AIJan 3, 2017

From Preference-Based to Multiobjective Sequential Decision-Making

arXiv:1701.00646v11 citations
Originality Synthesis-oriented
AI Analysis

This work provides a theoretical foundation for applying multiobjective techniques to preference-based problems, particularly when rewards are unknown, but it is incremental as it builds on existing utility and expectation frameworks.

The paper establishes a formal connection between preference-based and multiobjective sequential decision-making, showing how to transform preference-based problems into multiobjective ones under conditions of additively decomposable utilities and expectation-based policy evaluation, enabling cross-application of solving methods.

In this paper, we present a link between preference-based and multiobjective sequential decision-making. While transforming a multiobjective problem to a preference-based one is quite natural, the other direction is a bit less obvious. We present how this transformation (from preference-based to multiobjective) can be done under the classic condition that preferences over histories can be represented by additively decomposable utilities and that the decision criterion to evaluate policies in a state is based on expectation. This link yields a new source of multiobjective sequential decision-making problems (i.e., when reward values are unknown) and justifies the use of solving methods developed in one setting in the other one.

Foundations

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