Non-learning problem solvers have been applied to many interesting and complex domains. Experience-based learning techniques have been developed to augment the capabilities of certain non-learning problem solvers in order to improve overall performance. An alternative approach to enhancing preexisting systems is automatic case elicitation, a learning technique in which a case-based reasoning sy...

A Case-Based Reasoning system, nicknamed SARTRE, that uses a memory-based approach to play two-player, limit Texas Hold’em is introduced. SARTRE records hand histories from strong players and attempts to re-use this information to handle novel situations. SARTRE’S case features and their representations are described, followed by the results obtained when challenging a world-class computerised ...

We envisage retrieval in textual case-based reasoning (TCBR) as an instance of abductive reasoning. The two main subtasks underlying abductive reasoning are ‘hypotheses generation’ where plausible case hypotheses are generated, and ‘hypothesis testing’ where the best hypothesis is selected among these in sequel. The central idea behind the presented two-stage retrieval model for TCBR is that re...

A new approach for synthesis of separation sequences by case-based reasoning (CBR) is presented. CBR is a method of finding the most similar existing designs and applying the knowledge of their concept and design for solving new problems. The method has earlier been applied to selecting single separations and simple sequences but has now been extended to cover synthesis of more complicated syst...

Conversational Case-Based Reasoning (CBR) systems assist their users in formulating queries for case retrieval. Existing textual CBR (TCBR) systems support conversation using only hand-crafted features and indices. However, to be practical, TCBR systems that require conversation should automatically generate their features. This is a difficult problem because TCBR applications routinely involve...

This paper describes an approach to the integration of case and models called model-based adaptation (MBA). Under this approach, case-based reasoning is used to find an approximate solution, and model-based reasoning is then used to adapt this approximate solution into a more precise solution. This paper distinguishes two distinct forms of MBA--per]ect-model-based adaptation and approximate-mod...

This paper presents Cabata, a hybrid case-based reasoning system that has been developed at the The most characteristic feature of the system is the combination of model-based and case-based reasoning within a hybrid architecture.

In the CBR literature from the past 25 years there is a considerable amount of research work that makes use of cases that are subspaces of some representation space rather than points in it. For cases of that kind, different terms have been used such as generalized case, prototype, schema, script, or abstract case. Our analysis of selected publications yields that on the one hand the same term ...

The aim of this paper is to introduce a new method for finding and reusing process equipment design and inherently safer process configurations by case-based reasoning (CBR) and object database techniques. CBR is based on finding most alike existing solutions and applying the knowledge of their properties for solving new problems in the early phases of design. This supports design engineer's kn...

One of the major issues confronting case-based reasoning (CBR) is rapid retrieval of similar cases from a large case base. This paper describes three algorithms which address this problem. The first algorithm works with quantitative cases using a graphical paradigm where the hyperspace containing the cases is divided into smaller and smaller hypercubes. The retrieval time for this algorithm is ...