Titlebook: Recent Advances in AI Planning; 4th European Confere Sam Steel,Rachid Alami Conference proceedings 1997 Springer-Verlag Berlin Heidelberg 1

组装

Célia Da Costa Pereira,Frédérick Garcia,Jérôme Lange,Roger Martin-Clouaire

harpsichord

罗盘

Information gathering plans with sensing actions,d at run-time to optimize a query plan. Next, we describe how the discrimination matrix is used to decide which of the possible run-time sensing actions to perform. Finally, we demonstrate that this approach yields significant savings (over 90% for some queries) in a real-world task.

Bureaucracy

Planning, learning, and executing in autonomous systems,efinitions, plans using those operators, and executes the plans for modifying the acquired operators. The results clearly show that the integrated planning, learning, and executing system outperforms the basic planner in a robot domain.

一回合

Combining the expressivity of UCPOP with the efficiency of Graphplan,ard problems (i.e., those that require more than one second of CPU time) it can solve them significantly faster than UCPOP. While UCPOP was unable to solve 7 of the test problems within a search limit of 100,000 nodes (which requires 414 to 980 CPU seconds), Graphplan with the preprocessor solved th

penance

debris

Conference proceedings 1997 revised full papers presented were carefully reviewed and selected from 90 submissions. The range of topics covered spans all aspects of current artificial intelligence planning, from theoretical and foundational matters to actual planning of systems and applications in a variety of areas.

nerve-sparing

0302-9743 97. The 35 revised full papers presented were carefully reviewed and selected from 90 submissions. The range of topics covered spans all aspects of current artificial intelligence planning, from theoretical and foundational matters to actual planning of systems and applications in a variety of areas

bonnet

A time and resource problem for planning architectures,nts of time (the time values) to synthesize necessary quantitative temporal constraints. The Order Propagation technique is more original. It observes single time points (the time variables) and their orderings to synthesize necessary qualitative temporal constraints.

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