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Simulating exploration versus exploitation in agent foraging under different environment uncertainties

Published online by Cambridge University Press:  19 March 2019

Nader Chmait Open the ORCID record for Nader Chmait [Opens in a new window] ,
David L. Dowe ,
David G. Green Open the ORCID record for David G. Green [Opens in a new window]  and
Yuan-Fang Li Open the ORCID record for Yuan-Fang Li [Opens in a new window]
Nader Chmait
Affiliation:
Faculty of Information Technology, Monash University, Melbourne, VIC 3800, Australia. [email protected]@[email protected]@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. [email protected]@[email protected]@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. [email protected]@[email protected]@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. [email protected]@[email protected]@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/
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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
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Copyright
Copyright © Cambridge University Press 2019 

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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
Wallace, C. S. & Dowe, D. L. (1999) Minimum message length and Kolmogorov complexity. Computer Journal 42(4):270–83.Google Scholar