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 ...

An interactive cased based reasoning tool for assisting engineers with the mathematical modelling tasks associated with the analysis of thermal problems is described. By representing fundamental thermal modelling scenarios as cases, complex physical systems are modelled in a piecewise fashion by successive application of matching cases. Retrieval is based on the use of qualitative indices, deri...

We present a methodology for building validation models the knowledge structure used during the retrieval phase of our case based reasoning systems This methodology allows a knowledge engineer to start with a textual database of previously solved cases and quickly produce an e ective case based reasoning system Using our methodology we have built two such systems one of these systems operates o...

To promote discussion at the Fourth Workshop on Textual Case-Based Reasoning (TCBR), we set a workshop challenge. We encouraged all potential workshop participants, individually or in their research groups, to submit a short paper that addressed this challenge. The purpose of this paper is to explain the challenge. We challenged participants to do the following: analyse the corpus of Air Invest...

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...

Model generation has emerged as a key task in engineering design and analysis. AI research in this area has focused on model based reasoning emphasising qualitative models in attempting to automate this process. In this paper, we propose that this work on the use of model based reasoning in model generation would benefit from the inclusion of case-based reasoning (CBR) techniques. We argue that...

In this paper, we present the knowledge-based system rosa working on spatial and functional organizations in agriculture. The reasoning in rosa combines hierarchical classification, case-based reasoning, and qualitative spatial reasoning. The goal of the system is twofold: formalizing and building a case base holding on farm spatial and functional organizations, and helping the analysis of new ...

This paper looks at EBMT from the perspective of the Case-based Reasoning (CBR) paradigm. We attempt to describe the task of machine translation (MT) seen as a potential application of CBR, and attempt to describe MT in standard CBR terms. The aim is to see if other applications of CBR can suggest better ways to approach EBMT.

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...