Learning Action Models from Disordered and Noisy Plan Traces

TL;DR

This paper introduces a novel MAX-SAT based method for learning action models from disordered, noisy, and partially observable plan traces, including natural language data, addressing real-world complexities in automated planning.

Contribution

It presents a new approach that handles disordered, noisy, and partially observable plan traces using a MAX-SAT framework, advancing learning in realistic planning scenarios.

Findings

Effective in IPC benchmark domains

Successfully applied to real-world natural language data

Outperforms traditional models in noisy environments

Abstract

There is increasing awareness in the planning community that the burden of specifying complete domain models is too high, which impedes the applicability of planning technology in many real-world domains. Although there have many learning systems that help automatically learning domain models, most existing work assumes that the input traces are completely correct. A more realistic situation is that the plan traces are disordered and noisy, such as plan traces described by natural language. In this paper we propose and evaluate an approach for doing this. Our approach takes as input a set of plan traces with disordered actions and noise and outputs action models that can best explain the plan traces. We use a MAX-SAT framework for learning, where the constraints are derived from the given plan traces. Unlike traditional action models learners, the states in plan traces can be partially observable and noisy as well as the actions in plan traces can be disordered and parallel. We demonstrate the effectiveness of our approach through a systematic empirical evaluation with both IPC domains and the real-world dataset extracted from natural language documents.