Abstract
Intelligent systems encompass a wide range of software technologies including heuristic and normative expert systems, case-based reasoning systems, and neural networks. This field has been augmented in recent years by Web-based applications, such as recommender systems and the semantic Web. The uses of explanation facilities have their roots in heuristic rule-based expert systems and have long been touted as an important adjunct in intelligent decision support systems. However, in recent years, their uses have been explored in many other intelligent system technologies - particularly those making an impact in e-commerce such as recommender systems. This paper shows how explanation facilities work with a range of symbolic intelligent techniques and, when carefully designed, provide a range of benefits. The paper also shows how, despite being more difficult to augment with non-symbolic technologies, hybrid methods predominantly using rule-extraction techniques have provided moderate success for explanation facilities in a range of ad-hoc applications.
Highlights
One of the assumed strengths of expert systems has been their explanatory capabilities
This paper has shown that a strong case can be made for the provision of explanation facilities in intelligent decision making systems providing that designers consider the factors discussed in section two of this paper – regarding the provision of justification explanations
case-based reasoning (CBR) explanations offer much scope because a retrieved case is a specific description of an actual case and specific explanations are preferred to general explanations
Summary
One of the assumed strengths of expert systems has been their explanatory capabilities. According to [1], “One of the most important lessons of medical computing research is that expert-level decision making performance does not guarantee user acceptance”. According to [2] the ability to explain advice is the single most important feature of a computer based decision support system They show that explanation can enhance the acceptability of expert systems. The attempts to incorporate explanation facilities were first attempted with the heuristic rule-based expert system MYCIN during the late 1970’s [3] During this time, the potential for explanations became apparent because of the way that the explanation chain links problem with solution. This work is important because in recent years, a range of Web-based decision making applications have emerged which could be enhanced with explanation support
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