A Case-Based Reasoning Framework for Developing Agents Using Learning by Observation

Michael W. Floyd, Babak Esfandiari
2011 2011 IEEE 23rd International Conference on Tools with Artificial Intelligence  
Most realistic environments are complex, partially observable and impose real-time constraints on agents operating within them. This paper describes a framework that allows agents to learn by observation in such environments. When learning by observation, agents observe an expert performing a task and learn to perform the same task based on those observations. Our framework aims to allow agents to learn in a variety of domains (physical or virtual) regardless of the behaviour or goals of the
more » ... or goals of the observed expert. To achieve this we ensure that there is a clear separation between the central reasoning system and any domain-specific information. We present case studies in the domains of obstacle avoidance, robotic arm control, simulated soccer and Tetris.
doi:10.1109/ictai.2011.86 dblp:conf/ictai/FloydE11 fatcat:mxv25mnhbfbjbe5ork4tcrf32i