Machine Learning in Multi-Agent Systems using Associative Arrays

Abstract 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?”

[1]  David Mazières,et al.  Kademlia: A Peer-to-Peer Information System Based on the XOR Metric , 2002, IPTPS.

[2]  Joachim Diederich,et al.  Survey and critique of techniques for extracting rules from trained artificial neural networks , 1995, Knowl. Based Syst..

[3]  Yingmin Jia,et al.  Neural network-based adaptive consensus tracking control for multi-agent systems under actuator faults , 2016, Int. J. Syst. Sci..

[4]  Dariusz Mrozek,et al.  UMAP - A Universal Multi-Agent Platform for .NET Developers , 2014, BDAS.

[5]  Petar M. Djuric,et al.  Distributed Bayesian learning in multiagent systems: Improving our understanding of its capabilities and limitations , 2012, IEEE Signal Processing Magazine.

[6]  Gerhard Weiss,et al.  Multiagent systems: a modern approach to distributed artificial intelligence , 1999 .

[7]  Sotiris B. Kotsiantis,et al.  Supervised Machine Learning: A Review of Classification Techniques , 2007, Informatica.

[8]  Ryszard Arendt,et al.  Centralized and Distributed Structures of Intelligent Systems for Aided Design of Ship Automation , 2017, ISAT.

[9]  Derong Liu,et al.  Distributed control algorithm for bipartite consensus of the nonlinear time-delayed multi-agent systems with neural networks , 2016, Neurocomputing.

[10]  Bart De Schutter,et al.  A Comprehensive Survey of Multiagent Reinforcement Learning , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[11]  Mihaela van der Schaar,et al.  Distributed Multi-Agent Online Learning Based on Global Feedback , 2015, IEEE Transactions on Signal Processing.

[12]  Demis Hassabis,et al.  Mastering the game of Go with deep neural networks and tree search , 2016, Nature.

[13]  Mohamed Abdel-Aziz,et al.  MLIMAS: A Framework for Machine Learning in Interactive Multi-agent Systems , 2015 .

[14]  Ryszard Arendt,et al.  An application of multi-agent system for ship’s power systems design , 2016 .

[15]  Yunlong Wang.  Distributed Bayesian Learning in Multi-agent Systems , 2015 .

[16]  Ming Tan,et al.  Multi-Agent Reinforcement Learning: Independent versus Cooperative Agents , 1997, ICML.

[17]  Bartlomiej Sniezynski,et al.  Agent-based Adaptation System for Service-oriented Architectures Using Supervised Learning , 2014, ICCS.

[18]  Gerhard Weiss,et al.  Multiagent Learning: Basics, Challenges, and Prospects , 2012, AI Mag..

[19]  Pedro M. Domingos,et al.  Learning Bayesian network classifiers by maximizing conditional likelihood , 2004, ICML.

[20]  Yoav Shoham,et al.  If multi-agent learning is the answer, what is the question? , 2007, Artif. Intell..

[21]  Erfu Yang,et al.  Multi-robot systems with agent-based reinforcement learning: evolution, opportunities and challenges , 2009, Int. J. Model. Identif. Control..

[22]  Judea Pearl,et al.  Bayesian Networks , 1998, Encyclopedia of Social Network Analysis and Mining. 2nd Ed..

[23]  Sean Luke,et al.  Cooperative Multi-Agent Learning: The State of the Art , 2005, Autonomous Agents and Multi-Agent Systems.

[24]  Ethem Alpaydin,et al.  Introduction to machine learning , 2004, Adaptive computation and machine learning.

[25]  I A Basheer,et al.  Artificial neural networks: fundamentals, computing, design, and application. , 2000, Journal of microbiological methods.