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Collaborative Decision Support Systems Based on Neuro-Symbolic Artificial Intelligence: Problems and Generalized Conceptual Model

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Abstract

The development of artificial intelligence technologies and the growing complexity of decision-making when managing complex dynamic systems necessitate the joint work of humans and artificial intelligence, including as part of teams of heterogeneous participants (for example, experts and agents operating with artificial intelligence). The paper discusses the requirements for collaborative human-machine decision support systems and the problems that can arise during their creation. The methods of neuro-symbolic artificial intelligence can help resolve some of these problems. An analysis of modern results in the field of ontology-oriented neuro-symbolic artificial intelligence is carried out, primarily intended to explain neural network models using ontologies and symbolic knowledge to improve the efficiency of neural network models. A conceptual model of a collaborative human-machine decision support system based on ontology-oriented neuro-symbolic intelligence is proposed.

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Funding

The work was supported by the Russian Science Foundation, project no. 22-11-00214.

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  1. St. Petersburg Federal Research Center, Russian Academy of Sciences, St. Petersburg, Russia

    A. V. Smirnov, A. V. Ponomarev, N. G. Shilov & T. V. Levashova

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  1. A. V. Smirnov
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Correspondence to A. V. Ponomarev.

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Smirnov, A.V., Ponomarev, A.V., Shilov, N.G. et al. Collaborative Decision Support Systems Based on Neuro-Symbolic Artificial Intelligence: Problems and Generalized Conceptual Model. Sci. Tech. Inf. Proc. 50, 635–645 (2023). https://doi.org/10.3103/S0147688223060151

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