Temporal Planning with Intermediate Conditions and Effects

TL;DR

This paper introduces a heuristic-search method for temporal planning that allows defining actions with conditions and effects at any point within their duration, improving flexibility over standard models.

Contribution

It presents a novel approach enabling intermediate conditions and effects in temporal planning, surpassing standard PDDL 2.1 encodings.

Findings

Outperforms standard PDDL 2.1 encodings in experiments

Competitive with approaches that model intermediate conditions/effects

Effective heuristic-search technique for complex temporal actions

Abstract

Automated temporal planning is the technology of choice when controlling systems that can execute more actions in parallel and when temporal constraints, such as deadlines, are needed in the model. One limitation of several action-based planning systems is that actions are modeled as intervals having conditions and effects only at the extremes and as invariants, but no conditions nor effects can be specified at arbitrary points or sub-intervals. In this paper, we address this limitation by providing an effective heuristic-search technique for temporal planning, allowing the definition of actions with conditions and effects at any arbitrary time within the action duration. We experimentally demonstrate that our approach is far better than standard encodings in PDDL 2.1 and is competitive with other approaches that can (directly or indirectly) represent intermediate action conditions or effects.