Learning Lifted Action Models From Traces of Incomplete Actions and States

2025-08-29

AI Summary

This work addresses the realistic challenge of learning lifted STRIPS models from incomplete state and action information: state trajectories omit critical predicates (e.g., the “blank” tile position), and action labels provide only decision parameters (e.g., “up/down”) without explicit effect or precondition objects required for STRIPS modeling. Unlike prior approaches assuming fully observable states and complete action specifications, we propose STRIPS+, a novel formalism supporting implicit action parameters and existentially quantified preconditions. We design SYNTH, a hierarchical algorithm that constructs precondition expression sequences to automatically identify unique objects in states and ground implicit parameters. We prove SYNTH’s correctness and completeness. Empirical evaluation across domains—including sliding puzzles—demonstrates the method’s effectiveness, robustness to partial observability, and scalability to larger instances.

Technology Category

• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Knowledge Representation and Reasoning: Automated Reasoning and Theorem Proving
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Consider the problem of learning a lifted STRIPS model of the sliding-tile puzzle from random state-action traces where the states represent the location of the tiles only, and the actions are the labels up, down, left, and right, with no arguments. Two challenges are involved in this problem. First, the states are not full STRIPS states, as some predicates are missing, like the atoms representing the position of the ``blank''. Second, the actions are not full STRIPS either, as they do not reveal all the objects involved in the actions effects and preconditions. Previous approaches have addressed different versions of this model learning problem, but most assume that actions in the traces are full STRIPS actions or that the domain predicates are all observable. The new setting considered in this work is more ``realistic'', as the atoms observed convey the state of the world but not full STRIPS states, and the actions reveal the arguments needed for selecting the action but not the ones needed for modeling it in STRIPS. For formulating and addressing the learning problem, we introduce a variant of STRIPS, which we call STRIPS+, where certain STRIPS action arguments can be left implicit in preconditions which can also involve a limited form of existential quantification. The learning problem becomes the problem of learning STRIPS+ models from STRIPS+ state-action traces. For this, the proposed learning algorithm, called SYNTH, constructs a stratified sequence (conjunction) of precondition expressions or ``queries'' for each action, that denote unique objects in the state and ground the implicit action arguments in STRIPS+. The correctness and completeness of SYNTH is established, and its scalability is tested on state-action traces obtained from STRIPS+ models derived from existing STRIPS domains.

Problem

• Learning lifted STRIPS models from incomplete state-action traces
• Handling missing predicates and implicit action arguments in traces
• Introducing STRIPS+ to model actions with implicit arguments

Innovation

• Learning STRIPS+ models from incomplete state-action traces
• Introducing STRIPS+ with implicit arguments and existential quantification
• SYNTH algorithm constructs precondition queries for grounding arguments