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 domains.

The DAML-S Process Model is designed to support the application of AI planning techniques to the automated composition of Web services. SHOP2 is an Hierarchical Task Network (HTN) planner well-suited for working with the Process Model. We have proven the correspondence between the semantics of SHOP2 and the situation calculus semantics of the Process Model. We have also implemented a system which soundly and completely plans over sets of DAML-S descriptions using a SHOP2 planner, and then executes the resulting plans over the Web. We discuss the challenges and di#culties of using SHOP2 in the information-rich and human-oriented context of Web services.

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 and complete algorithm to translate OWL-S service descriptions to a SHOP2 domain. We prove the correctness of the algorithm by showing the correspondence to the situation calculus semantics of OWL-S. We implemented a system that plans over sets of OWL-S descriptions using SHOP2 and then executes the resulting plans over the Web. The system is also capable of executing information-providing Web Services during the planning process. We discuss the challenges and difficulties of using planning in the information-rich and human-oriented context of Web Services.

Semantic markup of Web services will enable the automation of various kinds of tasks, including discovery, composition, and execution of Web services. We describe how an AI planning system (SHOP2) can be used with DAML-S Web service descriptions to automatically compose Web services.

In this paper, we present the partitioning of mutual-exclusion (mutex) constraints in temporal planning problems and its implementation in the SGPlan4 planner. Based on the strong locality of mutex constraints observed in many benchmarks of the Fourth International Planning Competition (IPC4), we propose to partition the constraints of a planning problem into groups based on their subgoals. Constraint partitioning leads to significantly easier subproblems that are similar to the original problem and that can be efficiently solved by the same planner with some modifications to its objective function. We present a partition-and-resolve strategy that looks for locally optimal subplans in constraint-partitioned temporal planning subproblems and that resolves those inconsistent global constraints across the subproblems. We also discuss some implementation details of SGPlan4, which include the resolution of violated global constraints, techniques for handling producible resources, landmark analysis, path finding and optimization, search-space reduction, and modifications of Metric-FF when used as a basic planner in SGPlan4. Last, we show results on the sensitivity of each of these techniques in quality-time trade-offs and experimentally demonstrate that SGPlan4 is effective for solving the IPC3 and IPC4 benchmarks. 1.

The Semantic Web has the potential to allow software agents to intelligently process and integrate the Web's wealth of information. These agents must plan how to achieve their goals in light of the information available.

This paper provides a general mechanism and a solid theoretical basis for performing planning within Belief-Desire-Intention (BDI) agents. BDI agent systems have emerged as one of the most widely used approaches to implementing intelligent behaviour in complex dynamic domains, in addition to which they have a strong theoretical background. However, these systems either do not include any built-in capacity for "lookahead" type of planning or they do it only at the implementation level without any precise defined semantics. In some situations, the ability to plan ahead is clearly desirable or even mandatory for ensuring success. Also, a precise definition of how planning can be integrated into a BDI system is highly desirable. By building on the underlying similarities between BDI systems and Hierarchical Task Network (HTN) planners, we present a formal semantics for a BDI agent programming language which cleanly incorporates HTN-style planning as a built-in feature. We argue that the resulting integrated agent programming language combines the advantages of both BDI agent systems and hierarchical offline planners.

In this paper we describe a formalism for integrating the SHOP HTN planning sys-tem with the IMPACT multi-agent environment. We define the A-SHOP algorithm, an agentized adaptation of the SHOP planning algorithm that takes advantage of IMPACT’s capabilities for interacting with external agents, performing mixed sym-bolic/numeric computations, and making queries to distributed, heterogeneous in-formation sources (such as arbitrary legacy and/or specialized data structures or external databases). We show that A-SHOP is both sound and complete if certain conditions are met.

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

... of hand-tailorable planners by compiling each domain description into a separate domain-specific planner. We discuss why and when this approach can be useful, and we present experimental results showing that our approach produces significant increases in the speed of planning.