Probabilistic contingent planning based on HTN for high-quality plans

TL;DR

This paper introduces HQCP, a probabilistic contingent HTN planner that generates high-quality, flexible plans in partially observable environments, extending HTN formalisms and evaluating cost-effectiveness through empirical studies.

Contribution

It extends HTN planning formalisms to partial observability, introduces a novel heuristic for high-quality plans, and develops an integrated planning algorithm.

Findings

Effective in probabilistic contingent planning

Produces high-quality plans efficiently

Validated through empirical studies

Abstract

Deterministic planning assumes that the planning evolves along a fully predictable path, and therefore it loses the practical value in most real projections. A more realistic view is that planning ought to take into consideration partial observability beforehand and aim for a more flexible and robust solution. What is more significant, it is inevitable that the quality of plan varies dramatically in the partially observable environment. In this paper we propose a probabilistic contingent Hierarchical Task Network (HTN) planner, named High-Quality Contingent Planner (HQCP), to generate high-quality plans in the partially observable environment. The formalisms in HTN planning are extended into partial observability and are evaluated regarding the cost. Next, we explore a novel heuristic for high-quality plans and develop the integrated planning algorithm. Finally, an empirical study verifies the effectiveness and efficiency of the planner both in probabilistic contingent planning and for obtaining high-quality plans.