Building High-Performing Human-Like Tactical Agents Through Observation and Experience
Abbreviated Journal Title
IEEE Trans. Syst. Man Cybern. Part B-Cybern.
Experiential learning; FALCONET; haptics; machine learning; multimodal; observational learning; PIGEON; NEURAL NETWORKS; Automation & Control Systems; Computer Science, Artificial Intelligence; Computer Science, Cybernetics
This paper describes a two-phase approach for automating the agent-building process when the agent is to perform tactical tasks. The research is inspired by how humans learn-first by observation of a teacher's performance and then by practicing the performance themselves. The objectives of this approach are to produce a high-performing agent that 1) approaches or exceeds the proficiency of a human and 2) does so in a human-like manner. We accomplish these objectives by combining observational learning with experiential learning. These processes are executed sequentially, with the former creating a competent but somewhat limited human-like model from scratch, and the latter improving its performance without significantly eroding its human-like qualities. The process is described in detail, and test results confirming our hypothesis are described.
Ieee Transactions on Systems Man and Cybernetics Part B-Cybernetics
"Building High-Performing Human-Like Tactical Agents Through Observation and Experience" (2011). Faculty Bibliography 2010s. 1950.