The optimal explorer hypothesis and Its formulation as a combinatorial optimization problem
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Autores: Mikel Malagón Azpeitia, Jon Vadillo Jueguen, Josu Ceberio Uribe
, José Antonio Lozano Alonso
- Localización: Actas del XVI Congreso Español de Metaheurísticas, Algoritmos Evolutivos y Bioinspirados: (MAEB 2025) 28-30 de mayo, Donostia/San Sebastián / coord. por Leticia Hernando Rodríguez , Josu Ceberio Uribe , Jon Vadillo Jueguen, 2025, ISBN 978-84-1319-656-5, págs. 300-306
- Idioma: inglés
- Enlaces
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Resumen
This research project explores the hypothesis that, given a bounded number of steps in an environment, agents that most efficiently optimize their model of the environment are more likely to induce emergent intelligent behavior in a reward- free scenario. We refer to this as the optimal explorer hypothesis. The project aims to formalize and analyze this hypothesis, investigating its theoretical impli- cations and connections to related areas such as open-ended learning and active inference. Building on this foundation, we will develop a practical implementation of an approximate “optimal explorer” agent by formulating it as a combinatorial optimization problem and leveraging established methods from the field. Finally, we will conduct extensive experiments to evaluate whether the proposed agent induces emergent behaviors in diverse and challenging environments.
