2 resultados para Maze Learning
em University of Queensland eSpace - Australia
Resumo:
Based on clues from epidemiology, low prenatal vitamin D has been proposed as a candidate risk factor for schizophrenia. Recent animal experiments have demonstrated that transient prenatal vitamin D deficiency is associated with persistent alterations in brain morphology and neurotrophin expression. In order to explore the utility of the vitamin D animal model of schizophrenia, we examined different types of learning and memory in adult rats exposed to transient prenatal vitamin D deficiency. Compared to control animals, the prenatally deplete animals had a significant impairment of latent inhibition, a feature often associated with schizophrenia. In addition, the deplete group was (a) significantly impaired on hole board habituation and (b) significantly better at maintaining previously learnt rules of brightness discrimination in a Y-chamber. In contrast, the prenatally deplete animals showed no impairment on the spatial learning task in the radial maze, nor on two-way active avoidance learning in the shuttle-box. The results indicate that transient prenatal vitamin D depletion in the rat is associated with subtle and discrete alterations in learning and memory. The behavioural phenotype associated with this animal model may provide insights into the neurobiological correlates of the cognitive impairments of schizophrenia. (c) 2005 Elsevier B.V. All rights reserved.
Resumo:
Pac-Man is a well-known, real-time computer game that provides an interesting platform for research. We describe an initial approach to developing an artificial agent that replaces the human to play a simplified version of Pac-Man. The agent is specified as a simple finite state machine and ruleset. with parameters that control the probability of movement by the agent given the constraints of the maze at some instant of time. In contrast to previous approaches, the agent represents a dynamic strategy for playing Pac-Man, rather than a pre-programmed maze-solving method. The agent adaptively "learns" through the application of population-based incremental learning (PBIL) to adjust the agents' parameters. Experimental results are presented that give insight into some of the complexities of the game, as well as highlighting the limitations and difficulties of the representation of the agent.