TIDE: A Trace-Informed Depth-First Exploration for Planning with Temporally Extended Goals

2026-01-17

AI Summary

This work addresses the lack of effective heuristic guidance in temporal extended goal (TEG) planning by proposing TIDE, a novel approach that decomposes temporal planning into a sequence of reachability and obstacle-avoidance subproblems. The method leverages LTLf for formal goal specification and integrates off-the-shelf classical planners for subproblem resolution. By analyzing automaton trajectories, TIDE derives cost-driven heuristics and combines them with depth-first search, penalties for infeasible transitions, and an adaptive backtracking mechanism. This integration ensures completeness while substantially improving search efficiency. Experimental results demonstrate that TIDE outperforms existing methods in both success rate and solution speed.

Technology Category

• Planning, Routing, and Scheduling: Temporal Planning
• Search and Optimization: Heuristic Search
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Task planning with temporally extended goals (TEGs) is a critical challenge in AI and robotics, enabling agents to achieve complex sequences of objectives over time rather than addressing isolated, immediate tasks. Linear Temporal Logic on finite traces (LTLf ) provides a robust formalism for encoding these temporal goals. Traditional LTLf task planning approaches often transform the temporal planning problem into a classical planning problem with reachability goals, which are then solved using off-the-shelf planners. However, these methods often lack informed heuristics to provide a guided search for temporal goals. We introduce TIDE (Trace-Informed Depth-first Exploration), a novel approach that addresses this limitation by decomposing a temporal problem into a sequence of smaller, manageable reach-avoid sub-problems, each solvable using an off-the-shelf planner. TIDE identifies and prioritizes promising automaton traces within the domain graph, using cost-driven heuristics to guide exploration. Its adaptive backtracking mechanism systematically recovers from failed plans by recalculating costs and penalizing infeasible transitions, ensuring completeness and efficiency. Experimental results demonstrate that TIDE achieves promising performance and is a valuable addition to the portfolio of planning methods for temporally extended goals.

Problem

• temporally extended goals
• task planning
• LTLf
• automaton traces
• reachability goals

Innovation

• TIDE
• temporally extended goals
• LTLf planning
• trace-informed heuristic
• adaptive backtracking