S. Koenig and R.G. Simmons. Risk-Sensitive Planning with Probabilistic Decision Graphs. In Proceedings of the International Conference on Principles of Knowledge Representation and Reasoning (KR), pages 363-373, 1994.

Abstract: Probabilistic AI planning methods that minimize expected execution cost have a neutral attitude towards risk. We demonstrate how one can transform planning problems for risk-sensitive agents into equivalent ones for risk-neutral agents provided that exponential utility functions are used. The transformed planning problems can then be solved with these existing AI planning methods. To demonstrate our ideas, we use a probabilistic planning framework (probabilistic decision graphs) that can easily be mapped into Markov decision problems. It allows one to describe probabilistic effects of actions, actions with different costs (resource consumption), and goal states with different rewards. We show the use of probabilistic decision graphs for finding optimal plans for risk-sensitive agents in a stochastic blocks-world domain.

Download the paper in pdf.

Download the paper in gzipped postscript (large download time).

Many publishers do not want authors to make their papers available electronically after the papers have been published. Please use the electronic versions provided here only if hardcopies are not yet available. If you have comments on any of these papers, please send me an email! Also, please send me your papers if we have common interests.

This page was automatically created by a bibliography maintenance system that was developed as part of an undergraduate research project, advised by Sven Koenig.