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Hierarchical Task Network planning in the Goal Lifecycle — arXiv:2610.34668

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.34668
License (as archived): CC0-1.0
Archived: 2026-10-27T07:57:36.904Z
SHA-256 of the archived text: 032979f0a680ee7574cef4c0f0f22379f93559f4bba3aa47be6ad7bd4aef6a8c
Stable URL of this page: https://metraipolis.com/documento/dc_dc6e2cfe232f

How to cite

Hierarchical Task Network planning in the Goal Lifecycle — arXiv:2610.34668 — arXiv (en), https://arxiv.org/abs/2610.34668 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_dc6e2cfe232f (Metraipolis archive dc_dc6e2cfe232f, sha256 032979f0a680ee7574cef4c0f0f22379f93559f4bba3aa47be6ad7bd4aef6a8c)

BibTeX

@misc{dcdc6e2cfe232f,
  title = {Hierarchical Task Network planning in the Goal Lifecycle — arXiv:2610.34668},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.34668},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_dc6e2cfe232f; sha256 032979f0a680ee7574cef4c0f0f22379f93559f4bba3aa47be6ad7bd4aef6a8c},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.34668. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Goal reasoning (GR) is the process by which intelligent agents continually reason about the goals they are pursuing, which may lead to goal change, for instance when opportunities arise, or when events cause discrepancies among expected and actual outcomes of actions planned to achieve goals. In this paper we investigate whether Hierarchical Task Network (HTN) planning can provide a uniform implementation of the Goal Lifecycle, a flexible process model for goal reasoning in dynamic environments. The Goal Lifecycle progressively refines goals by adding constraints to them through a series of strategies (e.g., {\it formulate, select, expand}) and uses other strategies (e.g., {\it defer, re-expand, repair}) to resolve the current goal when an event impacts it. We present a novel GR agent architecture that employs HTN planning in this Goal Lifecycle. In addition to using HTN planning for the \textit{expand} and \textit{re-expand} strategies, as is frequently done in GR and planning research, we also use it in the \textit{formulate} strategy, to obtain a uniform planning approach that provides several benefits (e.g., the possibility to formulate new goals in view of achieving higher level overarching goals). We also explain how we use an enhanced HTN action representation to process discrepancies among expected and actual action outcomes that can trigger the transition from one goal to another. Finally, we empirically demonstrate that our GR architecture enables simulated autonomous vehicles to adaptively conduct missions in dynamic environments.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2610.34668 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-27T07:55:30.698Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).