Titlebook: Recent Advances in AI Planning; 5th European Confere Susanne Biundo,Maria Fox Conference proceedings 2000 Springer-Verlag Berlin Heidelberg

Twinge

书目名称Recent Advances in AI Planning影响因子(影响力)




书目名称Recent Advances in AI Planning影响因子(影响力)学科排名




书目名称Recent Advances in AI Planning网络公开度




书目名称Recent Advances in AI Planning网络公开度学科排名




书目名称Recent Advances in AI Planning被引频次




书目名称Recent Advances in AI Planning被引频次学科排名




书目名称Recent Advances in AI Planning年度引用




书目名称Recent Advances in AI Planning年度引用学科排名




书目名称Recent Advances in AI Planning读者反馈




书目名称Recent Advances in AI Planning读者反馈学科排名

蒙太奇

Plan Merging & Plan Reuse as Satisfiability,ns can be removed and new actions can be added. We report the asymptotic sizes of the propositional encodings for several cases of plan reuse and plan merging. Our empirical evaluation shows that the satisfiability paradigm can scale up to handle plan reuse and plan merging.

草率男

Numeric State Variables in Constraint-Based Planning,lete state description of every time point of a plan execution. The approach we take requires that for operators that are executed in parallel, all linearizations of the operations to total orders behave equivalently. We provide an efficient and general solution to the problem.

burnish

Action Constraints for Planning,nts in the framework of parallel total-order planning, which is the design of the most powerful planners at the moment. We present two classes of action constraints and demonstrate their capabilities in the planner ..

侵蚀

慢跑鞋

LEERY

lattice

Automata-Theoretic Approach to Planning for Temporally Extended Goals,ed; and states are only partially observable. We prove that the techniques proposed are optimal wrt the worst case complexity of the problem. Thanks to the scalability of the nonemptiness algorithms, the techniques presented here promise to be applicable to fairly large systems, notwithstanding the intrinsic complexity of the problem.

FICE

Exploiting Competitive Planner Performance,mented a meta-planner that interleaves execution of six planners on a problem until one of them solves it. The control strategy for ordering the planners and allocating time is derived from the performance study data. We found that our meta-planner is able to solve more problems than any single planner, but at the expense of computation time.

Euthyroid