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ECAI
2010
Springer

On Finding Compromise Solutions in Multiobjective Markov Decision Processes

14 years 18 days ago
On Finding Compromise Solutions in Multiobjective Markov Decision Processes
A Markov Decision Process (MDP) is a general model for solving planning problems under uncertainty. It has been extended to multiobjective MDP to address multicriteria or multiagent problems in which the value of a decision must be evaluated according to several viewpoints, sometimes conflicting. Although most of the studies concentrate on the determination of the set of Pareto-optimal policies, we focus here on a more specialized problem that concerns the direct determination of policies achieving wellbalanced tradeoffs. We first explain why this problem cannot simply be solved by optimizing a linear combination of criteria. This leads us to use an alternative optimality concept which formalizes the notion of best compromise solution, i.e. a policy yielding an expected-utility vector as close as possible (w.r.t. Tchebycheff norm) to a reference point. We show that this notion of optimality depends on the initial state. Moreover, it appears that the best compromise policy cannot be fou...
Patrice Perny, Paul Weng
Added 08 Nov 2010
Updated 08 Nov 2010
Type Conference
Year 2010
Where ECAI
Authors Patrice Perny, Paul Weng
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