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

Hyperplasia

Scaleability in Planning, problems, where resources form a significant component. The empirical studies reveal that least commitment planning, as implemented in .PLAN, is far more effective than the strategies in . and .. But the studies also reveal a serious limitation on the scaleability of .PLAN’s algorithm.

Isometric

勋章

Hierarchical Task Network Planning as Satisfiability,he guidance from the task networks can be used to significantly reduce the sizes of the propositional encodings. We report promising empirical results on various encodings that demonstrate an orders of magnitude reduction in the solving times.

打火石

arousal

Strong Cyclic Planning Revisited,eed”. In such domains, a certain effect (e.g., action success) might never be guaranteed . of execution and, in principle, iterative plans might loop forever. Here, the planner should generate iterative plans whose executions always have a possibility of terminating and, when they do, they are guara

有机体

Scaleability in Planning, a restricted number of table positions, and the number of arms can vary (from 1 upwards). This type of problem is typical of many real world planning problems, where resources form a significant component. The empirical studies reveal that least commitment planning, as implemented in .PLAN, is far

Microgram

Exploiting Competitive Planner Performance, performed a large study to test its limits. In this research, we tested performance of a set of planners to determine which is best on what types of problems. The study included six planners and over 200 problems. We found that performance, as measured by number of problems solved and computation t

易改变

badinage

crutch