Temporal Planning via Interval Logic Satisfiability for Autonomous Systems

TL;DR

This paper introduces a novel temporal planning approach using Interval Logic Satisfiability, modeling complex concurrency with a Constraint Programming framework, and demonstrates superior performance over traditional PDDL 2.1 planners in complex scenarios.

Contribution

It proposes a new planning graph model based on Interval Logic for capturing complex concurrency, implemented within a Constraint Programming framework, and shows improved planning performance.

Findings

Our algorithm outperforms PDDL 2.1 planners in complex concurrency scenarios.

Scalability challenges remain with intricate concurrent interactions.

The approach effectively models complex temporal relations in planning.

Abstract

Many automated planning methods and formulations rely on suitably designed abstractions or simplifications of the constrained dynamics associated with agents to attain computational scalability. We consider formulations of temporal planning where intervals are associated with both action and fluent atoms, and relations between these are given as sentences in Allen's Interval Logic. We propose a notion of planning graphs that can account for complex concurrency relations between actions and fluents as a Constraint Programming (CP) model. We test an implementation of our algorithm on a state-of-the-art framework for CP and compare it with PDDL 2.1 planners that capture plans requiring complex concurrent interactions between agents. We demonstrate our algorithm outperforms existing PDDL 2.1 planners in the case studies. Still, scalability remains challenging when plans must comply with intricate concurrent interactions and the sequencing of actions.