Aspects of Intelligent Systems Explanation
By Dr. Keith Darlington. 2013
31 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Judith Masthoff
- Nava Tintarev
- Shane T. Mueller
- Gary Klein
- Robert R. Hoffman
- William J. Clancey
- Abigail Emrey
- Madalina Croitoru
Citations in the corpus
- Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AI (2019, background): “se, the work illustrates how the computational instantiation of the a model of the user can lead to improved performance of the human-machine system by improving the user’s understanding of problems. Darlington (2013) Good and succinct review of explanation systems, types of explanations and how var”
- SAsSy - Making Decisions Transparent with Argumentation and Natural Language Generation (2014, background): “According to the recent classification of explanations in IS by [4], the explanations in our system can be classified as justification, and to certain extent, trace-based explanations.”
- Explaining Recommendations: Design and Evaluation (2015, background): “While explanations in recommender systems may be most beneficial for experienced users [26], explanations research in other types of decision support systems have found that explanations are helpful when something unexpected or undesired happens [22].”
- Formalizing Explanatory Dialogues (2015, background): “The design of explanation facilities for intelligent systems is an active research area and a widely recognized problem [3,15,12,16] in Artificial Intelligence.”