A Probabilistic Model of Action for Least-Commitment Planning with Information Gather

TL;DR

This paper introduces a probabilistic action model that extends traditional planning frameworks to incorporate both causal and informational effects, enabling more flexible and realistic planning under uncertainty.

Contribution

It presents a novel probabilistic action representation that integrates informational effects into least-commitment planning, addressing limitations of deterministic models.

Findings

Extended STRIPS model with informational effects

Planning algorithm supports contingent execution

Handles noisy, context-dependent informational actions

Abstract

AI planning algorithms have addressed the problem of generating sequences of operators that achieve some input goal, usually assuming that the planning agent has perfect control over and information about the world. Relaxing these assumptions requires an extension to the action representation that allows reasoning both about the changes an action makes and the information it provides. This paper presents an action representation that extends the deterministic STRIPS model, allowing actions to have both causal and informational effects, both of which can be context dependent and noisy. We also demonstrate how a standard least-commitment planning algorithm can be extended to include informational actions and contingent execution.