Paper Info
Title
Selective Perception as a Mechanism to Adapt Agents to the Environment: An Evolutionary Approach
Abstract
Rapid advancement of machine learning makes it possible to consider large amounts of data to learn from. Learning agents may get data ranging on real intervals directly from the environment they interact with, in a process usually time expensive. To improve learning and manage these data, approximated models and memory mechanisms are adopted. In most of the implementations of reinforcement learning facing this type of data, approximation is obtained by neural networks and the process of drawing information from data is mediated by a short-term memory that stores the previous experiences for additional relearning, to speed-up the learning process, mimicking what is done by people. In this paper, we are proposing a novel computational approach able to selectively filter the information, or cognitive load, for the agent’s short-term memory, thus emulating the attention mechanism characteristic of human perception. In this work, we use genetic algorithms in order to evolve the most efficient attention filter mechanism that would be able to provide the agent with an optimal perception for a specific environment by discriminating which experiences are valuable for the learning process. This approach can evolve a filter which can able to provide an optimal cognitive load of the experiences entering in the agent’s short-term memory of a limited capacity. The evolved sampling dynamics can also lead to the emergence of intrinsically motivated curiosity.
Year
DOI
Venue
2020
10.1109/TCDS.2019.2896306
IEEE Transactions on Cognitive and Developmental Systems
Keywords
DocType
Volume
Focusing,Computational modeling,Mathematical model,Reinforcement learning,Biological neural networks
Journal
12
Issue
ISSN
Citations 
1
2379-8920
1
PageRank 
References 
Authors
0.37
0
2
Name
Order
Citations
PageRank
Mirza Ramicic121.41
Andrea Bonarini262376.73