Knowledge Lite Explanation Oriented Retrieval

Thirteenth UK Workshop on Case …, 2008

This paper describes the various explanation capabilities of the open source case-based reasoning tool myCBR. myCBR features conceptual explanations, which provide information about concepts of the application domain, backward explanations, which explain results of the retrieval process, and forward explanations, which support in the modelling of similarity measures. myCBR has been developed as a rapid prototyping tool with a general purpose interface as well as a similarity-based retrieval engine for easy integration in other applications where the explanations can be further adapted to the application's requirements.

1994

Problem solving in weak theory domains should compensate for the lack of strong theories by combining the various other knowledge types involved. Such methods should be able to effectively combine general domain knowledge with specific case knowledge. A method is described that utilises a presumably extensive and dense model of general domain knowledge as explanatory support for case-based problem solving and learning.

Artificial Intelligence Review, 2005

Artificial Intelligence Review, 2005

Most users of machine-learning products are reluctant to use the systems without any sense of the underlying logic that has led to the system's predictions. Unfortunately many of these systems lack any transparency in the way they operate and are deemed to be 'black boxes'. In this paper we present a Case-Based Reasoning (CBR) solution to providing supporting explanations of black-box systems. This CBR solution uses locally derived feature ranking information that reflects the importance of each feature to a prediction and a locally adjusted case retrieval mechanism. The retrieval mechanism takes advantage of the derived feature weightings to help select cases that are a better reflection of the black-box solution and thus more convincing explanations.

Case-Based Reasoning Research …, 2003

One of the perceived benefits of Case-Based Reasoning (CBR) is the potential to use retrieved cases to explain predictions. Surprisingly, this aspect of CBR has not been much researched. There has been some early work on knowledge-intensive approaches to CBR where the cases contain explanation patterns (e.g. SWALE). However, a more knowledge-light approach where the case similarity is the basis for explanation has received little attention. To explore this, we have developed a CBR system for predicting blood-alcohol level. We compare explanations of predictions produced with this system with alternative rule-based explanations. The casebased explanations fare very well in this evaluation and score significantly better than the rule-based alternative.

WM, 2005

Proceedings of the ECCBR, 2004

1995

Guiding the generation of abductive explanations is a di cult problem. Applying casebased reasoning to abductive explanation generation|generating new explanations by retrieving and adapting explanations for prior episodes|o ers the bene t of re-using successful explanatory reasoning but raises new issues concerning how to perform similarity assessment to judge the relevance of prior explanations to new situations. Similarity assessment a ects two points in the case-based explanation process: deciding which explanations to retrieve and evaluating the retrieved candidates. We address the problem of identifying similar explanations to retrieve by basing that similarity assessment on a categorization of anomaly types. We show that the problem of evaluating retrieved candidate explanations is often impeded by incomplete information about the situation to be explained, and address that problem with a novel similarity assessment method which we call constructive similarity assessment. Constructive similarity assessment contrasts with traditional \feature-mapping" similarity assessment methods by using the contents of memory to hypothesize important features in the new situation, and in using a pragmatic criterion|the system's ability to adapt features of the old case into features that apply in the new circumstances|as the basis for comparing features. Thus constructive similarity assessment does not merely compare new cases to old; instead, based on adaptation of prior cases in memory, it addresses the problem of incomplete input cases by building up and reasoning about augmented descriptions of those cases.

Knowledge Acquisition, 1990

The paper* describes a framework, RATIONALE, for building knowledge-based diagnostic systems that explain by reasoning explicitly. Unlike most existing explanation facilities that are grafted onto an independently designed inference engine, RATIONALE behaves as though it has to deliberate over and explain to itself, each refinement step. By treating explanation as primary, RATIONALE forces the system designer to represent knowledge explicitly that might otherwise be left implicit. This includes knowledge as to why a particular hypothesis is preferred, an exception is ignored, and a global inference strategy is chosen. RATIONALE integrates explanations with reasoning by allowing a causal and/or functional description of the domain to be represented explicitly. Reasoning proceeds by constructing a hypothesis-based classification tree whose root hypothesis contains the most general diagnosis of the system. Guided by a focusing algorithm, the classification tree branches into more specific hypotheses that explain the more detailed symptoms provided by the user. As the system is used, the classification tree also forms the basis for a dynamically generated explanation tree which holds both the successful and failed branches of the reasoning knowledge. RATIONALE is implemented in Quintus Prolog with a hypertext and graphics oriented interface tinder NEWS. § It provides an environment for tying together the processes of knowledge acquisition, system implementation and explanation of system reasoning.

Proceeding of the 2008 …, 2008