Abstract
The work presents an approach to the design of intellectual decision-making systems of autonomous robots, which consists in the synthesis of cognitive architectures and multi-agent systems. As a cognitive architecture, architecture is used, the nodes of which are multi-agent systems. This approach is based on the computing abstraction of the processes of multi-agent exchange of information between the neurons of the brain, in which individual neurons of the brain are considered as rational software agents that perform cooperative interaction with each other in order to maximize their local target functions. A simulation model of a decision-making system has been developed. The software implementation of the process of interaction between agents is presented within the framework of a multi-agent neurocognitive decision-making model. The algorithm and a program for modelling multi-agent neurocognitive architectures have been developed. Such architectures, through the collaboration of agents, can provide the search for suboptimal solutions under conditions of partial uncertainty. The developed software can be used to simulate the decision-making process for performing tasks in a real environment. In particular, the presented formalism of multi-agent neurocognitive artificial intelligence systems is experimentally used to ensure goal-directed behavior of autonomous mobile robots.
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Nagoev, Z., Bzhikhatlov, K., Pshenokova, I., Unagasov, A. (2024). Algorithms and Software for Simulation of Intelligent Systems of Autonomous Robots Based on Multi-agent Neurocognitive Architectures. In: Ronzhin, A., Savage, J., Meshcheryakov, R. (eds) Interactive Collaborative Robotics. ICR 2024. Lecture Notes in Computer Science(), vol 14898. Springer, Cham. https://doi.org/10.1007/978-3-031-71360-6_29
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