Epsilon-Safe Planning

TL;DR

Epsilon-safe planning is a probabilistic high-level planning approach that ensures a specified success probability, introducing algorithms based on conditional planners and addressing probability computation challenges.

Contribution

The paper introduces epsilon-safe planning, a new probabilistic planning method, along with algorithms based on existing conditional planners and methods to compute success probabilities.

Findings

Proposes epsilon-safe planning with success probability at least 1-epsilon.

Develops algorithms based on CNLP and PLINTH planners.

Introduces methods to compute probabilities under independence assumptions.

Abstract

We introduce an approach to high-level conditional planning we call epsilon-safe planning. This probabilistic approach commits us to planning to meet some specified goal with a probability of success of at least 1-epsilon for some user-supplied epsilon. We describe several algorithms for epsilon-safe planning based on conditional planners. The two conditional planners we discuss are Peot and Smith's nonlinear conditional planner, CNLP, and our own linear conditional planner, PLINTH. We present a straightforward extension to conditional planners for which computing the necessary probabilities is simple, employing a commonly-made but perhaps overly-strong independence assumption. We also discuss a second approach to epsilon-safe planning which relaxes this independence assumption, involving the incremental construction of a probability dependence model in conjunction with the construction of the plan graph.