| Abstract: | It is widely believed that a group of cooperating agents engaged in problem solving can solve a task faster than either a single agent or the same group of agents working in isolation from each other. Nevertheless, little is known about the quantitative improvements that result from cooperation. A number of experimental results are presented on constraint satisfaction that both test the predictions of a theory of cooperative problem solving and assess the value of cooperation for this class of problems. These experiments suggest an alternative methodology to existing techniques for solving constraint satisfaction problems in computer science and distributed artificial intelligence. |
