Department of Psychology
Carnegie Mellon University
Mauldin Auditorium (NSH 1305 )
Talk 3:30 pm
Microsoft Robotics Studio (MSRS) is a Windows-based IDE for robotics programming. Its primary components are the Concurrency and Coordination Runtime (CCR) and the Decentralized System Services (DSS). The CCR emphasizes in scheduling the tasks to manage concurrency and load-balancing for different applications. The DSS is a service-oriented approach to robot component integration where every software or hardware component of a design is a service. Such web-based architecture allows services within a network to interact. Given the experience of MSRS with LEGO NXT bricks, this presentation will provide a brief introduction to CCR and DSS, and give some insight on the maturity of MSRS.
Proficient teams can accomplish goals that would not otherwise be achievable by groups of uncoordinated individuals. This thesis addresses the problem of analyzing team activities from external observations and prior knowledge of the team's behavior patterns. There are three general classes of recognition cues that are potentially valuable for team activity/plan recognition: (1) spatial relationships between team members and/or physical landmarks that stay fixed over a period of time; (2) temporal dependencies between behaviors in a plan or between actions in a behavior; (3) coordination constraints between agents and the actions that they are performing. This thesis examines how to leverage available spatial, temporal, and coordination cues to perform offline multi-agent activity/plan recognition for teams with dynamic membership.
In physical domains (military, athletic, or robotic), team behaviors often have an observable spatio-temporal structure, defined by the relative physical positions of team members and their relation to static landmarks; we suggest that this structure, along with temporal dependencies and coordination constraints defined by a team plan library, can be exploited to perform behavior recognition on traces of agent activity over time, even in the presence of uninvolved agents. Unlike prior work in team plan recognition where it is assumed that team membership stays constant over time, this thesis addresses the novel problem of recovering agent-to-team assignment for team tasks where team composition, the mapping of agents into teams, changes over time; this allows the analysis of more complicated tasks in which agents must periodically divide into subteams.
This thesis makes four main contributions: (1) an efficient and robust technique for formation identification based on spatial relationships; (2) a new algorithm for simultaneously determining team membership and performing behavior recognition on spatio-temporal traces with dynamic team membership; (3) a general pruning technique based on coordination cues that improves the efficiency of plan recognition for dynamic teams; (4) methods for identifying player policies in team games that lack strong spatial, temporal, and coordination dependencies.