title: Building high-performing human-like tactical agents through observation and experience.
abstract: 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.
authors.affiliation: School of Electrical Engineering and Computer Science, University of Central Florida, Orlando, FL 32826, USA. gstein@mail.ucf.edu
authors.forename: Gary
authors.initials: G
authors.lastname: Stein
authors.forename: Avelino J
authors.initials: AJ
authors.lastname: Gonzalez
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D032701:Biomimetics / Q000379:methods*; D003198:Computer Simulation; D003661:Decision Support Techniques; D006801:Humans; D008962:Models, Theoretical; D010363:Pattern Recognition, Automated / Q000379:methods*; D012371:Robotics / Q000379:methods*