Recognizing Multi-Agent Activities from GPS Data

Recent research has shown that surprisingly rich models of human behavior can be learned from GPS (positional) data. However, most research to date has concentrated on modeling single individuals or aggregate statistical properties of groups of people. Given noisy real-world GPS data, we--in contrast--consider the problem of modeling and recognizing activities that involve multiple related individuals playing a variety of roles. Our test domain is the game of capture the flag--an outdoor game that involves many distinct cooperative and competitive joint activities. We model the domain using Markov logic, a statistical relational language, and learn a theory that jointly denoises the data and infers occurrences of high-level activities, such as capturing a player. Our model combines constraints imposed by the geometry of the game area, the motion model of the players, and by the rules and dynamics of the game in a probabilistically and logically sound fashion. We show that while it may...

• Type: Conference
• Year: 2010
• Where: AAAI
• Authors: Adam Sadilek, Henry A. Kautz
• Keywords: AAAI 2010, GPS Data, Intelligent Agents, Modeling Single Individuals, Noisy Real-world Gps