Abstract
In the context of current information systems, the necessity of processing vast amounts of information enforces a significant paradigm shift. The monolithic data formats are gradually replaced with semantic models enclosing light, layered, and easily replicable structures. Advances represented by blockchain, decentralized data storage and replication along with semantic models open new possibilities to combine Artificial Intelligence (AI) algorithms in multi-agent, cross-organizational systems. By taking a semantic model approach, the communication and coordination between agents can be abstracted by the ontology concepts used to define the data models. This paper explores the feasibility of utilizing ontology-based semantic data models as communication interfaces among multiple AI agents. Specifically, it focuses on the storage, retrieval, and replication part of this process in a decentralized medium.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Admane, L., Benatchba, K., Koudil, M., Siad, L., Maziz, S.: AntPart: an algorithm for the unsupervised classification problem using ants. Appl. Math. Comput. 180(1), 16–28 (2006)
Androulaki, E., et al.: Hyperledger fabric: a distributed operating system for permissioned blockchains. In: Proceedings of the Thirteenth EuroSys Conference, pp. 1–15 (2018)
Benet, J.: IPFS-content addressed, versioned, P2P file system. arXiv preprint arXiv:1407.3561 (2014)
Brayford, D., Vallecorsa, S., Atanasov, A., Baruffa, F., Riviera, W.: Deploying AI frameworks on secure HPC systems with containers. In: 2019 IEEE High Performance Extreme Computing Conference (HPEC), pp. 1–6. IEEE (2019)
Costa, D., Bezemer, C.P., Leitner, P., Andrzejak, A.: What’s wrong with my benchmark results? Studying bad practices in JMH benchmarks. IEEE Trans. Softw. Eng. 47(7), 1452–1467 (2019)
Crosby, M., Pattanayak, P., Verma, S., Kalyanaraman, V., et al.: Blockchain technology: beyond bitcoin. Appl. Innov. 2(6–10), 71 (2016)
Demazeau, Y., Müller, J.P.: Decentralized AI, 2nd edn. Elsevier (1991)
Flores-Mendez, R.A.: Towards a standardization of multi-agent system framework. XRDS: Crossroads ACM Mag. Students 5(4), 18–24 (1999)
Hamada, M.: An environment for simulating multi-agents based on ants behavior. In: Proceedings of the 7th Conference on 7th WSEAS International Conference on Systems Theory and Scientific Computation, vol. 7, pp. 294–299. Citeseer (2007)
Harris, J.D., Waggoner, B.: Decentralized and collaborative AI on blockchain. In: 2019 IEEE International Conference on Blockchain (Blockchain), pp. 368–375. IEEE (2019)
Höb, M., Kranzlmüller, D.: Enabling EASEY deployment of containerized applications for future HPC systems. In: Krzhizhanovskaya, V.V., et al. (eds.) ICCS 2020. LNCS, vol. 12137, pp. 206–219. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-50371-0_15
John, M.M., Holmström Olsson, H., Bosch, J.: Architecting AI deployment: a systematic review of state-of-the-art and state-of-practice literature. In: Klotins, E., Wnuk, K. (eds.) ICSOB 2020. LNBIP, vol. 407, pp. 14–29. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-67292-8_2
Kogure, J., Kamakura, K., Shima, T., Kubo, T.: Blockchain technology for next generation ICT. Fujitsu Sci. Tech. J 53(5), 56–61 (2017)
Kurtulmus, A.B., Daniel, K.: Trustless machine learning contracts; evaluating and exchanging machine learning models on the Ethereum blockchain. arXiv preprint arXiv:1802.10185 (2018)
Kwon, J., Buchman, E.: Cosmos whitepaper (2019)
Liu, D., Zhang, Y., Jia, D., Zhang, Q., Zhao, X., Rong, H.: Toward secure distributed data storage with error locating in blockchain enabled edge computing. Comput. Stan. Interfaces 79, 103560 (2022)
Marathe, A., Narayanan, K., Gupta, A., Manoj, P.: DInEMMo: decentralized incentivization for enterprise marketplace models. In: 2018 IEEE 25th International Conference on High Performance Computing Workshops (HiPCW), pp. 95–100. IEEE (2018)
Pipattanasomporn, M., Feroze, H., Rahman, S.: Multi-agent systems in a distributed smart grid: design and implementation. In: 2009 IEEE/PES Power Systems Conference and Exposition, pp. 1–8. IEEE (2009)
Ren, W., Beard, R.W.: Distributed Consensus in Multi-vehicle Cooperative Control, vol. 27. Springer, Heidelberg (2008)
Rizal Batubara, F., Ubacht, J., Janssen, M.: Unraveling transparency and accountability in blockchain. In: Proceedings of the 20th Annual International Conference on Digital Government Research, pp. 204–213 (2019)
Sun, Y.G., Wang, L.: Consensus problems in networks of agents with double-integrator dynamics and time-varying delays. Int. J. Control 82(10), 1937–1945 (2009)
Tara, A., Butean, A., Zamfirescu, C., Learney, R.: An ontology model for interoperability and multi-organization data exchange. In: Silhavy, R. (ed.) CSOC 2020. AISC, vol. 1225, pp. 284–296. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-51971-1_23
Tran, Q.N.N., Low, G.: MOBMAS: a methodology for ontology-based multi-agent systems development. Inf. Softw. 50(7–8), 697–722 (2008)
Vicsek, T., Czirók, A., Ben-Jacob, E., Cohen, I.: Novel type of phase transition in a system of self-driven particles. Phys. Rev. Lett. 75(6), 1226 (1995)
Wang, L., Xiao, F.: Finite-time consensus problems for networks of dynamic agents. IEEE Trans. Autom. Control 55(4), 950–955 (2010)
Zheng, Y., Zhu, Y., Wang, L.: Consensus of heterogeneous multi-agent systems. IET Control Theor. Appl. 5(16), 1881–1888 (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Tara, A., Taban, N., Turesson, H. (2022). Performance Analysis of an Ontology Model Enabling Interoperability of Artificial Intelligence Agents. In: Silhavy, R. (eds) Artificial Intelligence Trends in Systems. CSOC 2022. Lecture Notes in Networks and Systems, vol 502. Springer, Cham. https://doi.org/10.1007/978-3-031-09076-9_35
Download citation
DOI: https://doi.org/10.1007/978-3-031-09076-9_35
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-09075-2
Online ISBN: 978-3-031-09076-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)