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

The SHOP2 planning system received one of the awards for distinguished performance in the 2002 International Planning Competition. This paper describes the features of SHOP2 which enabled it to excel in the competition, especially those aspects of SHOP2 that deal with temporal and metric planning

Casting planning problems as propositional satisfiability problems has recently been shown to be an effective way of scaling up plan synthesis. Until now, the benefits of this approach have only been utilized in primitive action-based planning models. Motivated by the conventional wisdom

Automated composition of Web Services can be achieved by using AI planning techniques. Hierarchical Task Network (HTN) planning is especially well-suited for this task. In this paper, we describe how HTN planning system SHOP2 can be used with OWL-S Web Service descriptions. We provide a sound

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

A major virtue of von Neumann-Morgenstern utilities, for example, in the theory of general financial equilibrium (GFE), is that they ensure time consistency: consumption-portfolio plans (for the future) are in fact executed (in the future) — assuming that there is perfect foresight about relevant

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

of the competition and always connect to the network which can best service their preferences for the current application. They will rely on intelligent network selection decision strategies to aid them in their choice. The contribution of this paper is to propose an intelligent utility-based strategy for network

of simple and temporally extended 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

We describe the HDL algorithm, which learns HTN domain representations by examining plan traces produced by an expert problem-solver. Prior work on learning HTN methods required everything to be given in advance except for the methods ’ preconditions, and the learner would learn the preconditions