A conversion between utility and information

16Citations
Citations of this article
46Readers
Mendeley users who have this article in their library.

Abstract

Rewards typically express desirabilities or preferences over a set of alternatives. Here we propose that rewards can be defined for any probability distribution based on three desiderata, namely that rewards should be real-valued, additive and order-preserving, where the latter implies that more probable events should also be more desirable. Our main result states that rewards are then uniquely determined by the negative information content. To analyze stochastic processes, we define the utility of a realization as its reward rate. Under this interpretation, we show that the expected utility of a stochastic process is its negative entropy rate. Furthermore, we apply our results to analyze agent-environment interactions. We show that the expected utility that will actually be achieved by the agent is given by the negative cross-entropy from the input-output (I/O) distribution of the coupled interaction system and the agent's I/O distribution. Thus, our results allow for an information-theoretic interpretation of the notion of utility and the characterization of agent-environment interactions in terms of entropy dynamics.

Author supplied keywords

Cite

CITATION STYLE

APA

Ortega, P. A., & Braun, D. A. (2010). A conversion between utility and information. In Artificial General Intelligence - Proceedings of the Third Conference on Artificial General Intelligence, AGI 2010 (pp. 115–120). Atlantis Press. https://doi.org/10.2991/agi.2010.10

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free