Simulating exploration versus exploitation in agent foraging under different environment uncertainties

Nader Chmait; David L. Dowe; David G. Green; Yuan-Fang Li

doi:10.1017/S0140525X18001954

Simulating exploration versus exploitation in agent foraging under different environment uncertainties

Published online by Cambridge University Press: 19 March 2019

and

Nader Chmait: Affiliation:
Faculty of Information Technology, Monash University, Melbourne, VIC 3800, Australia. nader.chmait@monash.edudavid.dowe@monash.edudavid.green@monash.eduyuanfang.li@monash.eduhttp://users.monash.edu.au/~naderc/http://www.csse.monash.edu.au/~dld/David.Dowe.publications.htmlhttps://research.monash.edu/en/persons/david-greenhttp://users.monash.edu.au/~yli/
David L. Dowe: Affiliation:
Faculty of Information Technology, Monash University, Melbourne, VIC 3800, Australia. nader.chmait@monash.edudavid.dowe@monash.edudavid.green@monash.eduyuanfang.li@monash.eduhttp://users.monash.edu.au/~naderc/http://www.csse.monash.edu.au/~dld/David.Dowe.publications.htmlhttps://research.monash.edu/en/persons/david-greenhttp://users.monash.edu.au/~yli/
David G. Green: Affiliation:
Faculty of Information Technology, Monash University, Melbourne, VIC 3800, Australia. nader.chmait@monash.edudavid.dowe@monash.edudavid.green@monash.eduyuanfang.li@monash.eduhttp://users.monash.edu.au/~naderc/http://www.csse.monash.edu.au/~dld/David.Dowe.publications.htmlhttps://research.monash.edu/en/persons/david-greenhttp://users.monash.edu.au/~yli/
Yuan-Fang Li: Affiliation:
Faculty of Information Technology, Monash University, Melbourne, VIC 3800, Australia. nader.chmait@monash.edudavid.dowe@monash.edudavid.green@monash.eduyuanfang.li@monash.eduhttp://users.monash.edu.au/~naderc/http://www.csse.monash.edu.au/~dld/David.Dowe.publications.htmlhttps://research.monash.edu/en/persons/david-greenhttp://users.monash.edu.au/~yli/

Article contents

Abstract
References

Get access

Rights & Permissions

Abstract

For artificial agents trading off exploration (food seeking) versus (short-term) exploitation (or consumption), our experiments suggest that uncertainty (interpreted information, theoretically) magnifies food seeking. In more uncertain environments, with food distributed uniformly randomly, exploration appears to be beneficial. In contrast, in biassed (less uncertain) environments, with food concentrated in only one part, exploitation appears to be more advantageous. Agents also appear to do better in biassed environments.

Type: Open Peer Commentary
Information: Behavioral and Brain Sciences , Volume 42 , 2019 , e39

DOI: https://doi.org/10.1017/S0140525X18001954 [Opens in a new window]
Copyright: Copyright © Cambridge University Press 2019

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Bartumeus, F., Campos, D., Ryu, W. S., Lloret-Cabot, R., Méndez, V. & Catalan, J. (2016) Foraging success under uncertainty: Search tradeoffs and optimal space use. Ecology Letters 19(11):1299–313.Google Scholar

Boulton, D. M. & Wallace, C. S. (1969) The information content of a multistate distribution. Journal of Theoretical Biology 23:269–78.Google Scholar

Chmait, N., Dowe, D. L., Green, D. G. & Li, Y. F. (2015) Observation, communication and intelligence in agent-based systems. In: 8th International Conference on Artificial General Intelligence, ed. Bieger, J., Goertzel, B., & Potapov, Alexey, pp. 50–59. Lecture Notes in Computer Science 9205. Springer.Google Scholar

Chmait, N., Dowe, D. L., Li, Y. F. & Green, D. G. (2017) An information-theoretic predictive model for the accuracy of AI agents adapted from psychometrics. In: 10th International Conference on Artificial General Intelligence, ed. Everitt, T., Goertzel, B. & Potapov, A., pp. 225–36. Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics 10414. Springer.Google Scholar

Chmait, N., Dowe, D. L., Li, Y. F., Green, D. G. & Insa-Cabrera, J. (2016a) Factors of collective intelligence: How smart are agent collectives? In: European 22nd Conference on Artificial Intelligence, ed. Kaminka, G. A., Fox, M., Bouquet, P., Hüllermeier, E., Dignum, V., Dignum, F. & van Harmelen, F., pp. 542–50. IOS Press.Google Scholar

Chmait, N., Li, Y. F., Dowe, D. L. & Green, D. G. (2016b) A dynamic intelligence test framework for evaluating AI agents. In: Proceedings of 1st International Workshop on Evaluating General-Purpose AI (EGPAI 2016), A workshop held in conjunction with the European Conference on Artificial Intelligence (ECAI 2016), The Hague, The Netherlands.Google Scholar

Green, D. G., Klomp, N., Rimmington, G. & Sadedin, S. (2006) Complexity in landscape ecology, vol. 4. Springer Science & Business Media.Google Scholar

Green, D. G., Liu, J. & Abbass, H. A. (2014) Dual-phase evolution (chapter 1). In: Dual phase evolution, ed. Green, D. G., Liu, J. & Abbass, H. A., pp. 3–40. Springer.Google Scholar

Hernández-Orallo, J., Baroni, M., Bieger, J., Chmait, N., Dowe, D. L., Hofmann, K., Martínez-Plumed, F., Strannegård, C. & Thórisson, K. R. (2017) A new AI evaluation cosmos: Ready to play the game? AI Magazine 38(3):66–69.Google Scholar

Mehlhorn, K., Newell, B. R., Todd, P. M., Lee, M. D., Morgan, K., Braithwaite, V. A., Hausmann, D., Fiedler, K. & Gonzalez, C. (2015) Unpacking the exploration–exploitation tradeoff: A synthesis of human and animal literatures. Decision 2(3):191.Google Scholar

Newman, M. E. J. & Ziff, R. M. (2000) Efficient Monte-Carlo algorithm and high-precision results for percolation. Physical Review Letters 85(19):4104–107.Google Scholar

Paperin, G., Green, D. G. & Sadedin, S. (2011) Dual-phase evolution in complex adaptive systems. Journal of the Royal Society Interface 8(58):609–29.Google Scholar