Abstract
There are many real scenarios in which some correlated complex problems have to be addressed by different autonomous learners working in parallel. In such a scenario, the collaboration among the learners can be extremely useful since they can share acquired knowledge so as to reach a reduction in the learning time, an increase in the learning quality, or both of them. Anyway, in some cases, it is not always feasible to collaborate with other learners. This is because the problems to solve are not compatible or they can have dissimilar boundary conditions leading to very different problem solutions. In this paper, we propose an approach to collaborative learning which leverages cellular automata for efficiently solving a set of compatible and sufficiently similar problems. In this direction, the notion of compatibility and similarity between problems is also given and discussed. A case study based on the maze problem will show the effectiveness of the proposed approach.
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Acknowledgment
This work has been partially supported by the COGITO (A COGnItive dynamic sysTem to allOw buildings to learn and adapt) project, funded by the Italian Government (PON ARS01 00836) and by the CNR project “Industrial transition and resilience of post-Covid19 Societies - Sub-project: Energy Efficient Cognitive Buildings (TIRS-EECB)”.
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Cicirelli, F., Greco, E., Guerrieri, A., Spezzano, G., Vinci, A. (2023). Collaborative Learning over Cellular Automata. In: De Stefano, C., Fontanella, F., Vanneschi, L. (eds) Artificial Life and Evolutionary Computation. WIVACE 2022. Communications in Computer and Information Science, vol 1780. Springer, Cham. https://doi.org/10.1007/978-3-031-31183-3_1
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