Modelling human function learning has been the subject of intense research in cognitive sciences. The topic is relevant in black-box optimization where information about the objective and/or constraints is not available and must be learned through function evaluations. In this paper, we focus on the relation between the behaviour of humans searching for the maximum and the probabilistic model used in Bayesian optimization. As surrogate models of the unknown function, both Gaussian processes and random forest have been considered: the Bayesian learning paradigm is central in the development of active learning approaches balancing exploration/exploitation in uncertain conditions towards effective generalization in large decision spaces. In this paper, we analyse experimentally how Bayesian optimization compares to humans searching for the maximum of an unknown 2D function. A set of controlled experiments with 60 subjects, using both surrogate models, confirm that Bayesian optimization provides a general model to represent individual patterns of active learning in humans.

Candelieri, A., Perego, R., Giordani, I., Ponti, A., Archetti, F. (2020). Modelling human active search in optimizing black-box functions. SOFT COMPUTING, 24(23), 17771-17785 [10.1007/s00500-020-05398-2].

Modelling human active search in optimizing black-box functions

Candelieri A.
Primo
;
Perego R.
Secondo
;
Giordani I.;Ponti A.;Archetti F.
Ultimo
2020

Abstract

Modelling human function learning has been the subject of intense research in cognitive sciences. The topic is relevant in black-box optimization where information about the objective and/or constraints is not available and must be learned through function evaluations. In this paper, we focus on the relation between the behaviour of humans searching for the maximum and the probabilistic model used in Bayesian optimization. As surrogate models of the unknown function, both Gaussian processes and random forest have been considered: the Bayesian learning paradigm is central in the development of active learning approaches balancing exploration/exploitation in uncertain conditions towards effective generalization in large decision spaces. In this paper, we analyse experimentally how Bayesian optimization compares to humans searching for the maximum of an unknown 2D function. A set of controlled experiments with 60 subjects, using both surrogate models, confirm that Bayesian optimization provides a general model to represent individual patterns of active learning in humans.
Articolo in rivista - Articolo scientifico
Active learning; Bayesian optimization; Cognitive models; Search strategy
English
24-ott-2020
2020
24
23
17771
17785
open
Candelieri, A., Perego, R., Giordani, I., Ponti, A., Archetti, F. (2020). Modelling human active search in optimizing black-box functions. SOFT COMPUTING, 24(23), 17771-17785 [10.1007/s00500-020-05398-2].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/298215
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