By Martin V. Butz (auth.), Pier Luca Lanzi, Wolfgang Stolzmann, Stewart W. Wilson (eds.)
This e-book constitutes the completely refereed post-proceedings of the 4th overseas Workshop on studying Classifier structures, IWLCS 2001, held in San Francisco, CA, united states, in July 2001.
The 12 revised complete papers provided including a different paper on a proper description of ACS have undergone rounds of reviewing and development. the 1st a part of the publication is dedicated to theoretical problems with studying classifier platforms together with the effect of exploration method, self-adaptive classifier structures, and using classifier structures for social simulation. the second one half is dedicated to purposes in quite a few fields comparable to info mining, inventory buying and selling, and gear distributionn networks.
Read Online or Download Advances in Learning Classifier Systems: 4th International Workshop, IWLCS 2001 San Francisco, CA, USA, July 7–8, 2001 Revised Papers PDF
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Extra resources for Advances in Learning Classifier Systems: 4th International Workshop, IWLCS 2001 San Francisco, CA, USA, July 7–8, 2001 Revised Papers
To appear in the proceedings of the 8th IEEE International Conference on Emerging Technologies and Factory Automation, Antibes France. A Representation for Accuracy-Based Assessment of Classifier System Prediction Performance John H. edu Abstract. The increasing use of learning classifier systems (LCS) in data mining necessitates a methodology for improving the assessment of predictive accuracy at both the individual classifier and system levels. A metric, predictive value, is used extensively in clinical diagnosis and medical decision making, and is easily adapted to the LCS to facilitate assessing the ability of individual classifiers used as rules to predict class membership.
In our context, agents are classiﬁer systems. Classiﬁer systems introduced by Holland  are machine learning systems using for their evolution genetic algorithms  . When learning is achieved, such systems know some of the best solutions and when they are integrated to an agent community in order to solve a complex task, exchanging information becomes the main diﬃculty to clear up. The next section of this paper describes classiﬁer systems and multi-agent systems. The third section is devoted to the deﬁnition of the minimal model of communication we proposed.
As an example, here are those individuals for one of the performed experiments: C1 C2 C3 C4 Agent 1 *1 01 : 01 00 0* 11 : 00 10 *0 01 : 01 10 0* 11 : 10 11 C1 C2 C3 C4 Agent 2 10 00 : 01 11 *0 10 : 10 11 01 10 : 10 01 1* 00 : 01 01 A Minimal Model of Communication for a Multi-agent Classiﬁer System 39 Here are the resulting communication possibilities: Word sent 00 11 A1 01 01 10 11 01 00 A2 10 10 Guessed env. Real env. (C2 ) 11 (C3 ) 01 (C1 ,C2 ) 01 (C4 ) 11 (C1 ) 11 (C2 ,C4 ) 00 (C3 ) 10 Agents have learnt the meaning of three words: 00, 01 and 10.