Analysis of reinforcement learning strategies for predation in a mimic-model prey environment

Simple item page
dc.contributor.authorTsoularis, A.
dc.contributor.authorWallace, J.
dc.date.accessioned2013-05-15T02:09:46Z
dc.date.available2013-05-15T02:09:46Z
dc.date.issued2005
dc.description.abstractIn this paper we propose a mathematical learning model for a stochastic automaton simulating the behaviour of a predator operating in a random environment occupied by two types of prey: palatable mimics and unpalatable models. Specifically, a well known linear reinforcement learning algorithm is used to update the probabilities of the two actions, eat prey or ignore prey, at every random encounter. Each action elicits a probabilistic response from the environment that can be either favorable or unfavourable. We analyse both fixed and varying stochastic responses for the system. The basic approach of mimicry is defined and a short review of relevant previous approaches in the literature is given. Finally, the conditions for continuous predator performance improvement are explicitly formulated and precise definitions of predatory efficiency and mimicry efficiency are also provided.en
dc.identifier.citationTsoularis, A., Wallace, J. (2005), Analysis of reinforcement learning strategies for predation in a mimic-model prey environment, Research Letters in the Information and Mathematical Sciences, 7, 43-88en
dc.identifier.issn1175-2777
dc.identifier.urihttp://hdl.handle.net/10179/4442
dc.language.isoenen
dc.publisherMassey Universityen
dc.subjectPredator-prey modelen
dc.subjectBatesian mimicryen
dc.titleAnalysis of reinforcement learning strategies for predation in a mimic-model prey environmenten
dc.typeArticleen
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Analysis_of_reinforcement_learning_strategies_for_predation_in_a_mimic-model_prey_environment.pdf
Size:
287.51 KB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Research Letters in the Information and Mathematical Sciences