Autonomous Decision Systems Using Multi-Agent Artificial Intelligence

  • Authors

    • Dr. Ibrahim Lawal Federal University of Technology Minna, Niger state. Author
    • S. Moses Stephen Raj Federal University of Technology Minna, Niger state. Author
    • K. Ansari Federal University of Technology Minna, Niger state. Author

    DOI:

    https://doi.org/10.67228/30713315/IJAIDT-2024PI8R4S9

    Published 04-04-2024

  • Multi-Agent Systems, Autonomous Decision Making, Distributed Artificial Intelligence, BDI Architecture, Reinforcement Learning, Coordination, Distributed Optimization

    Issue

    Vol. 7 No. 1 (2024)

    Section

    Articles

    How to Cite

    [1]
    I. Lawal, S. Moses Stephen Raj, and K. Ansari, “Autonomous Decision Systems Using Multi-Agent Artificial Intelligence”, IJAIDT, vol. 7, no. 1, pp. 01–14, Apr. 2024, doi: 10.67228/30713315/IJAIDT-2024PI8R4S9.

  • Abstract

    Autonomous decision systems have become essential in modern intelligent computing, driven by advances in AI and distributed computing. This paper studies multi-agent AI (MAAI) systems, focusing on their theoretical foundations, design methods, and performance before 2018. Multi-agent systems enable decentralized, scalable, and adaptive decision-making by distributing intelligence among interacting agents capable of perception, reasoning, and action. The paper highlights how agent-based models integrate with decision frameworks, where cooperation, coordination, and competition lead to intelligent behavior. It reviews approaches such as rule-based systems, utility models, and reinforcement learning in multi-agent contexts, while addressing challenges like scalability, communication overhead, conflict resolution, and uncertainty. It also examines key developments in distributed AI, including contract net protocols, distributed constraint satisfaction, and game-theoretic methods, along with applications in robotics, smart grids, traffic, and defense. Finally, it discusses system evaluation metrics like efficiency, convergence, and fault tolerance, offering a consolidated reference and identifying future research directions.

  • References

    [1] Wooldridge, M. (2009). An Introduction to MultiAgent Systems. John Wiley & Sons.

    [2] Russell, S., & Norvig, P. (2021). Artificial Intelligence: A Modern Approach (4th ed.). Pearson.

    [3] Durfee, E. H. (1988). Coordination of Distributed Problem Solvers. Springer.

    [4] Smith, R. G. (1980). “The Contract Net Protocol: High-Level Communication and Control in a Distributed Problem Solver.” IEEE Transactions on Computers, 29(12), 1104–1113.

    [5] Sandholm, T. (1999). “Distributed Rational Decision Making.” In Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence. MIT Press.

    [6] Yokoo, M., & Hirayama, K. (2000). “Algorithms for Distributed Constraint Satisfaction: A Review.” Autonomous Agents and Multi-Agent Systems, 3(2), 185–207.

    [7] Modi, P. J., Shen, W.-M., Tambe, M., & Yokoo, M. (2005). “ADOPT: Asynchronous Distributed Constraint Optimization with Quality Guarantees.” Artificial Intelligence, 161(1–2), 149–180.

    [8] Shoham, Y., & Leyton-Brown, K. (2009). Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations. Cambridge University Press.

    [9] Osborne, M. J., & Rubinstein, A. (1994). A Course in Game Theory. MIT Press.

    [10] Busoniu, L., Babuska, R., & De Schutter, B. (2008). “A Comprehensive Survey of Multiagent Reinforcement Learning.” IEEE Transactions on Systems, Man, and Cybernetics, 38(2), 156–172.

    [11] Panait, L., & Luke, S. (2005). “Cooperative Multi-Agent Learning: The State of the Art.” Autonomous Agents and Multi-Agent Systems, 11(3), 387–434.

    [12] Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press.

    [13] Stone, P., & Veloso, M. (2000). “Multiagent Systems: A Survey from a Machine Learning Perspective.” Autonomous Robots, 8(3), 345–383.

    [14] Jennings, N. R. (2001). “An Agent-Based Approach for Building Complex Software Systems.” Communications of the ACM, 44(4), 35–41.

    [15] Weiss, G. (Ed.). (2013). Multiagent Systems (2nd ed.). MIT Press.

    [16] Gajula, S. (2023). A review of anomaly identification in finance frauds using machine learning system. International Journal of Current Engineering and Technology, 13(6), 568–575. https://ijcet.evegenis.org/index.php/ijcet/article/view/820

  • Downloads