@inproceedings{wang02aamas,
  title={Mutual Online Concept Learning for Multiple Agents},
  author={Jun Wang and Les Gasser},
  year=2002,
  booktitle={Proceedings of the First International Joint Conference on Autonomous Agents and Multi-Agent Systems},
  editor={Cristiano Castelfranchi and W. Lewis Johnson},
  address={Bologna, Italy},
  publisher={ACM Press},
  pages={362-369},
  abstract={
    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.  
  }
}