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Location-based reasoning about complex multiagent behavior (2011)
| Venue: | In Journal of Artificial Intelligence Research. AI Access Foundation |
| Citations: | 11 - 3 self |
BibTeX
@INPROCEEDINGS{Sadilek11location-basedreasoning,
author = {Adam Sadilek and Henry Kautz},
title = {Location-based reasoning about complex multiagent behavior},
booktitle = {In Journal of Artificial Intelligence Research. AI Access Foundation},
year = {2011}
}
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Abstract
Recent research has shown that surprisingly rich models of human activity can be learned from GPS (positional) data. However, most effort to date has concentrated on modeling single individuals or statistical properties of groups of people. Moreover, prior work focused solely on modeling actual successful executions (and not failed or attempted executions) of the activities of interest. We, in contrast, take on the task of understanding human interactions, attempted interactions, and intentions from noisy sensor data in a fully relational multi-agent setting. We use a real-world game of capture the flag to illustrate our approach in a well-defined domain 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 a player capturing an enemy. Our unified 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 be impossible to directly detect a multi-agent activity due to sensor noise or malfunction, the occurrence of the activity can still be inferred by considering both its impact on the
Keyphrases
complex multiagent behavior human activity game area well-defined domain attempted execution motion model rich model statistical-relational language relational multi-agent setting real-world game actual successful execution multi-agent activity high-level activity noisy sensor data human interaction attempted interaction sound fashion prior work single individual recent research statistical property unified model combine constraint competitive joint activity markov logic infers occurrence
