A coevolutionary approach to learn animal behavior through controlled interaction

Li, Wei; Gauci, Melvin; Groß, Roderich

doi:10.1145/2463372.2465801

Cited by 12 publications

(10 citation statements)

References 17 publications

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“…We originally presented the basic idea of Turing Learning, along with preliminary simulations, in (Li et al, 2013(Li et al, , 2014. This paper extends our prior work as follows:…”

Section: Introductionmentioning

confidence: 73%

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Turing learning: a metric-free approach to inferring behavior and its application to swarms

Gauci

Groß

2016

Swarm Intell

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We propose Turing Learning, a novel system identification method for inferring the behavior of natural or artificial systems. Turing Learning simultaneously optimizes two populations of computer programs, one representing models of the behavior of the system under investigation, and the other representing classifiers. By observing the behavior of the system as well as the behaviors produced by the models, two sets of data samples are obtained. The classifiers are rewarded for discriminating between these two sets, that is, for correctly categorizing data samples as either genuine or counterfeit. Conversely, the models are rewarded for 'tricking' the classifiers into categorizing their data samples as genuine. Unlike other methods for system identification, Turing Learning does not require predefined metrics to quantify the difference between the system and its models. We present two case studies with swarms of simulated robots and prove that the underlying behaviors cannot be inferred by a metric-based system identification method. By contrast, Turing Learning infers the behaviors with high accuracy. It also produces a useful by-product-the classifiersthat can be used to detect abnormal behavior in the swarm. Moreover, we show that Turing Learning also successfully infers the behavior of physical robot swarms. The results show All authors have contributed equally to this work. Intell (2016) 10:211-243 that collective behaviors can be directly inferred from motion trajectories of individuals in the swarm, which may have significant implications for the study of animal collectives. Furthermore, Turing Learning could prove useful whenever a behavior is not easily characterizable using metrics, making it suitable for a wide range of applications. Electronic supplementary material

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“…We originally presented the basic idea of Turing Learning, along with preliminary simulations, in (Li et al, 2013(Li et al, , 2014. This paper extends our prior work as follows:…”

Section: Introductionmentioning

confidence: 73%

“…The objective for the classifiers is to distinguish between the models and the system. The idea of Turing Learning was first proposed in (Li et al, 2013); this work presented a coevolutionary approach for inferring the behavioral rules of a single agent. The agent moved in a simulated, one-dimensional environment.…”

Section: Related Workmentioning

confidence: 99%

Turing learning: a metric-free approach to inferring behavior and its application to swarms

Gauci

Groß

2016

Swarm Intell

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“…Li et al [19] presented a coevolutionary approach for learning the behavior of a single animal that moved in a 1-D world. The system uses classifiers to distinguish between the models and the animal.…”

Section: Introductionmentioning

confidence: 99%

“…The system uses classifiers to distinguish between the models and the animal. Our system extends the framework in [19] to learn about the collective behavior of groups of animals. The classifiers decide whether an agent is an animal or the replica solely based on the motion data of this particular agent.…”

Section: Introductionmentioning

confidence: 99%

Coevolutionary learning of swarm behaviors without metrics

Gauci

Groß

2014

Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation

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We propose a coevolutionary approach for learning the behavior of animals, or agents, in collective groups. The approach requires a replica that resembles the animal under investigation in terms of appearance and behavioral capabilities. It is able to identify the rules that govern the animals in an autonomous manner. A population of candidate models, to be executed on the replica, compete against a population of classifiers. The replica is mixed into the group of animals and all individuals are observed. The fitness of the classifiers depends solely on their ability to discriminate between the replica and the animals based on their motion over time. Conversely, the fitness of the models depends solely on their ability to 'trick' the classifiers into categorizing them as an animal. Our approach is metric-free in that it autonomously learns how to judge the resemblance of the models to the animals. It is shown in computer simulation that the system successfully learns the collective behaviors of aggregation and of object clustering. A quantitative analysis reveals that the evolved rules approximate those of the animals with a good precision.

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“…In the second part of the talk, we present a method that is able to identify models (parameters) of individuals, for example, when part of a swarm, through observation and interaction [5,6]. This method does not require any pre-defined metric to gauge the resemblance of models to observed individuals.…”

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confidence: 99%