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A Comprehensive Analysis on Multi Agent Decision Making Systems

Affiliations

  • Department of CSE,Mannadipet Commune, Madagadipet, Puducherry – 605107, Pondicherry, India
  • Department of CSE, Mannadipet Commune, Madagadipet, Puducherry – 605107, Pondicherry, India
  • Department of IT, SMVEC, Mannadipet Commune, Madagadipet, Puducherry – 605107, Pondicherry, India
  • Department of CSE, Mazoon University College, Muscat
  • Department of CSE, Pondicherry University, Kalapet, Puducherry – 605014, Pondicherry, India

Abstract


Background/Objectives: To analyze and find the decision making systems in multi agent capable of solving complex problems. Method/Statistical Analysis: Multi agent systems are the collection of many individual intelligent systems. Decision making is important because a multi agent system consists of many agents that may be homogeneous or heterogeneous. In heterogeneous network agent must trust another agents in the network for sharing of messages. Hence an agent must be capable to make decision towards trusting of neighbor agents. Decision making technique plays an important role to make decision in such a situation. This is one scenario. Multiple scenarios are discussed in this paper towards the decision making capability of multi agent systems. Findings: In this research, the study of multi agent system, problem solving and decision making are considered as the two important concepts. Multi agent systems are capable of interacting with different environments like virtual environment or real time environment. In this paper a survey is done towards the decision making capability of multi agent systems. Applications/Improvements: The results from this work serve as the motivation to apply the future implementation of multi agent decision making in the complex problem solving.

Keywords

Multi Agent Systems, Decision Making, Environment, Agents, Trust, Problem Solving

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References


  • Fan ZP, Feng B. A multiple attributes decision making method using individual and collaborative attribute data in a fuzzy environment. Information Sciences. 2009; 179(20):3603–18.
  • Antucheviciene J, Zavadskas EK, Zakarevicius A. Multiple criteria construction management decisions considering relations between criteria, Technological and Economic Development of Economy. 2010; 16(1):109–25.
  • Xu Z. Choquet integrals of weighted intuitionistic fuzzy information. Information Sciences. 2010; 180(5):726–36.
  • Geetha PV, Lukshmi RA, Venkatesan P. Tuberculosis Disease Classification Using Genetic-neuro Expert System. Indian Journal of Science and Technology. 2014 Jan; 7(4):421–25.
  • Yager RR. Prioritized aggregation operators. International Journal of Approximate Reasoning. 2008; 48(1):263–74.
  • Yager RR. Prioritized OWA aggregation, Fuzzy Optimization and Decision Making. 2009; 8(3):245–62.
  • Lahdelma R, Makkonen S, Salminen P. Modelling dependent uncertainties in stochastic multicriteria acceptability analysis. Coimbra, Portugal: Antunes CH, Figuera J, Climaco J. (eds.) Multiple Criteria Decision Aiding. 2004; p. 1–18.
  • Figueira J, Greco S, Ehrgott M. Springer Science Business Media Pvt. Ltd: USA: Multiple Criteria Decision Analysis: State of the art Surveys. 2005.
  • Diestel R. Springer-Verlag, Berlin: Heidelberg: Graph Theory, 4th edn. 2006; p. 173.
  • Mordeson JN, Nair PS. Springer-Verlag: Heidelberg: Fuzzy Graphs and Fuzzy Hypergraphs. 2012.
  • Basheer GS, Ahmad MS, Tang AYC, Graf S. Certainty, trust and evidence: Towards an integrative model of confidence in multi-agent systems. Computers in Human Behavior. 2014; 45:307–15.
  • Amudhavel J, et al. An robust recursive ant colony optimization strategy in VANET for accident avoidance (RACO-VANET). International Conference on Circuit, Power and Computing Technologies (ICCPCT); Nagercoil. 2015. p. 1–6.
  • Amudhavel J, et al. A krill herd optimization based fault tolerance strategy in MANETs for dynamic mobility. International Conference on Circuit, Power and Computing Technologies (ICCPCT); Nagercoil. 2015. p. 1–7.
  • Amudhavel J, Prabu U, Dhavachelvan P, Moganarangan N, Ravishankar V, Baskaran R. Non-homogeneous hidden Markov model approach for load balancing in web server farms (NH2M2-WSF). Global Conference on Communication Technologies; Thuckalay. 2015. p. 843–5.
  • Hubbard DW. Wiley: New Jersey: How to measure anything: Finding the value of intangibles in business, 2nd edn. 2014.
  • Imam AT. De Montfort University: Leicester, United Kingdom: A novel approach for handling complex ambiguity for software engineering of data mining models. 2010.
  • Diestel R. Springer-Verlag, Berlin: Heidelberg: Graph Theory. 2006.
  • Mordeson JN, Nair PS. Springer-Verlag: Heidelberg: Fuzzy Graphs and Fuzzy Hypergraphs. 2000.
  • Basheer GS, Ahmad MS, Tang AYC, Graf S. Certainty, trust and evidence: Towards an integrative model of confidence in multi-agent systems. Computers in Human Behavior. 2015; 45(2):307–15.
  • Yu X, Xu Z. Graph-based multi-agent decision making. International Journal of Approximate Reasoning. 2012; 53(4):502–12.
  • Bhattacharya P. Some remarks on fuzzy graphs. Pattern Recognition Letters. 1987;6(5):297–302.
  • Fishburn PC, Lavalle IH. MCDA: theory, practice and the future. Journal of Multi-Criteria Decision Analysis. 1999; 8(1):1–2.
  • Hubbard DW. Wiley: Chichester, United Kingdom: How to measure anything: Finding the value of intangibles in business, 2nd edn. 2014.
  • Imam AT. Leicester University: United kingdom: A novel approach for handling complex ambiguity for software engineering of data mining models. 2010.
  • Yu W, Zheng WX, Chen G, Ren W, Cao J. Secondorder consensus in multi-agent dynamical systems with sampled position data. Automatica. 2011; 47(11):1496– 503.
  • Cheng TM, Savkin AV. Decentralized control of multiagent systems for swarming with a given geometric pattern. Computers & Mathematics with Applications. 2011; 61(4):731–44.
  • Li Z, Liu X, Lin P, Ren W. Consensus of linear multi-agent systems with reduced-order observer-based protocols. Systems & Control Letters. 2011; 60(7):510–16.
  • Chen Y, Lu J, Han F, Yu X. On the cluster consensus of discretetime multi-agent systems. Systems & Control Letters. 2011; 60(7):517–23.
  • Gutierrez C, Garcia-Magarino I, Fuentes-Fernandez R. Detection of undesirable communication patterns in multi-agent systems. Engineering Applications of Artificial Intelligence. 2011; 24(1):103–16.
  • Lin P, Qin K, Li Z, Ren W. Collective rotating motions of second-order multi-agent systems in three-dimensional space, Systems & Control Letters. 2011; 60(6):365–72.
  • Qin J, Gao H, Zheng WX. Second-order consensus for multi-agent systems with switching topology and communication delay. Systems & Control Letters. 2011; 60(6):390-97.
  • Purvis MK, Long ALS. Affinities between multi-agent systems and service-dominant logic: Interactionist implications for business marketing practice. Industrial Marketing Management. 2011; 40(2):248–54.
  • Liu S, Xie L, Zhang H. Distributed consensus for multiagent systems with delays and noises in transmission channels. Automatica. 2011; 47(5):920–34.
  • van Pruissen O, van der Togt A, Werkman Ewoud. Energy Efficiency Comparison of a Centralized and a Multi-agent Market Based Heating System in a Field Test. Energy Procedia. 2014; 62:170–79.
  • Foderaro G, Ferrari S, Wettergren TA. Distributed optimal control for multi-agent trajectory optimization. Automatica. 2014; 50(1):149–54.
  • Amirgholipour SK, Sharifi AM, Alirezanejad M, Hasiri A, Zadeh FM. Multi Agent Electronic Cargo Terminal System. Indian Journal of Science and Technology. 2014 Jan; 7(4):430–38.

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