An Active Inference approach to the multi-agent lost target search problem

dc.contributor.advisor	Lanillos Pradas, P.L.
dc.contributor.advisor	Keemink, S.W.
dc.contributor.author	Beek, van, Berend
dc.date.issued	2022-03-11
dc.description.abstract	Active Inference is nowadays more often used in a search-related environ- ment, thus this research tests whether it is as e ective as other algorithms such as a No Detection algorithm. This is tested in a 10 by 10 grid, where it tries to nd a target most optimally by minimizing the Variational Free Energy (VFE) and Expected Free Energy (EFE). Bayesian Inference will be applied to solve these minimization problems. The agent uses a momentum- cone in order to choose the most optimal action. Results show a de cit in information gain for the Active Inference compared to the No Detection algorithm. This could be due to an error made in computing the VFE for the agent, as well as an optimization error in the computation for the momentum-cone. Further research can be helpful in nding whether xing these errors will improve the algorithm.
dc.identifier.uri	https://theses.ubn.ru.nl/handle/123456789/15827
dc.language.iso	en
dc.thesis.faculty	Faculteit der Sociale Wetenschappen
dc.thesis.specialisation	specialisations::Faculteit der Sociale Wetenschappen::Artificial Intelligence::Bachelor Artificial Intelligence
dc.thesis.studyprogramme	studyprogrammes::Faculteit der Sociale Wetenschappen::Artificial Intelligence
dc.thesis.type	Bachelor
dc.title	An Active Inference approach to the multi-agent lost target search problem

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