Learning STRIPS Operators from Noisy and Incomplete Observations

TL;DR

This paper introduces a method for learning STRIPS action models from noisy and incomplete observations by combining classifier-based transition functions with rule extraction, enabling autonomous agents to better understand real-world dynamics.

Contribution

It presents a novel approach that decomposes the learning process into classifier-based transition modeling and explicit rule derivation, addressing noise and incompleteness in observations.

Findings

Successfully learns domain models from noisy, incomplete data

Outperforms existing methods on standard planning benchmarks

Demonstrates robustness to sensor noise and partial observability

Abstract

Agents learning to act autonomously in real-world domains must acquire a model of the dynamics of the domain in which they operate. Learning domain dynamics can be challenging, especially where an agent only has partial access to the world state, and/or noisy external sensors. Even in standard STRIPS domains, existing approaches cannot learn from noisy, incomplete observations typical of real-world domains. We propose a method which learns STRIPS action models in such domains, by decomposing the problem into first learning a transition function between states in the form of a set of classifiers, and then deriving explicit STRIPS rules from the classifiers' parameters. We evaluate our approach on simulated standard planning domains from the International Planning Competition, and show that it learns useful domain descriptions from noisy, incomplete observations.