Abstract not found

Most practical work on AI planning systems during the last fteen years has been based on hierarchical task network (HTN) decomposition, but until now, there has been very little analytical work on the properties of HTN planners. This paper describes how the complexity of HTN planning varies

should perform tasks, while also giving the planner enough flexibility to choose the lower-level steps and their order-ing. In this paper we present the HATP (Hierarchical Agent-based Task Planner) planning framework which extends the traditional HTN planning domain representation and seman

-known planners such as HSP and FF, Fast Downward is a progression planner, searching the space of world states of a planning task in the forward direction. However, unlike other PDDL planning systems, Fast Downward does not use the propositional PDDL representation of a planning task directly. Instead, the input

This paper presents some enhancements in the temporal reasoning of a Hierarchical Task Network (HTN) planner, named SIADEX, that, up to authors knowledge, no other HTN planner has. These new features include a sound partial order metric structure, deadlines, temporal landmarking or synchronization

of preferences over HTN constructs. To compute preferred HTN plans, we propose a branch-and-bound algorithm, together with a set of heuristics that, leveraging HTN structure, measure progress towards satisfaction of preferences. Our preference-based planner, HTNPLAN-P, is implemented as an extension of SHOP2. We

This paper describes HICAP, a general purpose and interactive case-based planning architecture. HICAP is a decision support tool for planning a hierarchical course of action. It integrates a hierarchical task editor, HTE, with a conversational case-based planner, NaCoDAE/HTN. HTE maintains a task

Most practical work on AI planning systems during the last fteen years has been based on hierarchical task network (HTN) decomposition, but until now, there has been very little analytical work on the properties of HTN planners. This paper describes how the complexity of HTN planning varies

Abstract. This paper presents some enhancements in the temporal reasoning of a Hierarchical Task Network (HTN) planner, named SIADEX, that, up to authors knowledge, no other HTN planner has. These new features include a sound partial order metric structure, deadlines, temporal landmarking

, has a space of domain names arranged hierarchically. Since Artificial Intelligence (AI) planning portrays information about the world and reasons to solve some of world’s problems, Hierarchical Task Network (HTN) planning has been introduced almost 40 years ago to represent and deal with hierarchies