Language Evolution and Computation Bibliography

To create multi-agent systems that are both adaptive and open, agents must collectively learn to generate their own concepts, interpretations, and even languages actively in an online fashion. The issue is that there is no pre- existing global concept to be learned; instead, agents are in effect collectively designing a concept that is evolving as they exchange information. This paper presents a framework of {\it mutual online concept learning} (MOCL) in a shared world. MOCL extends the classical online concept learning from single-agent to multi-agent setting. Based on the Perceptron algorithm, we design a specific MOCL algorithm, called the {\it mutual perceptron convergence algorithm}, which can converge within a finite number of mistakes under some conditions. Analysis of the convergence conditions shows that the possibility of convergence depends on the number of participating agents and the quality of the instances they produce. Finally, we point out applications of MOCL and the convergence algorithm to the formation of linguistic knowledge in the form of dynamically generated shared vocabulary and grammar structure for multiple agents.