Task Modifiers for HTN Planning and Acting

TL;DR

This paper introduces task modifiers for HTN planning, enabling agents to adapt objectives dynamically in response to unexpected events, especially in interleaved planning and execution scenarios.

Contribution

It extends HTN planning with task modifiers that adjust tasks based on state, improving adaptability in dynamic environments.

Findings

Effective in interleaved planning and execution scenarios

Improves agent adaptability to exogenous events

Validated in diverse environments including simulation

Abstract

The ability of an agent to change its objectives in response to unexpected events is desirable in dynamic environments. In order to provide this capability to hierarchical task network (HTN) planning, we propose an extension of the paradigm called task modifiers, which are functions that receive a task list and a state and produce a new task list. We focus on a particular type of problems in which planning and execution are interleaved and the ability to handle exogenous events is crucial. To determine the efficacy of this approach, we evaluate the performance of our task modifier implementation in two environments, one of which is a simulation that differs substantially from traditional HTN domains.