Machine Learning in Multi-Agent Systems using Associative Arrays - Publikacja - MOST Wiedzy


Machine Learning in Multi-Agent Systems using Associative Arrays


In this paper, a new machine learning algorithm for multi-agent systems is introduced. The algorithm is based on associative arrays, thus it becomes less complex and more efficient substitute of artificial neural networks and Bayesian networks, which is confirmed by performance measurements. Implementation of machine learning algorithm in multi-agent system for aided design of selected control systems allowed to improve the performance by reducing time of processing requests, that were previously acknowledged and stored in learning module. This article contains an insight into different machine learning algorithms and includes the classification of learning techniques regarding the criteria depicted by multi-agent systems. The publication is also an attempt to provide the answer for a question posted by Shoham, Powers and Grenager: “If multi-agent learning is the answer, what is the question?”


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Informacje szczegółowe

Publikacja w czasopiśmie
artykuł w czasopiśmie wyróżnionym w JCR
Opublikowano w:
PARALLEL COMPUTING nr 75, strony 88 - 99,
ISSN: 0167-8191
Rok wydania:
Opis bibliograficzny:
Spychalski P., Arendt R.: Machine Learning in Multi-Agent Systems using Associative Arrays// PARALLEL COMPUTING. -Vol. 75, (2018), s.88-99
Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.1016/j.parco.2018.03.006
Bibliografia: test
  1. Smola A., Vishwanathan S.V.N.: "Introduction to Machine Learning", Cambridge University Press (2008)
  2. Tuyls K., Weiss G.: "Multiagent Learning: Basics, Challenges, and Prospects", AI Magazine 33(3) (2012) 43-52 otwiera się w nowej karcie
  3. Silver D., Huang A., Maddison C.J., Guez A., Sifre L. et al.: "Mastering the Game of Go with Deep Neural Networks and Tree Search", Nature 529 (2016) 484-489 otwiera się w nowej karcie
  4. Kotsiantis S.B.: "Supervised Machine Learning: A Review of Classification Techniques", Informatica 31 (2007) 249-268
  5. Sniezynski B.: "Agent-based Adaptation System for Service-oriented Architectures Using Supervised Learning", Procedia Computer Science 29 (2014) 1057-1067
  6. Shoham Y., Powers R., Grenager T.: "If Multi-agent Learning is the Answer, What is the Question?", Artificial Intelligence 171(7) (2007) 365-377 otwiera się w nowej karcie
  7. Weiss G.: "Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence", MIT Press (1999)
  8. Tan M.: "Multi-agent Reinforcement Learning: Independent vs. Cooperative Agents", Proceedings of the 5th International Conference on Machine Learning (1993) 330-337 otwiera się w nowej karcie
  9. Panait L., Luke S.: "Cooperative Multi-Agent Learning: The State of the Art", Autonomous Agents and Multi-agent Systems 11(3) (2005) 387-434 otwiera się w nowej karcie
  10. Busoniu L., Babuška R., De Schutter B.: "A Comprehensive Survey of Multi-agent Reinforcement Learning", IEEE Transactions on Systems, Man, and Cybernetics 38(2) (2008) 156-172 otwiera się w nowej karcie
  11. Yang E., Gu D.: "Multi-robot Systems with Agent-based Reinforcement Learning: Evolution, Opportunities and Challenges", International Journal on Modelling, Identification and Control 6(4) (2009) 271-286
  12. Khalil K.M., Abdel-Aziz M., Nazmy T., Salem A-B.: "MLIMAS: A Framework for Machine Learning in Interactive Multi-agent Systems", Procedia Computer Science 65 (2015) 827-835 otwiera się w nowej karcie
  13. Xu J., Tekin C., Zhang S., Shaar M.: "Distributed Multi-agent Online Learning Based on Global Feedback", IEEE Transactions on Signal Processing 63(9) (2015) 2225-2238 otwiera się w nowej karcie
  14. Basheer I.A., Hajmeer M.: "Artificial Neural Networks: Fundamentals, Computing, Design, and Application", Journal of Microbiological Methods 43(1) (2000) 3-31 otwiera się w nowej karcie
  15. Zhao L., Jia Y.: "Neural Network-based Adaptive Consensus Tracking Control for Multi-agent Systems under Actuator Faults", International Journal of Systems Science 47(8) (2014) 1931-1942 otwiera się w nowej karcie
  16. Wang D., Ma H., Liu D.: "Distributed Control Algorithm for Bipartite Consensus of the Nonlinear Time- delayed Multi-agent Systems with Neural Networks", Neurocomputing 174 (2016) 928-936 otwiera się w nowej karcie
  17. Grossman D., Domingos P.: "Learning Bayesian Network Classifiers by Maximizing Conditional Likelihood", Proceedings of the 21st International Conference on Machine Learning (2004) 46-54 otwiera się w nowej karcie
  18. Pearl J., Stuart R.: "Bayesian Networks", UCLA Cognitive Systems Laboratory (2000) otwiera się w nowej karcie
  19. Djuric P.M., Wang Y.: "Distributed Bayesian Learning in Multi-agent Systems", IEEE Signal Processing Magazine 29(2) (2012) 65-76 otwiera się w nowej karcie
  20. Stone P., Veloso M.: "Multi-agent Systems: A Survey from a Machine Learning Perspective", Autonomous Robotics 8(3) (2000) 345-383 otwiera się w nowej karcie
  21. Arendt R., Spychalski P.: "An Application of Multi-Agent System for Ship's Power Systems Design", Proceedings of the 20th International Conference Transport Means (2016) 380-384 otwiera się w nowej karcie
  22. Arendt R., Kopczynski A., Spychalski P.: "Centralized and Distributed Structures of Intelligent Systems for Aided Design of Ship Automation", Proceedings of the 38th International Conference on Information Systems Architecture and Technology (2017) otwiera się w nowej karcie
  23. Mrozek, D., Malysiak-Mrozek B., Waligora I.: "UMAP -A Universal Multi-Agent Platform for .NET Developers", Beyond Databases, Architectures, and Structures: Communications in Computer and Information Science 424 (2014) 300-311 otwiera się w nowej karcie
  24. Andrews R., Diederich J., Tickle A.B.: "Survey and Critique of Techniques for Extracting Rules from Trained Artificial Neural Networks", Knowledge-Based Systems 8(6) (1995) 373-389 otwiera się w nowej karcie
  25. Maymounkov P., Mazieres D.: "Kademlia: A Peer-to-Peer Information System Based on the XOR Metric" Proceedings of the 1st International Workshop on Peer-to-Peer Systems (2002) 53-65 otwiera się w nowej karcie
  26. McCaffrey J.: "Test Run -Naive Bayes Classification with C#", MSDN Magazine 28(6) (2013)
  27. Heaton J.: "Programming Neural Networks with Encog3 in C#", Heaton Research (2011) otwiera się w nowej karcie
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