ADT 2011: Algorithmic Decision Theory pp 121-134 | Cite as
Minimal and Complete Explanations for Critical Multi-attribute Decisions
Conference paper
Abstract
The ability to provide explanations along with recommended decisions to the user is a key feature of decision-aiding tools. We address the question of providing minimal and complete explanations, a problem relevant in critical situations where the stakes are very high. More specifically, we are after explanations with minimal cost supporting the fact that a choice is the weighted Condorcet winner in a multi-attribute problem. We introduce different languages for explanation, and investigate the problem of producing minimal explanations with such languages.
Keywords
Cost Function Recommender System Factor Statement Preference Statement Minimal Element
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Preview
Unable to display preview. Download preview PDF.
References
- 1.Carenini, G., Moore, J.: Generating and evaluating evaluative arguments. Artificial Intelligence 170, 925–952 (2006)CrossRefGoogle Scholar
- 2.Klein, D.: Decision analytic intelligent systems: automated explanation and knowledge acquisition. Lawrence Erlbaum Associates, Mahwah (1994)Google Scholar
- 3.Buchanan, B.G., Shortliffe, E.H.: Rule Based Expert Systems: The Mycin Experiments of the Stanford Heuristic Programming Project. Addison-Wesley, Boston (1984)Google Scholar
- 4.Symeonidis, P., Nanopoulos, A., Manolopoulos, Y.: MoviExplain: a recommender system with explanations. In: Proceedings of the Third ACM Conference on Recommender Systems (RecSys 2009), pp. 317–320. ACM, New York (2009)Google Scholar
- 5.Herlocker, J.L., Konstan, J.A., Riedl, J.: Explaining collaborative filtering recommendations. In: Proceedings of the ACM Conference on Computer Supported Cooperative Work (CSCW 2000), pp. 241–250. ACM, New York (2000)CrossRefGoogle Scholar
- 6.Labreuche, C.: A general framework for explaining the results of a multi-attribute preference model. Artificial Intelligence 175, 1410–1448 (2011)MathSciNetCrossRefMATHGoogle Scholar
- 7.Garey, M., Johnson, D.: Computers and intractability. A guide to the theory of NP-completeness. Freeman, New York (1979)MATHGoogle Scholar
- 8.Junker, U.: QUICKXPLAIN: Preferred explanations and relaxations for over-constrained problems. In: McGuinness, D.L., Ferguson, G. (eds.) Proceedings of the Nineteenth AAAI Conference on Artificial Intelligence (AAAI 2004), pp. 167–172. AAAI Press, Menlo Park (2004)Google Scholar
- 9.O’Sullivan, B., Papadopoulos, A., Faltings, B., Pu, P.: Representative explanations for over-constrained problems. In: Proceedings of the Twenty-Second AAAI Conference on Artificial Intelligence (AAAI 2007), pp. 323–328. AAAI Press, Menlo Park (2007)Google Scholar
- 10.Amgoud, L., Prade, H.: Using arguments for making and explaining decisions. Artificial Intelligence 173, 413–436 (2009)MathSciNetCrossRefMATHGoogle Scholar
- 11.Loui, R.P.: Process and policy: Resource-bounded nondemonstrative reasoning. Computational Intelligence 14, 1–38 (1998)CrossRefGoogle Scholar
- 12.Konczak, K., Lang, J.: Voting procedures with incomplete preferences. In: Brafman, R., Junker, U. (eds.) Proceedings of the IJCAI 2005 Workshop on Advances in Preference Handling, pp. 124–129 (2005)Google Scholar
Copyright information
© Springer-Verlag Berlin Heidelberg 2011