Informatics and Applications
2023, Volume 17, Issue 1, pp 66-72
FUZZY RULES BASED METHOD FOR AGENT CONFLICT MANAGEMENT IN HYBRID INTELLIGENT MULTIAGENT SYSTEMS
- S. V. Listopad
- I. A. Kirikov
Abstract
The paper continues research on computer simulation with hybrid intelligent multiagent systems of a teamwork of specialists of various profiles who solve problems at a round table. The agents of such systems are autonomous software entities that imitate the reasoning of real specialists. Modeling of heterogeneous knowledge, goals, and points of view of agents on the problem posed within single intelligent system causes their collision, the emergence of conflicts by analogy with how it happens in simulated teams. Not every conflict between agents is destructive and requires suppression: conflict management in a hybrid intelligent multiagent system as well as in a team involves the identification of a decision-making situation, if necessary, stimulation and subsequent resolution of constructive forms of conflict as well as the prevention of its destructive forms. The paper proposes the method based on fuzzy rules to manage conflicts between agents in hybrid intelligent multiagent systems.
[+] References (21)
- Listopad, S.V., and I. A. Kirikov. 2019. Modelirovanie konfliktov agentov v gibridnykh intellektual'nykh mnogoagentnykh sistemakh [Modeling of agent conflicts in hybrid intelligent multiagent systems]. Sistemy i Sredstva Informatiki - Systems and Means of Informatics 29(3):139-148. doi: 10.14357/08696527190312.
- Maltarich, M. A., M. Kukenberger, G. Reilly, and J. Mathieu. 2018. Conflict in teams: Modeling early and late conflict states and the interactive effects of conflict processes. Group Organ. Manage. 43(1):6-37.
- Jehn, K.A., G. B. Northcraft, and M.A. Neale. 1999. Why differences make a difference: A field study of diversity, conflict, and performance in workgroups. Admin. Sci. Quart. 44:741-763.
- Rahim, M. A. 2002. Toward a theory of managing organizational conflict. Int. J. Confl. Manage. 13(3):206-235.
- Emel'yanov, S. M. 2009. Praktikum po konfliktologii [Tutorial at conflictology]. St. Petersburg: Piter. 384 p.
- Greer, L.L., H.M. Caruso, and K. A. Jehn. 2011. The bigger they are, the harder they fall: Linking team power,
team conflict, and performance. Organ. Behav. Hum. Dec. 116:116-128.
- De Wit, F R. C., L. L. Greer, and K. A. Jehn. 2012. The paradox of intragroup conflict: A meta-analysis. J. Appl. Psychol. 97:360-390.
- Khokhlov, A. S. 2014. Konfliktologiya: Istoriya. Teoriya. Praktika [Conflictology: History. Theory. Practice]. Samara: SF MGPU. 312 p.
- Sidorenkov, A. V. 2008. Konflikt v maloy gruppe: ponyatie, funktsii, vidy i model' [Conflict in small group: Concept, functions, forms and model]. Severo-Kavkazskiy psikho- logicheskiy vestnik [North Caucasian Psychological J.] 6(4):22-28.
- Listopad, S.V., and I. A. Kirikov. 2020. Metod identifikatsii konfliktov agentov v gibridnykh intellektual'nykh mnogoagentnykh sistemakh [Agent conflict identification method in hybrid intelligent multiagent systems]. Sistemy
i Sredstva Informatiki - Systems and Means of Informatics 30(1):56-65. doi: 10.14357/08696527200105.
- Listopad, S.V., and I.A. Kirikov. 2021. Stimulyatsiya konfliktov agentov v gibridnykh intellektual'nykh mnogoagentnykh sistemakh [Stimulation of agent conflicts in hybrid intelligent multiagent systems]. Sistemy i Sredstva Informatiki - Systems and Means of Informatics 31(2):47- 58. doi: 10.14357/08696527210205.
- Listopad, S. V., and I. A. Kirikov. 2022. Razreshenie konfliktov v gibridnykh intellektual'nykh mnogoagentnykh sistemakh [Conflict resolution in hybrid intelligent multi-agent systems]. Informatika i ee Primeneniya - Inform. Appl. 16(1):54-60. doi: 10.14357/19922264220108.
- Kolesnikov, A. V., and S.V. Listopad. 2019. Hybrid intelligent multiagent system of heterogeneous thinking for solving the problem of restoring the distribution power grid after failures. Open Semantic Technologies for Intelligent Systems: Research Papers Collection. Minsk: BGUIR. 133-138.
- Bana e Costa, C. 2001. The use of multi-criteria decision analysis to support the search for less conflicting policy options in a multi-actor context: Case study. J. Multi-Criteria Decision Analysis 10(2):111-125.
- Fasth, T, A. Larsson, L. Ekenberg, and M. Danielson. 2018. Measuring conflicts using cardinal ranking: an application to decision analytic conflict evaluations. Advances Operations Research 2018:8290434. 14 p.
- Leonenkov, A. V. 2005. Nechetkoe modelirovanie v srede MATLAB i fuzzyTECH [Fuzzy modeling in MATLAB and fuzzyTECH]. St. Petersburg: BHV-Peterburg. 736 p.
- Abonyi, J., L. Nagy, and F. Szeifert. 1999. Adaptive fuzzy inference system and its application in modelling and model based control. Chem. Eng. Res. Des. 77(4):281- 290.
- Jang, J.-S. R. 1993. ANFIS: Adaptive-network-based fuzzy inference systems. IEEE Transactions Systems, Man, and Cybernetics 23:665-685.
- Berenji, H. R., and P Khedkar. 1992. Learning and tuning fuzzy logic controllers through reinforcements. IEEE Trans. Neural Networks 3:724-740.
- Feng, J. C., and L.C. Teng. 1998. An online self constructing neural fuzzy inference network and its applications. IEEE T. Fuzzy Syst. 6(1):12-32.
- Kolesnikov, A.V., I. A. Kirikov, and S.V. Listopad. 2014. Gibridnye intellektual'nye sistemy s samoorganizatsiey: koordinatsiya, soglasovannost', spor [Hybrid intelligent sys-tems with self-organization: Coordination, consistency, and dispute]. Moscow: IPI RAN. 189 p.
[+] About this article
Title
FUZZY RULES BASED METHOD FOR AGENT CONFLICT MANAGEMENT IN HYBRID INTELLIGENT MULTIAGENT SYSTEMS
Journal
Informatics and Applications
2023, Volume 17, Issue 1, pp 66-72
Cover Date
2023-04-10
DOI
10.14357/19922264230109
Print ISSN
1992-2264
Publisher
Institute of Informatics Problems, Russian Academy of Sciences
Additional Links
Key words
conflict; hybrid intelligent multiagent system; team of specialists; conflict management
Authors
S. V. Listopad and I. A. Kirikov
Author Affiliations
Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences, 44-2 Vavilov Str., Moscow 119333, Russian Federation
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