One of the more controversial recent planning algorithms is the SHOP algorithm, an HTN planning algorithm that plans for tasks in the same order that they are to be executed. SHOP can use domaindependent knowledge to generate plans very quickly, but it can be difficult to write good knowledge bases for SHOP. Our hypothesis is that this difficulty is because SHOP’s total-ordering requirement for the subtasks of its methods is more restrictive than it needs to be. To examine this hypothesis, we have developed a new HTN planning algorithm called SHOP2. Like SHOP, SHOP2 is sound and complete, and it constructs plans in the same order that they will later be executed. But unlike SHOP, SHOP2 allows the subtasks of each

Abstract. Flexible support for crisis management can definitely be improved by making use of advanced planning capabilities. However, the complexity of the underlying domain often causes intractable efforts in modeling the domain as well as a huge search space to be explored by the system. A way to overcome these problems is to impose a structure not only according to tasks but also according to relationships between and properties of the objects involved, thereby using so-called decomposition axioms. We outline the prototype of a system that is capable of tackling planning for complex application domains. It is based on a well-founded combination of action and state abstractions. The paper presents the basic techniques and provides a formal semantic foundation of the approach. It introduces the planning system and illustrates its underlying principles by examples taken from the crisis management domain used in our ongoing project. 1

In this paper we investigate a formalism for solving planning problems based on ordered task decomposition using Answer Set Programming (ASP). Our planning methodology is an adaptation of Hierarchical Task Network (HTN) planning, an approach that has led to some very efficient planners. The ASP paradigm evolved out of the stable semantics for logic programs in recent years and is strongly related to nonmonotonic logics. It also led to various very efficient implementations (Smodels, DLV). While all previous approaches for using ASP for planning rely on action-based planning, we consider for the first time a formulation of HTN planning as described in the SHOP planning system and define a systematic translation method from SHOP’s representation of the planning problem into logic programs with negation. We show that our translation is sound and complete: answer sets of the logic program obtained by our translation correspond exactly to the solutions of the planning problem. Our approach does not rely on a particular system for computing answer sets and serves several purposes. (1) It constitutes a means

In this paper we present an algorithm for planning in nondeterministic domains. Our algorithm C-SHOP extends the successful classical HTN planner SHOP, by introducing new mechanisms to handle situations where there is incomplete and uncertain information about the state of the environment. Being an HTN planner, C-SHOP supports coding domain-dependent knowledge in a powerful way that describes how to solve the planning problem.

this paper we describe a formalism for integrating the SHOP HTN planning system

In this paper we introduce a formalism for solving Hierarchical Task Network (HTN) Planning using Answer Set Programming (ASP). The ASP paradigm evolved out of the stable semantics for logic programs in recent years and is strongly related to nonmonotonic logics. We consider the formulation of HTN planning as described in the SHOP planning system and dene a systematic translation method from SHOP's representation of the planning problem into logic programs with negation. We show that our translation is sound and complete: answer sets of the logic program obtained by our translation correspond exactly to the solutions of the planning problem. Our approach does not rely on a particular system for computing answer sets. It can therefore serve as a means to evaluate ASP systems by using well-established benchmarks from the planning community. We tested our method on various such benchmarks and used smodels and DLV for computing answer sets. We compared our method to (1) similar approaches based on non-HTN planning and (2) SHOP, a dedicated planning system. We show that our approach outperforms non-HTN methods and that its performance is closer to that of SHOP, when we are using ASP systems which allow for nonground programs.

In this paper we report on the extension of the classical HTN planner SHOP to plan in partially observable domains with uncertainty. Our algorithm PC-SHOP uses belief states to handle situations involving incomplete and uncertain information about the state of the world. Sensing and acting are integrated in the primitive actions through the use of a stochastic model. PC-SHOP is showed to scale up well compared to some of the state-of-the-art planners. We outline the main characteristics of the algorithm, and present performance results on some problems found in the literature.

Many real world planning problems involve resources that may not be completely specified at the time of plan generation and that may be utilized opportunistically during plan execution. What is needed in addressing such problems is the ability to reason about hypothetical resources and to generate bounds for other resources to ensure feasible choices during plan execution. We present a slight twist to the well known "travel domain" and a solution model to illustrate this point.

In this paper we present PTLplan and PCshop, two progressive planners for partially observable and probabilistic domains. PTLplan and PCshop are instances of a general planning schema based on progressive exploration of a transition graph where the nodes are belief states, and where plans have the form of policy graphs. The policy graphs may contain cycles, and provide a measure of expected cost. In addition, search control information is used to restrict what policies are explored. PTLplan uses temporal logic formulas in the manner of Bacchus and Kabanza’s TLplan for this purpose, whereas PCshop is a hierarchical planner derived from shop by Nau and others. We show in what ways the original techniques of TLplan and shop need to be adapted, among other things by introducing modalities on belief states, and conditional task network operators. We present some experimental results from a number of scenarios, including the omelette scenario (and an extension thereof), the power supply restoration scenario, an extension of the coffee robot scenario, and an anchoring recovery scenario from robotics.