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All Outputs (9)

On the evolution of behaviors through embodied imitation (2015)
Journal Article
Erbas, M. D., Bull, L., & Winfield, A. F. (2015). On the evolution of behaviors through embodied imitation. Artificial Life, 21(2), 141-165. https://doi.org/10.1162/ARTL_a_00164

© 2015 Massachusetts Institute of Technology. Abstract This article describes research in which embodied imitation and behavioral adaptation are investigated in collective robotics. We model social learning in artificial agents with real robots. The... Read More about On the evolution of behaviors through embodied imitation.

Embodied imitation-enhanced reinforcement learning in multi-agent systems (2013)
Journal Article
Erbas, M. D., Winfield, A. F., & Bull, L. (2014). Embodied imitation-enhanced reinforcement learning in multi-agent systems. Adaptive Behavior, 22(1), 31-50. https://doi.org/10.1177/1059712313500503

Imitation is an example of social learning in which an individual observes and copies another's actions. This paper presents a new method for using imitation as a way of enhancing the learning speed of individual agents that employ a well-known reinf... Read More about Embodied imitation-enhanced reinforcement learning in multi-agent systems.

Using the XCS classifier system for multi-objective reinforcement learning problems (2007)
Journal Article
Studley, M., & Bull, L. (2007). Using the XCS classifier system for multi-objective reinforcement learning problems. Artificial Life, 13(1), 69-86. https://doi.org/10.1162/artl.2007.13.1.69

We investigate the performance of a learning classifier system in some simple multi-objective, multi-step maze problems, using both random and biased action-selection policies for exploration. Results show that the choice of action-selection policy c... Read More about Using the XCS classifier system for multi-objective reinforcement learning problems.

Accuracy-based learning classifier system ensembles with rule-sharing (2007)
Journal Article
Bull, L., Studley, M., Bagnall, A., & Whittley, I. (2007). Accuracy-based learning classifier system ensembles with rule-sharing. IEEE Transactions on Evolutionary Computation, 11(4), 496-502. https://doi.org/10.1109/TEVC.2006.885163

Learning Classifier Systems (LCS) are a method of evolving compact rule-sets using reinforcement learning. This paper presents an investigation into exploiting the population-based nature of LCS for their use within highly-parallel systems, such as... Read More about Accuracy-based learning classifier system ensembles with rule-sharing.

Fuzzy-XCS: A Michigan genetic fuzzy system (2007)
Journal Article
Casillas, J., Carse, B., & Bull, L. (2007). Fuzzy-XCS: A Michigan genetic fuzzy system. IEEE Transactions on Fuzzy Systems, 15(4), 536-550. https://doi.org/10.1109/TFUZZ.2007.900904

The issue of finding fuzzy models with an interpretability as good as possible without decreasing the accuracy is one of the main research topics on genetic fuzzy systems. When they are used to perform online reinforcement learning by means of Michig... Read More about Fuzzy-XCS: A Michigan genetic fuzzy system.

X-TCS: Accuracy-based learning classifier system robotics (2005)
Journal Article
Studley, M., & Bull, L. (2005). X-TCS: Accuracy-based learning classifier system robotics. https://doi.org/10.1109/CEC.2005.1554954

Although most learning classifier system (LCS) research uses the accuracy-based XCS, it had never been used to control a physical robot before. In comparison to purely evolutionary or purely reinforcement learning approaches, an LCS should be faster... Read More about X-TCS: Accuracy-based learning classifier system robotics.

For Real! XCS with Continuous-Valued Inputs (2003)
Journal Article
Stone, C., & Bull, L. (2003). For Real! XCS with Continuous-Valued Inputs. Evolutionary Computation, 11(3), 299-336. https://doi.org/10.1162/106365603322365315

Many real-world problems are not conveniently expressed using the ternary representation typically used by Learning Classifier Systems and for such problems an interval-based representation is preferable. We analyse two interval-based representations... Read More about For Real! XCS with Continuous-Valued Inputs.

ZCS redux (2002)
Journal Article
Bull, L., & Hurst, J. (2002). ZCS redux. Evolutionary Computation, 10(2), 185-205. https://doi.org/10.1162/106365602320169848

Learning classifier systems traditionally use genetic algorithms to facilitate rule discovery, where rule fitness is payoff based. Current research has shifted to the use of accuracy-based fitness. This paper re-examines the use of a particular payof... Read More about ZCS redux.