retention ...

While similarity and retrieval in case-based reasoning (CBR) have received a lot of attention in the literature, other aspects of CBR, such as case reuse are less understood. Specifically, we focus on one of such, less understood, problems:" knowledge transfer". The issue we intend to elucidate can be expressed as follows: what knowledge present in a source case is transferred to a target problem in case-based inference? This paper presents a preliminary formal model of knowledge transfer and relates it to the classical notion of ...

Abstract. A desired capability of automatic problem solvers is how they can explain the results. Such explanations should justify that the solution proposed by the problem solver arises from the known domain knowledge. In this paper we discuss how the explanations can be used in CBR methods in order to justify the results in classification tasks and also for solving new problems.

Committees of classifiers with learning capabilities have good performance in a variety of domains. We focus on committees of agents with learning capabilities where no agent is omniscient but has a local, limited, individual view of data. In this framework, a major issue is how to integrate the individual results in an overall result—usually a voting mechanism is used. We propose a setting where agents can express a symbolic justification of their individual results. Justifications can then be examined by other agents and ...

Invited Talks.- The Virtue of Reward: Performance, Reinforcement and Discovery in Case-Based Reasoning.- Learning to Optimize Plan Execution in Information Agents.- Cased-Based Reasoning by Human Experts.- Scientific Papers.- Learning to Win: Case-Based Plan Selection in a Real-Time Strategy Game.- An Ensemble of Case-Based Classifiers for High-Dimensional Biological Domains.- Language Games: Solving the Vocabulary Problem in Multi-Case-Base Reasoning.- Evaluation and Monitoring of the Air-Sea Interaction Using a CBR-Agents Approach.- A Comparative Analysis of Query Similarity Metrics for Community-Based Web Search.- A Case-Based Approach for Indoor Location.- P2P Case Retrieval with an Unspecified Ontology.- Autonomous Internal Control System for Small to Medium Firms.- The Application of a Case-Based Reasoning System to Attention-Deficit Hyperactivity Disorder.- Reasoning with Textual Cases.- Using Ensembles of Binary Case-Based Reasoners.- Transfer in Visual Case-Based Problem Solving.- Generating Estimates of Classification Confidence for a Case-Based Spam Filter.- Improving Gene Selection in Microarray Data Analysis Using Fuzzy Patterns Inside a CBR System.- CBR for State Value Function Approximation in Reinforcement Learning.- Using CBR to Select Solution Strategies in Constraint Programming.- Case-Based Art.- Supporting Conversation Variability in COBBER Using Causal Loops.- Opportunities for CBR in Learning by Doing.- Navigating Through Case Base Competence.- A Knowledge-Intensive Method for Conversational CBR.- Re-using Implicit Knowledge in Short-Term Information Profiles for Context-Sensitive Tasks.- Acquiring Similarity Cases for Classification Problems.- A Live-User Evaluation of Incremental Dynamic Critiquing.- Case Based Representation and Retrieval with Time Dependent Features.- The Best Way to Instil Confidence Is by Being Right.- Cooperative Reuse for Compositional Cases in Multi-agent Systems.- Evaluating the Effectiveness of Exploration and Accumulated Experience in Automatic Case Elicitation.- HYREC: A Hybrid Recommendation System for E-Commerce.- Extending jCOLIBRI for Textual CBR.- Critiquing with Confidence.- Mapping Goals and Kinds of Explanations to the Knowledge Containers of Case-Based Reasoning Systems.- An Approach for Temporal Case-Based Reasoning: Episode-Based Reasoning.- How to Combine CBR and RBR for Diagnosing Multiple Medical Disorder Cases.- Case-Based Student Modeling Using Concept Maps.- Learning Similarity Measures: A Formal View Based on a Generalized CBR Model.- Knowledge-Rich Similarity-Based Classification.- Autonomous Creation of New Situation Cases in Structured Continuous Domains.- Retrieval and Configuration of Life Insurance Policies.- Analogical and Case-Based Reasoning for Predicting Satellite Task Schedulability.- Case Adaptation by Segment Replanning for Case-Based Planning Systems.- Selecting the Best Units in a Fleet: Performance Prediction from Equipment Peers.- CCBR-Driven Business Process Evolution.- CBR for Modeling Complex Systems.- CBE-Conveyor: A Case-Based Reasoning System to Assist Engineers in Designing Conveyor Systems.

Argumentation can be used by a group of agents to discuss about the validity of hypotheses. In this paper we propose an argumentation-based frame-work for multiagent induction, where two agents learn separately from individual training sets, and then engage in an argumentation process in order to converge to a common hypothesis about the data. The result is a multiagent induction strategy in which the agents minimize the set of examples that they have to exchange (using argumentation) in order to converge to a shared hypothesis. The proposed strategy works for any induction algorithm which expresses the hypothesis as a set of rules. We show that the strategy converges to a hypothesis indistinguishable in training set accuracy from that learned by a centralized strategy.