Abstract
Multi-agent simulations facilitate modelling the complex behaviours through simple rules codified within the agents. However, the manual exploration of effective rules is generally prohibitive or at least sub-optimal in these types of simulations. Evolutionary techniques can effectively explore the rule and parameter space for high fidelity simulations; however, the pairing of these two techniques is challenging in classes of problems where the evaluation of simulation fidelity is reliant on human judgement. In this work we present a machine learning approach to evolve high fidelity low complexity sheepdog herding simulations. A multi-objective evolutionary algorithm is applied to evolve simulations with both high fidelity and low complexity as the two objectives to be optimised. Fidelity is measured via a machine learning system trained using a small set of training samples of human judgements; whereas the complexity is measured using the number of rules and parameters used to codify the agents in the system. The experimental results demonstrate the effectiveness of the approach in evolving high fidelity and low complexity multi-agent simulations when the fidelity cannot be measured objectively.
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Notes
- 1.
Note that these weights were decided by co-evolving the dog and sheep in our pilot experiments. Although we have provided the weights with a high precision, that precision has no significant impact on the behaviours. A higher weight for the rule attract to the flock makes the dog more aggressive.
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Lakshika, E., Barlow, M., Easton, A. (2015). Evolving High Fidelity Low Complexity Sheepdog Herding Simulations Using a Machine Learner Fitness Function Surrogate for Human Judgement. In: Pfahringer, B., Renz, J. (eds) AI 2015: Advances in Artificial Intelligence. AI 2015. Lecture Notes in Computer Science(), vol 9457. Springer, Cham. https://doi.org/10.1007/978-3-319-26350-2_29
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