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This paper introduces the notion of heuristic planning, and describes a particular approach to heuristic planning based on a declarative formalization of strategies for action selection.
This approach is compared with some heuristic planning systems proposed in the literature.
The heuristic planning algorithm (WHIPP) has been developed and validated for the prediction of path loss in indoor environments[27, 28].
Heuristic planning tools seem to offer a good methodology to deal with a sub-optimal scheduling policy of present flexible manufacturing systems.
In [ 16], the authors presented the WiCa Heuristic Indoor Propagation Prediction (WHIPP) tool, a set of heuristic planning algorithms, experimentally validated for network planning in indoor environments [ 16].
Finally, conclusions and future work are presented in Section 4. The WHIPP algorithm is a heuristic planning algorithm, developed and validated for the prediction of path loss in indoor environments [ 16].
Similar(54)
A heuristic network planning algorithm has been developed and validated for the prediction of path loss in indoor environments.
We also discuss the strengths and limitations of this approach, establish a correspondence between heuristic search planning and Graphplan, and briefly survey recent ideas that can reduce the current gap in performance between general heuristic search planners and specialized solvers.
COMPOSER has been successfully applied to learning heuristics for planning and scheduling systems.
Both systems are able to learn heuristics for planning on their own, but both of them have weaknesses.
The purpose of this article is to present a multi-strategy approach to learn heuristics for planning.
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