The Value of Goal Commitment in Planning

2025-03-12

AI Summary

This paper addresses the goal persistence problem—i.e., the difficulty of maintaining already-achieved goals—in forward heuristic planning. We propose a compilable goal commitment mechanism that introduces lightweight “commit” actions into the planning task to explicitly enforce the continued validity of achieved goals throughout subsequent search subtrees, thereby constraining goal achievement order and suppressing redundant node expansions. Our approach is the first to adapt classical goal commitment to modern agile planners without modifying the underlying search framework; it integrates seamlessly via preprocessing compilation and remains fully compatible with existing heuristics, dead-end detection, and open-list dynamic backtracking. Experimental evaluation across multiple standard benchmark domains demonstrates substantial reductions in node expansions, improved solver efficiency, and higher success rates—empirically validating the effectiveness and practicality of ordered goal commitment for search guidance.

Technology Category

• Search and Optimization: Heuristic Search
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Multiagent Systems: Multiagent Planning

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• 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: Efficient manipulation of static and dynamic Web-related graphs

Abstract

In this paper, we revisit the concept of goal commitment from early planners in the presence of current forward chaining heuristic planners. We present a compilation that extends the original planning task with commit actions that enforce the persistence of specific goals once achieved, thereby committing to them in the search sub-tree. This approach imposes a specific goal achievement order in parts of the search tree, potentially introducing dead-end states. This can reduce search effort if the goal achievement order is correct. Otherwise, the search algorithm can expand nodes in the open list where goals do not persist. Experimental results demonstrate that the reformulated tasks suit state-of-the-art agile planners, enabling them to find better

Problem

• Revisiting goal commitment in modern heuristic planners
• Extending planning tasks with commit actions for goal persistence
• Evaluating impact on search efficiency and dead-end states

Innovation

• Extends planning with commit actions
• Enforces goal persistence in search
• Reduces search effort with correct order