Imitative learning as a connector of collective brains.
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Here we consider a primordial form of cooperation - imitative learning - that allows an effective exchange of information between agents, which are viewed as the processing units of a social intelligence system or collective brain.In particular, we use agent-based simulations to study the performance of a group of agents in solving a cryptarithmetic problem.If those parameters are chosen far from the optimal setting, however, then imitative learning can impair greatly the performance of the group.
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PubMed Central - PubMed
Affiliation: Instituto de Física de São Carlos, Universidade de São Paulo, São Carlos, Brazil.
ABSTRACT
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The notion that cooperation can aid a group of agents to solve problems more efficiently than if those agents worked in isolation is prevalent in computer science and business circles. Here we consider a primordial form of cooperation - imitative learning - that allows an effective exchange of information between agents, which are viewed as the processing units of a social intelligence system or collective brain. In particular, we use agent-based simulations to study the performance of a group of agents in solving a cryptarithmetic problem. An agent can either perform local random moves to explore the solution space of the problem or imitate a model agent - the best performing agent in its influence network. There is a trade-off between the number of agents N and the imitation probability p, and for the optimal balance between these parameters we observe a thirtyfold diminution in the computational cost to find the solution of the cryptarithmetic problem as compared with the independent search. If those parameters are chosen far from the optimal setting, however, then imitative learning can impair greatly the performance of the group. |
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Mentions: In order to verify the generality of our findings, which were obtained for the specific alphametic problem , we have considered a variety of random cryptarithmetic problems with 10 letters and a unique solution, so that the sizes of their solution spaces are the same as that of the alphametic problem. The comparison between the mean computational costs to solve four such random problems and our alphametic problem is shown in Figure 8 for the fully connected system. The results are qualitatively the same, as expected. The alphametic problem, however, was somewhat easier to solve by the cooperative system than the random problems, perhaps because of the coincidence of the last three letters (“ALD”) in the first and second operands. Interestingly, the independent system () cannot distinguish between the problems but the cooperative system () can, and this distinction is most pronounced when the parameters are set so as to achieve the optimal performance. It is as if the cooperative system had adapted to the specific task posed to it. We expect that our conclusions remain valid, in a qualitative sense of course, for any constraint satisfaction problem characterized by a rugged cost landscape. |
View Article: PubMed Central - PubMed
Affiliation: Instituto de Física de São Carlos, Universidade de São Paulo, São Carlos, Brazil.