Heuristic Search of Heuristics

Angelo Pirrone; Peter Lane; Laura Bartlett; Noman Javed; Fernand Gobet

doi:10.1007/978-3-031-47994-6_36

Heuristic Search of Heuristics

Angelo Pirrone, Peter Lane, Laura Bartlett, Noman Javed, Fernand Gobet

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Abstract

How can we infer the strategies that human participants adopt to carry out a task? One possibility, which we present and discuss here, is to develop a large number of strategies that participants could have adopted, given a cognitive architecture and a set of possible operations. Subsequently, the (often many) strategies that best explain a dataset of interest are highlighted. To generate and select candidate strategies, we use genetic programming, a heuristic search method inspired by evolutionary principles. Specifically, combinations of cognitive operators are evolved and their performance compared against human participants’ performance on a specific task. We apply this methodology to a typical decision-making task, in which human participants were asked to select the brighter of two stimuli. We discover several understandable, psychologically-plausible strategies that offer explanations of participants’ performance. The strengths, applications and challenges of this methodology are discussed.

Original language	English
Title of host publication	Artificial Intelligence XL
Subtitle of host publication	43rd SGAI International Conference on Artificial Intelligence, AI 2023, Proceedings
Editors	M Bramer, F Stahl
Publisher	Springer Nature
Pages	407-420
Number of pages	14
Volume	14381
ISBN (Electronic)	978-3-031-47994-6
ISBN (Print)	978-3-031-47993-9
DOIs	https://doi.org/10.1007/978-3-031-47994-6_36
Publication status	E-pub ahead of print - 8 Nov 2023
Event	International Conference on Innovative Techniques and Applications of Artificial Intelligence - Cambridge, United Kingdom Duration: 12 Dec 2023 → 14 Dec 2023 http://bcs-sgai.org/ai2023/

Publication series

Name	Lecture Notes in Computer Science book series
Publisher	Springer
Volume	14381
ISSN (Electronic)	1611-3349

Conference

Conference	International Conference on Innovative Techniques and Applications of Artificial Intelligence
Country/Territory	United Kingdom
City	Cambridge
Period	12/12/23 → 14/12/23
Internet address	http://bcs-sgai.org/ai2023/

Access to Document

10.1007/978-3-031-47994-6_36

Accepted_Manuscript
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG. All rights are reserved by the Publisher. his is the accepted manuscript version of a conference paper which has been published in final form at https://doi.org/10.1007/978-3-031-47994-6
Accepted author manuscript, 1.41 MB

Cite this

Pirrone, A., Lane, P., Bartlett, L., Javed, N., & Gobet, F. (2023). Heuristic Search of Heuristics. In M. Bramer, & F. Stahl (Eds.), Artificial Intelligence XL: 43rd SGAI International Conference on Artificial Intelligence, AI 2023, Proceedings (Vol. 14381, pp. 407-420). (Lecture Notes in Computer Science book series; Vol. 14381). Springer Nature . Advance online publication. https://doi.org/10.1007/978-3-031-47994-6_36

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title = "Heuristic Search of Heuristics",

abstract = "How can we infer the strategies that human participants adopt to carry out a task? One possibility, which we present and discuss here, is to develop a large number of strategies that participants could have adopted, given a cognitive architecture and a set of possible operations. Subsequently, the (often many) strategies that best explain a dataset of interest are highlighted. To generate and select candidate strategies, we use genetic programming, a heuristic search method inspired by evolutionary principles. Specifically, combinations of cognitive operators are evolved and their performance compared against human participants{\textquoteright} performance on a specific task. We apply this methodology to a typical decision-making task, in which human participants were asked to select the brighter of two stimuli. We discover several understandable, psychologically-plausible strategies that offer explanations of participants{\textquoteright} performance. The strengths, applications and challenges of this methodology are discussed.",

author = "Angelo Pirrone and Peter Lane and Laura Bartlett and Noman Javed and Fernand Gobet",

note = "{\textcopyright} 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG. All rights are reserved by the Publisher. his is the accepted manuscript version of a conference paper which has been published in final form at https://doi.org/10.1007/978-3-031-47994-6; International Conference on Innovative Techniques and Applications of Artificial Intelligence ; Conference date: 12-12-2023 Through 14-12-2023",

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Pirrone, A, Lane, P, Bartlett, L, Javed, N & Gobet, F 2023, Heuristic Search of Heuristics. in M Bramer & F Stahl (eds), Artificial Intelligence XL: 43rd SGAI International Conference on Artificial Intelligence, AI 2023, Proceedings. vol. 14381, Lecture Notes in Computer Science book series, vol. 14381, Springer Nature , pp. 407-420, International Conference on Innovative Techniques and Applications of Artificial Intelligence, Cambridge, United Kingdom, 12/12/23. https://doi.org/10.1007/978-3-031-47994-6_36

Heuristic Search of Heuristics. / Pirrone, Angelo; Lane, Peter; Bartlett, Laura et al.
Artificial Intelligence XL: 43rd SGAI International Conference on Artificial Intelligence, AI 2023, Proceedings. ed. / M Bramer; F Stahl. Vol. 14381 Springer Nature , 2023. p. 407-420 (Lecture Notes in Computer Science book series; Vol. 14381).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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N1 - © 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG. All rights are reserved by the Publisher. his is the accepted manuscript version of a conference paper which has been published in final form at https://doi.org/10.1007/978-3-031-47994-6

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AB - How can we infer the strategies that human participants adopt to carry out a task? One possibility, which we present and discuss here, is to develop a large number of strategies that participants could have adopted, given a cognitive architecture and a set of possible operations. Subsequently, the (often many) strategies that best explain a dataset of interest are highlighted. To generate and select candidate strategies, we use genetic programming, a heuristic search method inspired by evolutionary principles. Specifically, combinations of cognitive operators are evolved and their performance compared against human participants’ performance on a specific task. We apply this methodology to a typical decision-making task, in which human participants were asked to select the brighter of two stimuli. We discover several understandable, psychologically-plausible strategies that offer explanations of participants’ performance. The strengths, applications and challenges of this methodology are discussed.

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Pirrone A, Lane P, Bartlett L, Javed N, Gobet F. Heuristic Search of Heuristics. In Bramer M, Stahl F, editors, Artificial Intelligence XL: 43rd SGAI International Conference on Artificial Intelligence, AI 2023, Proceedings. Vol. 14381. Springer Nature . 2023. p. 407-420. (Lecture Notes in Computer Science book series). Epub 2023 Nov 8. doi: 10.1007/978-3-031-47994-6_36

Heuristic Search of Heuristics

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