Task Modifiers for HTN Planning and Acting
Weihang Yuan, Hector Munoz-Avila, Venkatsampath Raja Gogineni, Sravya, Kondrakunta, Michael Cox, Lifang He

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.
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Taxonomy
TopicsMulti-Agent Systems and Negotiation · AI-based Problem Solving and Planning · Semantic Web and Ontologies
