Building entity models through observational learning

Abstract

To support the missions and tasks of mixed robotic/human teams, future robotic systems will need to adapt to the dynamic behavior of both teammates and opponents. One of the basic elements of this adaptation is the ability to exploit both long- and short-term temporal data. This adaptation allows robotic systems to predict/anticipate, as well as influence, future behavior for both opponents and teammates and will afford the system the ability to adjust its own behavior in order to optimize its ability to achieve the mission goals. This work is a preliminary step in the effort to develop online entity behavior models through a combination of learning techniques and observations. As knowledge is extracted from the system through sensor and temporal feedback, agents within the multi-agent system attempt to develop and exploit a basic movement model of an opponent. For the purpose of this work, extraction and exploitation is performed through the use of a discretized two-dimensional game. The game consists of a predetermined number of sentries attempting to keep an unknown intruder agent from penetrating their territory. The sentries utilize temporal data coupled with past opponent observations to hypothesize the probable locations of the opponent and thus optimize their guarding locations.