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Modeling Success, Failure, and Intent of Multi-Agent Activities Under Severe Noise

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Mobile Context Awareness

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Abstract

This chapter takes 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. 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 its impact on the behavior of the people involved. Further, we show that given a model of successfully performed multi-agent activities, along with a set of examples of failed attempts at the same activities, our system automatically learns an augmented model that is capable of recognizing success and failure, as well as goals of people’s actions with high accuracy. We compare our approach with other alternatives and show that our unified model, which takes into account not only relationships among individual players, but also relationships among activities over the entire length of a game, although more computationally costly, is significantly more accurate. Finally, we demonstrate that interesting game segments and key players can be efficiently identified in an automated fashion. Our system exhibits a strong agreement with human judgement about the game situations at hand.

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Notes

  1. 1.

    http://alchemy.cs.washington.edu/.

  2. 2.

    http://code.google.com/p/theBeast/.

  3. 3.

    Cheating did not occur in our CTF games, but in principle could be accommodated by making the rules highly-weighted soft constraints rather than hard constraints.

  4. 4.

    While the noise in the GPS data introduces some ambiguity to the last two observed predicates, we can still reliably generate them since the road that marks the boundary between territories constitutes a neutral zone.

  5. 5.

    The initial point of each snapping (projection) vector is a raw GPS reading and the terminal point is the center of the cell we snap that reading to.

  6. 6.

    Since in our domain, each example is a complete truth assignment, testing entailment is equivalent to checking if F∪c is logically consistent.

  7. 7.

    This is often referred to as the covers relation in inductive logic programming.

  8. 8.

    A situation where a player in CTF moves through the campus at a speed of 100 mph and on her way passes an enemy player is certainly anomalous (and probably caused by GPS sensor noise), but we do not want to say that it is a failed attempt at capturing.

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Acknowledgements

This chapter extends work published in Sadilek and Kautz [72–74]. We thank Sebastian Riedel for his help with theBeast, and to Radka Sadílková and Wendy Beatty for their helpful comments. This work was supported by ARO grant #W911NF-08-1-0242, DARPA SBIR Contract #W31P4Q-08-C-0170, and a gift from Kodak.

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    Adam Sadilek & Henry Kautz

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Sadilek, A., Kautz, H. (2012). Modeling Success, Failure, and Intent of Multi-Agent Activities Under Severe Noise. In: Lovett, T., O'Neill, E. (eds) Mobile Context Awareness. Springer, London. https://doi.org/10.1007/978-0-85729-625-2_2

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