Minimal and Complete Explanations for Critical Multi-attribute Decisions

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.

[1]  Michael Pittarelli,et al.  Book review: Decision-Analytic Intelligent Systems by David A. Klein (Lawrence Erlbaum Associates, 1994) , 1995, SGAR.

[2]  Johanna D. Moore,et al.  Generating and evaluating evaluative arguments , 2006, Artif. Intell..

[3]  David A. Klein,et al.  Decision-Analytic Intelligent Systems: Automated Explanation and Knowledge Acquisition , 1994 .

[4]  David S. Johnson,et al.  Computers and In stractability: A Guide to the Theory of NP-Completeness. W. H Freeman, San Fran , 1979 .

[5]  Henri Prade,et al.  Using arguments for making and explaining decisions , 2009, Artif. Intell..

[6]  David S. Johnson,et al.  Computers and Intractability: A Guide to the Theory of NP-Completeness , 1978 .

[7]  Ronald Prescott Loui,et al.  Process and Policy: Resource‐Bounded NonDemonstrative Reasoning , 1998, Comput. Intell..

[8]  Jérôme Lang,et al.  Voting procedures with incomplete preferences , 2005 .

[9]  Christophe Labreuche,et al.  A general framework for explaining the results of a multi-attribute preference model , 2011, Artif. Intell..

[10]  Edward H. Shortliffe,et al.  Rule Based Expert Systems: The Mycin Experiments of the Stanford Heuristic Programming Project (The Addison-Wesley series in artificial intelligence) , 1984 .

[11]  Panagiotis Symeonidis,et al.  MoviExplain: a recommender system with explanations , 2009, RecSys '09.

[12]  John Riedl,et al.  Explaining collaborative filtering recommendations , 2000, CSCW '00.

[13]  Ulrich Junker,et al.  QUICKXPLAIN: Preferred Explanations and Relaxations for Over-Constrained Problems , 2004, AAAI.

[14]  Barry O'Sullivan,et al.  Representative Explanations for Over-Constrained Problems , 2007, AAAI.