Artificial Language Evolution on a Dynamical Interaction Network
Swarup, Samarth
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Permalink
https://hdl.handle.net/2142/81793
Description
Title
Artificial Language Evolution on a Dynamical Interaction Network
Author(s)
Swarup, Samarth
Issue Date
2007
Doctoral Committee Chair(s)
Sylvian R. Ray
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Artificial Intelligence
Language
eng
Abstract
"This dissertation studies the impact of a dynamical interaction network on the distributed learning of a common language. We derive a new algorithm for generating realistic complex networks, called Noisy Preferential Attachment (NPA). This is a modification of preferential attachment that unifies it with the quasispecies model of molecular evolution. The growing network can now be seen as a process in which the links in the network are undergoing selection, replication, and mutation. We also demonstrate that by varying the mutation rate over time, we can reproduce features of growing networks in the real world. We then model a population of language learning agents on an interaction topology evolving according to NPA and demonstrate that under certain conditions they can converge very rapidly. However, we also note that they always converge to a maximally simple language. This leads us to introduce a method of relating language to task based on an analogy between the agents' hypothesis space and an information channel. We introduce a new ""language game"" which we call the classification game. We show that the population, through playing the classification game, converges to a representation which is simple, but not too simple, by balancing the pressures for learnability and functionality. We demonstrate that the population can avoid overfitting through this process. The languages that emerge can be either holistic or compositional. We then introduce temporal tasks and show that the same setup, using recurrent neural networks and form-meaning association matrices, can generate languages with strict symbol ordering, which is a rudimentary form of syntax. Finally, we bring together language and topology evolution and show that when the classification game is played on a topology evolving according to NPA, very rapid convergence can be achieved at the expense of a small increase in complexity of the solution. We also compare the convergence rates of several other topologies and show that NPA results in the fastest convergence. Regular and small world topologies show very slow convergence, due to the formation of communities which are locally converged but at odds with other communities."
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