Robust Planning in Uncertain Environments

TL;DR

This paper introduces a decision-theoretic planning algorithm that computes robust, risk-aware conditional plans using expected utility, demonstrated through experiments in a non-deterministic blocks world domain.

Contribution

It presents a novel algorithm for robust planning in uncertain environments that incorporates a robustness factor to control risk preferences.

Findings

Robust plans can be tuned via a robustness factor.

The algorithm effectively handles non-deterministic domains.

Experimental results validate the approach's flexibility and effectiveness.

Abstract

This paper describes a novel approach to planning which takes advantage of decision theory to greatly improve robustness in an uncertain environment. We present an algorithm which computes conditional plans of maximum expected utility. This algorithm relies on a representation of the search space as an AND/OR tree and employs a depth-limit to control computation costs. A numeric robustness factor, which parameterizes the utility function, allows the user to modulate the degree of risk-aversion employed by the planner. Via a look-ahead search, the planning algorithm seeks to find an optimal plan using expected utility as its optimization criterion. We present experimental results obtained by applying our algorithm to a non-deterministic extension of the blocks world domain. Our results demonstrate that the robustness factor governs the degree of risk embodied in the conditional plans computed by our algorithm.