The complexity of finding an optimal policy for language convergence

Authors:
Kiran Lakkaraju;Les Gasser
Affiliations:
Department of Computer Science, University of Illinois, Urbana-Champaign;Graduate School of Library and Information Science, University of Illinois, Urbana-Champaign
Venue:
SAB'06 Proceedings of the 9th international conference on From Animals to Animats: simulation of Adaptive Behavior
Year:
2006

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Abstract

An important problem for societies of natural and artificial animals is to converge upon a similar language in order to communicate We call this the language convergence problem In this paper we study the complexity of finding the optimal (in terms of time to convergence) algorithm for language convergence We map the language convergence problem to instances of a Decentralized Partially Observable Markov Decision Process to show that the complexity can vary from P-complete to NEXP-complete based on the scenario being studied.