Explanation Goals In Case-Based Reasoning

1998

Case-based reasoning is a promising approach to develop a tutoring system. However, there still remains some problems to be solved. First, the system should explain the inference procedure upon the user's requests. Second, the explanation structure in the case may be inconsistent during the adaptation process. Third, when the system finds a gap between the new problem situation and the retrieved case, more complete matching should be performed by using not only the case but also multiple cases. In this paper, the authors will introduce the explanation structure so as to explain the reasoning process. Then, the authors will introduce a justification process for justifying the explanation structure. Finally, the authors will study the adaptability of multiple cases to create a solution which explains a complex reasoning process.

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.

Argument & Computation, 2021

This paper proposes a formal top-level model of explaining the outputs of machine-learning-based decision-making applications and evaluates it experimentally with three data sets. The model draws on AI & law research on argumentation with cases, which models how lawyers draw analogies to past cases and discuss their relevant similarities and differences in terms of relevant factors and dimensions in the problem domain. A case-based approach is natural since the input data of machine-learning applications can be seen as cases. While the approach is motivated by legal decision making, it also applies to other kinds of decision making, such as commercial decisions about loan applications or employee hiring, as long as the outcome is binary and the input conforms to this paper’s factor- or dimension format. The model is top-level in that it can be extended with more refined accounts of similarities and differences between cases. It is shown to overcome several limitations of similar arg...

2009

Abstract Traditional explanation strategies in machine learning have been dominated by rule and decision tree based approaches. Case-based explanations represent an alternative approach which has inherent advantages in terms of transparency and user acceptability. Case-based explanations are based on a strategy of presenting similar past examples in support of and as justification for recommendations made.

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.

Proceeding of the 2008 …, 2008

Artificial Intelligence, 1989

Explanation-based learning (EBL) is a very powerful method for category formation. Since EBL algorithms depend on having good explanations, it is crucial to have effective ways to build explanations, especially in complex real-world situations where complete causal information is not available. When people encounter new situations, they often explain them by remembering old explanations, and adapting them to fit. We believe that this case-based approach to explanation holds promise for use in AI systems, both for routine explanation and to creatively explain situations quite unlike what the system has encountered before. Building new explanations from old ones relies on having explanations available in memory. We describe explanation patterns (XPs), knowledge structures that package the reasoning underlying explanations. Using the SWALE system as a base, we discuss the retrieval and modification process, and the criteria used when deciding which explanation to accept. We also discuss issues in learning XPs: what generalization strategies are appropriate for real-world explanations, and which indexing strategies are appropriate for XPs. SWALE' s explanations allow it to understand nonstandard stories, and the XPs it learns increase its efficiency in dealing with similar anomalies in the future.

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.

… on Case-Based Reasoning (ICCBR'07), …, 2007

2000

Cases as used in case-based reasoning (CBR) typically re- cord experts' steps of reasoning and action, but not the ar- guments an expert may consider during the problem- solving. Knowledge that can improve the quality of per- formance of CBR is therefore lost. The paper describes an approach that tackles this problem by representing argu- ments in a simple form