Mark T. Keane

Retrieval, reuse, revision and retention in case-based reasoning

Case-based reasoning (CBR) is an approach to problem solving that emphasizes the role of prior experience during future problem solving (i.e., new problems are solved by reusing and if necessary adapting the solutions to similar problems that were solved in the past). It has enjoyed considerable success in a wide variety of problem solving tasks and domains. Following a brief overview of the traditional problem-solving cycle in CBR, we examine the cognitive science foundations of CBR and its relationship to analogical reasoning. We then review a representative selection of CBR research in the past few decades on aspects of retrieval, reuse, revision and retention.

Constraints on Analogical Mapping: A Comparison of Three Models

Three theories of analogy have been proposed that are supported by computational models and data from experiments on human analogical abilities. In this article we show how these theories can be unified within a common metatheoretical framework that distinguishes among levels of informational, behavioral, and hardware constraints. This framework clarifies the distinctions among three computational models in the literature: the Analogical Constraint Mapping Engine (ACME), the Structure-Mapping Engine (SME), and the Incremental Analogy Machine (IAM). We then go on to develop a methodology for the comparative testing of these models. In two different manipulations of an analogical mapping task we compare the results of computational experiments with these models against the results of psychological experiments. In the first experiment we show that increasing the number of similar elements in two analogical domains decreases the response time taken to reach the correct mapping for an analogy problem. In the second psychological experiment we find that the order in which the elements of the two domains are presented has significant facilitative effects on the ease of analogical mapping. Of the three models, only IAM embodies behavioral constraints and predicts both of these results. Finally, the immediate implications of these results for analogy research are discussed, along with the wider implications the research has for cognitive science methodology.

An assessment of tag presentation techniques

With the growth of social bookmarking a new approach for metadata creation called tagging has emerged. In this paper we evaluate the use of tag presentation techniques. The main goal of our evaluation is to investigate the effect of some of the different properties that can be utilized in presenting tags e.g. alphabetization, using larger fonts etc. We show that a number of these factors can affect the ease with which users can find tags and use the tools for presenting tags to users.

Efficient Creativity: Constraint-Guided Conceptual Combination

Abstract This paper describes a theory that explains both the creativity and the efficiency of people’s conceptual combination. In the constraint theory, conceptual combination is controlled by three constraints of diagnosticity, plausibility, and informativeness. The constraints derive from the pragmatics of communication as applied to compound phrases. The creativity of combination arises because the constraints can be satisfied in many different ways. The constraint theory yields an algorithmic model of the efficiency of combination. The C 3 model admits the full creativity of combination and yet efficiently settles on the best interpretation for a given phrase. The constraint theory explains many empirical regularities in conceptual combination, and makes various empirically verified predictions. In computer simulations of compound phrase interpretation, the C 3 model has produced results in general agreement with people’s responses to the same phrases.

Adaptation-guided retrieval: questioning the similarity assumption in reasoning

Abstract One of the major assumptions in Artificial Intelligence is that similar experiences can guide future reasoning, problem solving and learning; what we will call, the similarity assumption. The similarity assumption is used in problem solving and reasoning systems when target problems are dealt with by resorting to a previous situation with common conceptual features. In this article, we question this assumption in the context of case-based reasoning (CBR). In CBR, the similarity assumption plays a central role when new problems are solved, by retrieving similar cases and adapting their solutions. The success of any CBR system is contingent on the retrieval of a case that can be successfully reused to solve the target problem. We show that it is often unwarranted to assume that the most similar case is also the most appropriate from a reuse perspective. We argue that similarity must be augmented by deeper, adaptation knowledge about whether a case can be easily modified to fit a target problem. We implement this idea in a new technique, called adaptation-guided retrieval (AGR), which provides a direct link between retrieval similarity and adaptation needs. This technique uses specially formulated adaptation knowledge, which, during retrieval, facilitates the computation of a precise measure of a cases adaptation requirements. In closing, we assess the broader implications of AGR and argue that it is just one of a growing number of methods that seek to overcome the limitations of the traditional similarity assumption in an effort to deliver more sophisticated and scalable reasoning systems.

On retrieving analogues when solving problems

After criticism of the precision of previous experimental procedures for testing analogue retrieval, a new procedure that overcomes the proposed inadequacies is described. This procedure is then employed in two experiments that test aspects of the general hypothesis that base analogues that are semantically remote from a target problem (Dunckers radiation problem) are more difficult to retrieve than those that are semantically closer. Experiment 1 confirmed this hypothesis by finding that remote analogues are seldom retrieved relative to literal analogues. The results of Experiment 2 falsified the hypothesis that analogue retrieval is solely due to the recognition of an “identical element”. Finally, an ad hoc model of analogue retrieval is proposed based on Schanks dynamic memory theory, and its consistency with the evidence and more general implications are considered.

Learning Adaptation Rules from a Case-Base

A major challenge for case-based reasoning (CBR) is to overcome the knowledge-engineering problems incurred by developing adaptation knowledge. This paper describes an approach to automating the acquisition of adaptation knowledge overcoming many of the associated knowledge-engineering costs. This approach makes use of inductive techniques, which learn adaptation knowledge from case comparison. We also show how this adaptation knowledge can be usefully applied. The method has been tested in a property-evaluation CBR system and the technique is illustrated by examples taken from this domain. In addition, we examine how any available domain knowledge might be exploited in such an adaptation-rule learning-system.

Are people biased in their use of search engines

Assessing user search behavior when deciding which links to follow in rank-ordered results lists.

The Adaption Knowledge Bottleneck: How to Ease it by Learning from Cases

Assuming that adaptation knowledge will continue to be an important part of CBR systems, a major challenge for the area is to overcome the knowledge-engineering problems that arise in its acquisition. This paper describes an approach to automating the acquisition of adaptation knowledge overcoming many of the associated knowledge-engineering costs. This approach makes use of inductive techniques, which learn adaptation knowledge from case comparison. We also show how this adaptation knowledge can be usefully applied and report on how available domain knowledge might be exploited in such an adaptation-rule learning-system.

Hierarchical case-based reasoning integrating case-based and decompositional problem-solving techniques for plant-control software design

Case based reasoning (CBR) is an artificial intelligence technique that emphasises the role of past experience during future problem solving. New problems are solved by retrieving and adapting the solutions to similar problems, solutions that have been stored and indexed for future reuse as cases in a case-base. The power of CBR is severely curtailed if problem solving is limited to the retrieval and adaptation of a single case, so most CBR systems dealing with complex problem solving tasks have to use multiple cases. The paper describes and evaluates the technique of hierarchical case based reasoning, which allows complex problems to be solved by reusing multiple cases at various levels of abstraction. The technique is described in the context of Deja Vu, a CBR system aimed at automating plant-control software design.