Creating art in the woods.
Creating art in the woods.
A federal court has struck down Donald Trump’s “Liberation Day” tariffs, ruling that the president overstepped his legal authority in imposing them on a broad range of countries, according to media reports.The U.S. Court of International Trade issued the unanimous decision Wednesday, rejecting Trump...
Oh hey maybe I should post about it here. A bevy plugin I made for my current game project that provides an implementation of Hierarchical Task Network Planning AI with more or less simple to configure setup.
Anyway if some bevy devs wanna use an HTN and don't wanna worry about the setup, congrats bc I made one.
[Перевод] Введение в планировщики иерархических сетей задач (HTN) на примере. Часть 2
В прошлой части мы остановились на том, что сформировали из составных и примитивных задач функциональную область ( domain ), которая представляет всю иерархию задач нашего NPC. Объединив ее с состоянием мира ( world state ), мы можем перейти к рабочей лошадке нашей HTN — планировщику ( planner ). Есть три условия, которые заставляют планировщик искать новый план: NPC завершает или проваливает текущий план, у NPC нет плана, или какой-нибудь сенсор меняет состояние мира NPC.
Hierarchical Task Network (HTN) planning is a practical and efficient approach to planning when the 'standard operating procedures' for a domain are available. Like Belief-Desire-Intention (BDI) agent reasoning, HTN planning performs hierarchical and context-based refinement of goals into subgoals and basic actions. However, while HTN planners 'lookahead' over the consequences of choosing one refinement over another, BDI agents interleave refinement with acting. There has been renewed interest in making HTN planners behave more like BDI agent systems, e.g. to have a unified representation for acting and planning. However, past work on the subject has remained informal or implementation-focused. This paper is a formal account of 'HTN acting', which supports interleaved deliberation, acting, and failure recovery. We use the syntax of the most general HTN planning formalism and build on its core semantics, and we provide an algorithm which combines our new formalism with the processing of exogenous events. We also study the properties of HTN acting and its relation to HTN planning.