Abstract
We consider how to plan optimally in a testbed, multiagent expedition (MAE), by centralized or distributed computation. As optimal planning in MAE is highly intractable, we investigate speedup through partial evaluation of a subset of plans whereby only the intended effect of a plan is evaluated when certain conditions hold. We apply this technique to centralized planning and demonstrate significant speedup in runtime while maintaining optimality. We investigate the technique in distributed planning and analyze the pitfalls.
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Xiang, Y., Hanshar, F. (2011). Partial Evaluation for Planning in Multiagent Expedition. In: Butz, C., Lingras, P. (eds) Advances in Artificial Intelligence. Canadian AI 2011. Lecture Notes in Computer Science(), vol 6657. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21043-3_50
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DOI: https://doi.org/10.1007/978-3-642-21043-3_50
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21042-6
Online ISBN: 978-3-642-21043-3
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