G. M. T. C. Galahena, S. P. Wimalaratne, (29 th December 2015), "Modeling the Learning Ability of Fish in an Artificial Fish Simulation Using Reinforcement Learning" , in International Conference on Emerging Trends in Artificial Intelligence Read Abstract
Modeling the Learning Ability of Fish in an Artificial Fish Simulation Using Reinforcement Learning
Fish display a considerable amount of learning skills in activities like foraging and defense (ex: locations and quality food patches, areas where certain predators are in, etc.). But most of the existing models of fish behavior do not simulate the learning patterns of fish. It makes those models less realistic. This research tries to fill that gap by creating a model with learning ability which is more similar to the actual behavior of the fish. The main focus of this research will be on the learning involving in foraging and defense of the fish. The system will be a comprised of multiple agents to represent fish and each agent will act individually. The senses and the locomotion abilities of the agents in the simulation will be generalized representations of actual fish. And the learning will be simulated using machine learning algorithms.