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

Probabilistic contingent planning based on HTN for high-quality plans

Deterministic planning assumes that the planning evolves along a fully predictable path, and therefore it loses the practical value in most real projections. A more realistic view is that planning ought to take into consideration partial observability beforehand and aim for a more flexible and robust solution. What is more significant, it is inevitable that the quality of plan varies dramatically in the partially observable environment. In this paper we propose a probabilistic contingent Hierarchical Task Network (HTN) planner, named High-Quality Contingent Planner (HQCP), to generate high-quality plans in the partially observable environment. The formalisms in HTN planning are extended into partial observability and are evaluated regarding the cost. Next, we explore a novel heuristic for high-quality plans and develop the integrated planning algorithm. Finally, an empirical study verifies the effectiveness and efficiency of the planner both in probabilistic contingent planning and for obtaining high-quality plans.Comment: 10 pages, 1 figur

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

A proposal for a global task planning architecture using the RoboEarth cloud based framework

As robotic systems become more and more capable of assisting in human domains, methods are sought to compose robot executable plans from abstract human instructions. To cope with the semantically rich and highly expressive nature of human instructions, Hierarchical Task Network planning is often being employed along with domain knowledge to solve planning problems in a pragmatic way. Commonly, the domain knowledge is specific to the planning problem at hand, impeding re-use. Therefore this paper conceptualizes a global planning architecture, based on the worldwide accessible RoboEarth cloud framework. This architecture allows environmental state inference and plan monitoring on a global level. To enable plan re-use for future requests, the RoboEarth action language has been adapted to allow semantic matching of robot capabilities with previously composed plans