Qualitative Decision Theory with Sugeno Integrals

arXiv.org Artificial Intelligence

This paper presents an axiomatic framework for qualitative decision under uncertainty in a finite setting. The corresponding utility is expressed by a sup-min expression, called Sugeno (or fuzzy) integral. Technically speaking, Sugeno integral is a median, which is indeed a qualitative counterpart to the averaging operation underlying expected utility. The axiomatic justification of Sugeno integral-based utility is expressed in terms of preference between acts as in Savage decision theory. Pessimistic and optimistic qualitative utilities, based on necessity and possibility measures, previously introduced by two of the authors, can be retrieved in this setting by adding appropriate axioms.


On the Foundations of Qualitative Decision Theory

AAAI Conferences

Our main result is a constructive representation theorem in the spirit of Savage's result for expected utility maximization, which uses two choice axioms to characterize the maxapnin criterion. These axioms characterize agent behaviors that can be modeled compactly using the maxcirnin model, and, with some reservations, indicate that rnaxionin is a reasonable decision criterion.


Decision Principles to justify Carnap's Updating Method and to Suggest Corrections of Probability Judgments (Invited Talks)

arXiv.org Artificial Intelligence

This paper uses decision-theoretic principles to obtain new insights into the assessment and updating of probabilities. First, a new foundation of Bayesianism is given. It does not require infinite atomless uncertainties as did Savage s classical result, AND can therefore be applied TO ANY finite Bayesian network.It neither requires linear utility AS did de Finetti s classical result, AND r ntherefore allows FOR the empirically AND normatively desirable risk r naversion.Finally, BY identifying AND fixing utility IN an elementary r nmanner, our result can readily be applied TO identify methods OF r nprobability updating.Thus, a decision - theoretic foundation IS given r nto the computationally efficient method OF inductive reasoning r ndeveloped BY Rudolf Carnap.Finally, recent empirical findings ON r nprobability assessments are discussed.It leads TO suggestions FOR r ncorrecting biases IN probability assessments, AND FOR an alternative r nto the Dempster - Shafer belief functions that avoids the reduction TO r ndegeneracy after multiple updatings.r n


Relating Decision under Uncertainty and MCD Models D. Dubois and H. Prade

AAAI Conferences

For a long time, Artificial Intelligence had not been much concerned by decision issues. However, many reasoning tasks are more or less oriented towards decision or involve decision steps. In the last past five years, decision under uncertainty has become a topic of interest in AI. The application of classical expected utility theory to planning under uncertainty and the algorithmic igsues raised by its implementation have been specially investigated (e.g., [7], [5]) as well as a search for more qualitative approaches [4].


Decision Making for Symbolic Probability

arXiv.org Artificial Intelligence

This paper proposes a decision theory for a symbolic generalization of probability theory (SP). Darwiche and Ginsberg [2,3] proposed SP to relax the requirement of using numbers for uncertainty while preserving desirable patterns of Bayesian reasoning. SP represents uncertainty by symbolic supports that are ordered partially rather than completely as in the case of standard probability. We show that a preference relation on acts that satisfies a number of intuitive postulates is represented by a utility function whose domain is a set of pairs of supports. We argue that a subjective interpretation is as useful and appropriate for SP as it is for numerical probability. It is useful because the subjective interpretation provides a basis for uncertainty elicitation. It is appropriate because we can provide a decision theory that explains how preference on acts is based on support comparison.