Abstract
Intent recognition is the process of determining the action an agent is about to take, given a sequence of past actions. In this paper, we propose a method for recognizing intentions in highly populated multi-agent environments. Low-level intentions, representing basic activities, are detected through a novel formulation of Hidden Markov Models with perspective-taking capabilities. Higher level intentions, involving multiple agents, are detected with a distributed architecture that uses activation spreading between nodes to detect the most likely intention of the agents. The solution we propose brings the following main contributions: (i) it enables early recognition of intentions before they are being realized, (ii) it has real-time performance capabilities, and (iii) it can detect both single agent as well as joint intentions of a group of agents. We validate our framework in an open source naval ship simulator, the context of recognizing threatening intentions against naval ships. Our results show that our system is able to detect intentions early and with high accuracy.
This work has been supported by the Office of Naval Research, under grant number N00014-09-1-1121.
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Saffar, M.T., Nicolescu, M., Nicolescu, M., Bigelow, D., Ballinger, C., Louis, S. (2015). Intent Recognition in a Simulated Maritime Multi-agent Domain. In: Pardalos, P., Pavone, M., Farinella, G., Cutello, V. (eds) Machine Learning, Optimization, and Big Data. MOD 2015. Lecture Notes in Computer Science(), vol 9432. Springer, Cham. https://doi.org/10.1007/978-3-319-27926-8_14
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