AgentP Model: Learning Classifier System with Associative Perception
نویسنده
چکیده
Aliasing environments present the tasks of increased difficulty for Learning Classifier Systems. Aliasing squares look identical for an agent with limited perceptive power, but may demand a completely different optimal strategy. Thus, the presence of aliasing squares in a maze may lead to a non-optimal behaviour and decrease the agent’s performance. As a possible approach to the problem we introduce a psychological model of associative perception learning and based on the model AgentP, an LCS with explicitly imprinted images of the environmental states. The system is tested on several aliasing environments to establish the learning effectiveness of the approach.
منابع مشابه
A Reinforcement Learning Agent with Associative Perception
One of the most perspective ideas of further development of Reinforcement Learning (RL) research involves using associative learning models to improve performance of reinforcement learning agents. Learning Classifier Systems (LCS) have proved to be one of the most successful classes of RL methods that have been applied to maze environments. However, so far LCS have shown their effectiveness for...
متن کاملLearning Mazes with Aliasing States: An LCS Algorithm with Associative Perception
Maze problems represent a simplified virtual model of real environments that can be used for developing core algorithms of many real-world application related to the problem of navigation. However, the best achievements of Learning Classifier Systems (LCS) in maze problems are still mostly bounded to non-aliasing environments, while LCS complexity seems to obstruct a proper analysis of the reas...
متن کاملRole of Interestingness Measures in CAR Rule Ordering for Associative Classifier: An Empirical Approach
Associative Classifier is a novel technique which is the integration of Association Rule Mining and Classification. The difficult task in building Associative Classifier model is the selection of relevant rules from a large number of class association rules (CARs). A very popular method of ordering rules for selection is based on confidence, support and antecedent size (CSA). Other methods are ...
متن کاملAcquisition of automatic imitation is sensitive to sensorimotor contingency.
The associative sequence learning model proposes that the development of the mirror system depends on the same mechanisms of associative learning that mediate Pavlovian and instrumental conditioning. To test this model, two experiments used the reduction of automatic imitation through incompatible sensorimotor training to assess whether mirror system plasticity is sensitive to contingency (i.e....
متن کاملThe formation of context in artificial object recognition
Context plays an important role in the recognition of objects, allowing the general content of a scene to influence identification of individual parts. An autonomous learning system is presented that examines processes involved in the formation of context between multiple co-occurring objects, under the task of identifying abstract objects in a scene. Learning is performed using a form of Learn...
متن کامل