On-line Relaxing and Oo-line Learning of Eeective Social Laws
نویسنده
چکیده
Automation of planning techniques can potentially save a great deal of design and programming time, and can help robots design plans when human help is not available. Currently, the computational cost of machine planning algorithms prevents widespread use of these systems, and these costs are magniied in multiagent systems. The most expensive and time-consuming aspect of multiagent systems, communication, must be reduced if multiagent planning is to be practical. We propose a method by which agents may reduce both planning and communication costs by planning with stringent social laws, relaxing the laws as needed to nd a solution. We provide and implement a practical model for representing and relaxing social laws, and introduce methods for learning laws in minimal time.
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