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Additional resources for Advances in learning classifier systems: 4th international workshop, IWLCS 2001, San Francisco, CA, USA, July 7-8, 2001 : revised papers
W. ), Advances in Learning Classiﬁer Systems, LNAI 1996 pp. 37–51. Berlin Heidelberg: SpringerVerlag. , and Sejnowski, T. J. (1996). Exploration bonus and dual control. Machine Learning, 25 (1), 5–22. , and Sigaud, O. (2001). YACS: Combining dynamic programming with generalization in classiﬁer systems. In Lanzi, P. , and Wilson, S. W. ), Advances in Learning Classiﬁer Systems, LNAI 1996 pp. 52–69. Berlin Heidelberg: Springer-Verlag. Hoﬀmann, J. (1993). Vorhersage und Erkenntnis [Anticipation and cognition].
When agents need much more informations from neighbours, coordination reaches its limits. A second way to solve a problem with a multi-agent system is to use coevolution. In , Escazut and Fogarty used a map of traﬃc signals controllers to successfully test an endosymbiotic coevolution. An other way to solve a problem with a multi-agent system is to communicate. ” In an earlier work, we studied how agents are able to communicate to others the knowledge they acquired. To make them force to share their learning, we have enlarged the elitism concept  applying it to the whole community instead of to a single agent.
2 Applying the Predictive Values to LCS While the predictive values have been used extensively in medical diagnosis and signal detection, they offer a natural approach to assessing the ability of a classifier to predict class membership. Each classifier in the population after training represents a miniature diagnostic test, in that it can be applied to an unknown case to determine its class. Like any diagnostic test, a classifier has an implicit predictive accuracy, which is made explicit in the predictive values.