Linking adaptation and similarity learning (original) (raw)

A similarity-based Theory of Case-based Reasoning-II

2003

This work is the second part of a general similarity-based theory of case-based reasoning (CBR), which moves CBR towards a firm theoretical foundation based on similarity-based reasoning. This paper will first examine abductive CBR and deductive CBR and propose a knowledge-based model for integrating abductive CBR and deductive CBR. It then proposes similarity-based models for rule-based and fuzzy rule-based case retrieval. Finally it investigates similarity-based case adaptation.

Towards a general framework for substitutional adaptation in case-based reasoning

2005

Adaptation is one of the most problematic steps in the design and development of Case Based Reasoning (CBR) systems, as it may require considerable domain knowledge and involve complex knowledge engineering tasks. This paper describes a general framework for substitutional adaptation, which only requires analogical domain knowledge, very similar to the one required to define a similarity function. The approach is formally defined, and its applicability is discussed with reference to case structure and its variability. A case study focused on the adaptation of cases related to truck tyre production processes is also presented.

Acquiring Case Adaptation Knowledge: A Hybrid Approach

1996

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 difficult 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 flexible 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 benefits of the approach and comparing the relative contributions of case learning and adaptation learning to reasoning performance.

A CASE-BASED REASONING SYSTEM ADAPTIVE TO THE COGNITIVE TASK

Case-based reasoning systems are systems that take advantage from experience, and thus strain after adapting to their environment. They show different ways of achieving this goal. They all share this peculiarity to be memory-driven systems. Thus, adaptability in a casebased reasoning system must be studied from the point of view of the memory. If case-based reasoners generally are devoted to the realization of a single task, the need for such systems to perform various tasks questions how to organize their memory to permit them to be taskadaptive. Moreover they must be able to adapt to several types of cognitive tasks, analysis tasks as well as synthesis tasks. For that purpose, a case-based reasoning system adaptive to the cognitive task is presented in this paper. Its adaptability comes from its memory composition, both cases and concepts, and from its hierarchical memory organization, based on multiple points of view, some of them associated to the various cognitive tasks it performs. For analytic tasks, the most specific cases are preferably used for the reasoning. For synthesis tasks, the most specific concepts, learnt by conceptual clustering, are used. So intensifying the learning inferences during case-based reasoning, and especially the synthetic learning ones, enlarges the application range of case-based reasoning to true synthesis tasks. An example of this system abilities, in the domain of eating disorders in psychiatry, is then briefly presented.

A Knowledge-Level Task Model of Adaption in Case-Based Reasoning

1999

The adaptation step is central in case-based reasoning (CBR), because it conditions the obtaining of a solution to a problem. This step is difficult from the knowledge acquisition and engineering points of view. We propose a knowledge level analysis of the adaptation step in CBR using the reasoning task concept. Our proposal is based on the study of several CBR systems for complex applications which imply the adaptation task. Three of them are presented to illustrate our analysis. We sketch from this study a generic model of the adaptation process using the task concept. This model is in conformity with other CBR formal models.

A connectionist approach for similarity assessment in case-based reasoning systems

Decision Support Systems, 1997

Case-Based Reasoning (CBR) systems support ill-structured decision making. In ill-structured decision environments, decision makers (DMs) differ in their problem solving approaches. As a result, CBR systems would be more useful if they were able to adapt to the idiosyncrasies of individual decision makers. Existing implementations of CBR systems have been mainly symbolic, and symbolic CBR systems are unable to adapt to the preferences of decision makers (i.e., they are static). Retrieval of appropriate previous cases is critical to the success of a CBR system. Widely used symbolic retrieval functions, such as nearest-neighbor matching, assume independence of attributes and require specification of their importance lbr matching. To ameliorate these deficiencies connectionist systems have been proposed. However, these systems are limited in their ability to adapt and grow. To overcome this limitation, we propose a distributed connectionist-symbolic architecture that adapts to the preferences of a decision maker and that, additionally, ameliorates the limitations of symbolic matching. The proposed architecture uses a supervised learning technique to acquire the matching knowledge. The architecture allows the growth of a case base without the involvement of a knowledge engineer. Empirical investigation of the proposed architecture in an ill-structured diagnostic decision environment demonstrated a superior retrieval performance when compared to the nearest-neighbor matching function. © 1997 Elsevier Science B.V. . by a CBR system is as follows : to assist a decision maker (DM), previous case(s) that closely resemble the new decision problem (new case) is(are) retrie~'ed. The solution of the previous case is then mapped as a solution for the new case. The mapped solution is adapted to account for the differences between a new case and a previous case. For future decision making, feedback of the success or failure of the solution is obtained from the DM.

Learning adaptation knowledge to improve case-based reasoning

Artificial Intelligence, 2006

Case-Based Reasoning systems retrieve and reuse solutions for previously solved problems that have been encountered and remembered as cases. In some domains, particularly where the problem solving is a classification task, the retrieved solution can be reused directly. But for design tasks it is common for the retrieved solution to be regarded as an initial solution that should be refined to reflect the differences between the new and retrieved problems. The acquisition of adaptation knowledge to achieve this refinement can be demanding, despite the fact that the knowledge source of stored cases captures a substantial part of the problem-solving expertise. This paper describes an introspective learning approach where the case knowledge itself provides a source from which training data for the adaptation task can be assembled. Different learning algorithms are explored and the effect of the learned adaptations is demonstrated for a demanding component-based pharmaceutical design task, tablet formulation. The evaluation highlights the incremental nature of adaptation as a further reasoning step after nearest-neighbour retrieval. A new property-based classification to adapt symbolic values is proposed, and an ensemble of these property-based adaptation classifiers has been particularly successful for the most difficult of the symbolic adaptation tasks in tablet formulation.

Knowledge-intensive case-based reasoning and sustained learning

1990

Abstract In case-based reasoning (CBR) a problem is solved by matching the problem description to a previously solved case, using the past solution in solving the new problem. A case-based reasoner learns after each problem solving session by retaining relevant information from a problem just solved, making the new experience available for future problem solving.