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Decision making for two learning agents acting like human agents : A proof of concept for the application of a Learning Classifier Systems

Jordan, Tobias, De Wilde, Philippe, Buarque de Lima Neto, Fernando (2020) Decision making for two learning agents acting like human agents : A proof of concept for the application of a Learning Classifier Systems. In: 2020 IEEE Congress on Evolutionary Computation (CEC). . IEEE ISBN 978-1-72816-929-3. (doi:10.1109/CEC48606.2020.9185570) (KAR id:81325)

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http://dx.doi.org/10.1109/CEC48606.2020.9185570

Abstract

The paper investigates the suitability of a Learning Classifier System implementation for mimicking human decision making in agent based social simulations incorporating network effects. Model behavior is studied for three distinct scenario settings. We provide proof of concept for the adequacy of LCA to tackle the task at hand. Specifically, it is found that the LCA provides the agents within the simulation model with the ability to learn and to react to environmental changes while accounting for bounded rational decision making and the presence of imperfect information, as well as network effects. Moreover it can be shown that the LCA-agents exhibit a habit like behavioural pattern.

Item Type: Conference or workshop item (Proceeding)
DOI/Identification number: 10.1109/CEC48606.2020.9185570
Uncontrolled keywords: Agent Based Social Simulation, Learning Classifier Systems
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Central Services > Office of the Vice-Chancellor
Depositing User: Philippe De Wilde
Date Deposited: 20 May 2020 10:44 UTC
Last Modified: 16 Feb 2021 14:13 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/81325 (The current URI for this page, for reference purposes)
De Wilde, Philippe: https://orcid.org/0000-0002-4332-1715
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