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Solving Distributed CSPs Probabilistically

Bistarelli S., Gosti G.

Fundamenta Informaticae

2011

Vol. 105, nr 1/2

57-78

Constraint solving problems (CSPs) are the formalization of a large range of problems that emerge fromcomputer science. The solving methodology described here is based on the naming game. The two main features that distinguish this methodology from most distributed constraint solving problem (DCSPs) methods are: the system can react to small instance changes, and it does not require pre-agreed agent/variable ordering. The naming game was introduced to represent N agents that have to bootstrap an agreement on a name to give to an object. The agents do not have a hierarchy, and use a minimal protocol. Still they converge to a consistent state by using a distributed strategy. For this reason, the naming game can be used to untangle DCSPs. It was shown that a distributed system of uniform finite state machines does not solve the ring ordering problem in all the algorithm executions. Our algorithm is a distributed uniform system of agents able to perform random decisions when presented with equivalent alternatives. We show that this algorithm solves the ring ordering problem with a probability one.

Naming Game and Computational Modelling of Language Evolution

Lipowska D.

Computational Methods in Science and Technology

2011

Vol. 17, No. 1-2

41-51

Computational modelling with multi-agent systems has become an important technique in studying language evolution. We present a brief introduction into this rapidly developing field, as well as our own contributions, which include an analysis of the evolutionary naming game model. In this model, communicating agents, which try to establish a common vocabulary, are equipped with an evolutionarily selected learning ability. Such a coupling of biological and linguistic ingredients results in an abrupt transition: upon a small change of the model control parameter, a poorly communicating group of agents with small learning abilities transforms into almost perfectly communicating group of agents with large learning abilities. Genetic imprinting of the learning abilities progresses through the Baldwin effect: initially linguistically unskilled agents learn a language, which creates a niche where there is an evolutionary pressure for the increase of learning ability. Under the assumption that communication intensity increases continuously with finite speed, the transition is split into several transition-like changes. It shows that the speed of cultural changes, that sets an additional characteristic time scale, might be yet another factor affecting the evolution of language. In our opinion, this model shows that linguistic and biological processes have a strong influence on each other and this influence certainly has contributed to an explosive development of our species.