Efficient Temporal Piecewise-Linear Numeric Planning with Lazy Consistency Checking

TL;DR

This paper introduces a scalable approach for temporal numeric planning that reduces the computational overhead of consistency checking by selectively formulating smaller linear programs, improving performance on real-world problems.

Contribution

It proposes techniques for selective consistency checking and smaller linear programs, enhancing scalability and efficiency in temporal numeric planning.

Findings

Outperforms state-of-the-art planners in coverage and scalability.

Effective on domains with mixed discrete and continuous effects.

Reduces linear programming overhead in planning processes.

Abstract

Temporal planning often involves numeric effects that are directly proportional to their action's duration. These include continuous effects, where a numeric variable is subjected to a rate of change while the action is being executed, and discrete duration-dependent effects, where the variable is updated instantaneously but the magnitude of such change is computed from the action's duration. When these effects are linear, state--of--the--art temporal planners often make use of Linear Programming to ensure that these numeric updates are consistent with the chosen start times and durations of the plan's actions. This is typically done for each evaluated state as part of the search process. This exhaustive approach is not scalable to solve real-world problems that require long plans, because the linear program's size becomes larger and slower to solve. In this work we propose techniques that minimise this overhead by computing these checks more selectively and formulating linear programs that have a smaller footprint. The effectiveness of these techniques is demonstrated on domains that use a mix of discrete and continuous effects, which is typical of real-world planning problems. The resultant planner also outperforms most state-of-the-art temporal-numeric and hybrid planners, in terms of both coverage and scalability.