SHOP2: An HTN Planning System

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

SHOP2: An HTN planning system

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.open17833

HTN planning: Overview, comparison, and beyond

Hierarchies are one of the most common structures used to understand and conceptualise the world. Within the field of Artificial Intelligence (AI) planning, which deals with the automation of world-relevant problems, Hierarchical Task Network (HTN) planning is the branch that represents and handles hierarchies. In particular, the requirement for rich domain knowledge to characterise the world enables HTN planning to be very useful, and also to perform well. However, the history of almost 40 years obfuscates the current understanding of HTN planning in terms of accomplishments, planning models, similarities and differences among hierarchical planners, and its current and objective image. On top of these issues, the ability of hierarchical planning to truly cope with the requirements of real-world applications has been often questioned. As a remedy, we propose a framework-based approach where we first provide a basis for defining different formal models of hierarchical planning, and define two models that comprise a large portion of HTN planners. Second, we provide a set of concepts that helps in interpreting HTN planners from the aspect of their search space. Then, we analyse and compare the planners based on a variety of properties organised in five segments, namely domain authoring, expressiveness, competence, computation and applicability. Furthermore, we select Web service composition as a real-world and current application, and classify and compare the approaches that employ HTN planning to solve the problem of service composition. Finally, we conclude with our findings and present directions for future work. In summary, we provide a novel and comprehensive viewpoint on a core AI planning technique.<br/

Hierarchical task network approach for time and budget constrained construction project planning

© 2019 The Author(s). Completing a construction project on time and within budget is of great importance in the construction industry. To achieve this goal, a construction plan satisfying the time and cost constraints is crucial. While a rich amount of literature on the time-cost trade-off scheduling and time/cost optimization scheduling has been presented, developing a construction plan for the time and cost-constrained construction project has not been fully explored. This study presented a hierarchical task network (HTN) based construction planning model to fill this gap. First of all, a knowledge formalism catering to the HTN planning was provided to accommodate the construction planning knowledge. Then, the planning process was explained in detail, including temporal reasoning used to sequence the construction activities, and backtracking evasion mechanism used to avoid the trouble of backtracking due to inappropriate selection of execution modes for construction activities. Finally, two sets of comparisons based on a fictional construction project were performed, the results of which demonstrate that the time and budget constraints have an impact on the sec-tion of execution modes for construction methods, and the proposed planning model can develop construction plan that satisfies the specified deadline and budget limitations effectively regardless of the existing of backtracking

Progress in AI Planning Research and Applications

Planning has made significant progress since its inception in the 1970s, in terms both of the efficiency and sophistication of its algorithms and representations and its potential for application to real problems. In this paper we sketch the foundations of planning as a sub-field of Artificial Intelligence and the history of its development over the past three decades. Then some of the recent achievements within the field are discussed and provided some experimental data demonstrating the progress that has been made in the application of general planners to realistic and complex problems. The paper concludes by identifying some of the open issues that remain as important challenges for future research in planning

A hierarchical task-network planner based on symbolic model checking

Although several approaches have been developed for planning in nondeterministic domains, solving large planning problems is still quite difficult. In this work, we present a novel algorithm, called YoYo, for planning in nondeterministic domains under the assumption of full observability. This algorithm enables us to combine the power of search-control strategies as in Planning with Hierarchical Task Networks (HTNs) with techniques from the Planning via Symbolic Model-Checking (SMC). Our experimental evaluation confirms the potentialities of our approach, demonstrating that it combines the advantages of these paradigms

Route planning algorithms: Planific@ Project

Planific@ is a route planning project for the city of Madrid (Spain). Its main aim is to develop an intelligence system capable of routing people from one place in the city to any other using the public transport. In order to do this, it is necessary to take into account such things as: time, traffic, user preferences, etc. Before beginning to design the project is necessary to make a comprehensive study of the variety of main known route planning algorithms suitable to be used in this projec