International Conference on Computational Science, ICCS 2011 Farmer-Pest Problem: A Multidimensional Problem Domain for Comparison of Agent Learning Methods | Zendy

Bartłomiej Śnieżyński | Zendy; Jacek Dajda | Zendy; Marcin Mlostek | Zendy; Michał Pulchny | Zendy

Open Access

International Conference on Computational Science, ICCS 2011 Farmer-Pest Problem: A Multidimensional Problem Domain for Comparison of Agent Learning Methods

Author(s) -

Bartłomiej Śnieżyński,

Jacek Dajda,

Marcin Mlostek,

Michał Pulchny

Publication year - 2011

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2011.04.205

Subject(s) - computer science , reinforcement learning , artificial intelligence , benchmarking , domain (mathematical analysis) , machine learning , naive bayes classifier , bayes' theorem , simple (philosophy) , bayesian probability , mathematics , mathematical analysis , marketing , support vector machine , business , philosophy , epistemology

Learning is often utilized by multi-agent systems which can deal with complex problems by means of their decentralized approach. With a number of learning methods available, a need for their comparison arises. This paper presents initial comparison results for selected algorithms (SARSA, Naïve Bayes, C4.5 and Ripper), which are obtained based on the new multi-dimensional Farmer-Pest problem domain, which is suitable for benchmarking learning algorithms. The results show that supervised learning algorithms can be used to generate agent strategy. It appears that for simple environment reinforcement learning algorithm together with Naïve Bayes learning gives best results. Although, in a difficult environment, C4.5 and Ripper are the best

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