Trevor Sarratt

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Maintaining an accurate set of beliefs in a partially observable scenario, particularly with respect to other agents operating in the same space, is a vital aspect of multiagent planning. We analyze how the beliefs of an agent can be updated for fast adaptivity to changes in the behavior of an unknown teammate. The main contribution of this paper is the(More)
Player models allow search algorithms to account for differences in agent behavior according to player's preferences and goals. However, it is often not until the first actions are taken that an agent can begin assessing which models are relevant to its current opponent. This paper investigates the integration of belief distributions over player models in(More)
Sentiment classification provides information about the author’s feeling toward a topic through the use of expressive words. However, words indicative of a particular sentiment class can be domain-specific. We train a text classifier for Twitter data related to games using labels inferred from emoticons. Our classifier is able to differentiate between(More)
Ad hoc teams are formed of members who have little or no information regarding one another. In order to achieve a shared goal, agents are tasked with learning the capabilities of their teammates such that they can coordinate effectively. Typically, the capabilities of the agent teammates encountered are constrained by the particular domain specifications.(More)
The nematode Caenorhabditis elegans is an important model organism for many areas of biological research including genetics, development, and neurobiology. It is the first organism to have its genome sequenced, complete cell ontogeny determined, and nervous system mapped. With all of the information that is available on this simple organism, C. elegans may(More)
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