1) Luis Miramontes Hercog, Eguiara y Eguren # 128, Col Viaducto Piedad, 08200 México, D.F. MEXICO
Tel: +525555199861 
lmhercog@yahoo.co.uk
Terence C.Fogarty
School of Computing, South Bank University,103 Borough Rd.     London SE1 0AA, U.K.
Telephone:+44-20-78157008
Fax:+44-20-78157499
fogarttc@lsbu.ac.uk
 
2) Social simulation using a Multi-Agent Model based on Classifier
Systems: The Emergence of Vacillating Behaviour in the ``El Farol'' Bar
Problem
    

abstract:In this paper, MAXCS -- a Multi-Agent system that learns using XCS -- is
used for social modelling on the ``El Farol'' Bar problem. A  cooperative
reward distribution technique is used and compared with the original
selfish ``El Farol'' Bar problem reward distribution technique. When using
selfish reward distribution a vacillating agent emerges which, although
obtaining no reward itself, enables the other agents to benefit
in the best way possible from the system.
Experiments with 10 agents and different parameter settings for the
problem show that MAXCS is always able to solve it. Furthermore, emergent
behaviour can be observed by analysing the actions of the agents and
explained by analysing the rules utilised by the agents. The use of a
learning classifier system has been essential for the detailed analysis of
each agent's decision, as well as for the detection of the emergent
behaviour in the system.
The results are divided into three categories: those obtained using
cooperative reward, those obtained using selfish reward and those which
show emergent behaviour.
Analysis of the values of the rules' performance show that it is the amount of reward received by each XCS combined with its reinforcement mechanism which cause the emergent behaviour.
MAXCS has proved to be a good modelling tool for social simulation, both because of its performance and providing the explanation for the actions.

Keywords: Inductive Reasoning, Multi-Agent Systems, emergent behavior, XCS, Learning
Classifier Systems, ``El Farol'' Bar problem.

5)Social simulation using a Multi-Agent Model based on Classifier
Systems: The Emergence of Vacillating Behaviour in the ``El Farol'' Bar
Problem in Advances in Learning Classifier Systems, Lanzi et al. (eds.) Springer. 

6) The system described in the paper satisfy criteria F and G:
 
Inductive reasoning is the capacity we humans have to cope with ill-defined or ambiguous problems, which complements deductive reasoning for those well defined or stated. The "El Farol" Bar Problem (EFBP) was stated by Arthur[1994] to test how artificial agents (or artificial reasoning systems) could develop inductive reasoning. The multi-agent system that learns using XCS (known as MAXCS) has been able to solve the EFBP, performing better than the original experiments by Arthur, those tested by Wolpert[1998] and those tried by Fogel et al. [1999] with no success, despite the latter's extensive statistical analysis.
These are the reasons that the authors consider that the paper can develope human-competitive results:
(F) The result is equal to or better than a result that was considered an
achievement in its field at the time it was first discovered. As no other approach has shown a system as MAXCS that can arrive to a Nash equilibrium (optimal performance in a game Litmman [2000])
(G) The result solves a problem of indisputable difficulty in its field. The EFBP has been used for ten years as benchmark for inductive reasoning.