Synthesizing Robust Plans under Incomplete Domain Models

TL;DR

This paper introduces a method for generating plans that are robust to known incompleteness in domain models by formalizing annotations and compiling the problem into conformant probabilistic planning, demonstrated with experimental results.

Contribution

It formalizes domain incompleteness annotations and compiles robust planning into conformant probabilistic planning, addressing a key challenge in domain modeling.

Findings

Experimental results with Probabilistic-FF show the effectiveness of the approach.

The method successfully generates plans robust to domain model incompleteness.

Annotations improve planning robustness in incomplete domains.

Abstract

Most current planners assume complete domain models and focus on generating correct plans. Unfortunately, domain modeling is a laborious and error-prone task. While domain experts cannot guarantee completeness, often they are able to circumscribe the incompleteness of the model by providing annotations as to which parts of the domain model may be incomplete. In such cases, the goal should be to generate plans that are robust with respect to any known incompleteness of the domain. In this paper, we first introduce annotations expressing the knowledge of the domain incompleteness, and formalize the notion of plan robustness with respect to an incomplete domain model. We then propose an approach to compiling the problem of finding robust plans to the conformant probabilistic planning problem. We present experimental results with Probabilistic-FF, a state-of-the-art planner, showing the promise of our approach.