Muthukumaran Chandrasekaran

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We present a novel approach for identifying exact and approximate behavioral equivalence between models of agents. This is significant because both decision making and game play in multiagent settings must contend with behavioral models of other agents in order to predict their actions. One approach that reduces the complexity of the model space is to group(More)
Planning for ad hoc teamwork is challenging because it involves agents collaborating without any prior coordination or communication. The focus is on principled methods for a single agent to cooperate with others. This motivates investigating the ad hoc teamwork problem in the context of individual decision making frameworks. However, individual decision(More)
Interactive dynamic influence diagrams (I-DID) are graphical models for sequential decision making in uncertain settings shared by other agents. Algorithms for solving I-DIDs face the challenge of an exponentially growing space of candidate models ascribed to other agents, over time. Pruning behaviorally equivalent models is one way toward minimizing the(More)
Interactive dynamic influence diagrams (I-DID) are graphical models for sequential decision making in uncertain settings shared by other agents. Algorithms for solving I-DIDs face the challenge of an exponentially growing space of candidate models ascribed to other agents, over time. Pruning the behaviorally equivalent models is one way toward identifying a(More)
Interactive dynamic influence diagrams (I-DID) are graphical models for sequential decision making in uncertain settings shared by other agents. Algorithms for solving I-DIDs face the challenge of an exponentially growing space of candidate models ascribed to other agents, over time. Pruning behaviorally equivalent models is one way toward minimizing the(More)
Interactive dynamic influence diagram (I-DID) is a recognized graphical framework for sequential multiagent decision making under uncertainty. I-DIDs concisely represent the problem of how an individual agent should act in an uncertain environment shared with others of unknown types. I-DIDs face the challenge of solving a large number of models that are(More)
Planning for ad hoc teamwork is challenging because it involves agents collaborating without any prior coordination or communication. The focus is on principled methods for a single agent to cooperate with others. This motivates investigating the ad hoc teamwork problem in the context of self-interested decision-making frameworks. Agents engaged in(More)
We present a machine learning technique that recognizes patterns of normal movement, using GPS data and time stamps, to gain the ability to detect regions of time containing abnormal movement. We argue people move throughout regions of time in established patterns, and a person’s normal movement can be learned by machines. We use intelligent features(More)
Build time is a vital factor of layered manufacturing as it affects cost of the prototype. Reducing the manufacturing time of the products is an endless process, without compromising the quality of the model. Various approaches have been employed for reducing build time in different RP methodologies. Reduction of build time is a complicated task as one has(More)
I-DIDs suffer disproportionately from the curse of dimensionality dominated by the exponential growth in the number of models over time. Previous methods for scaling I-DIDs identify notions of equivalence between models, such as behavioral equivalence (BE). But, this requires that the models be solved first. Also, model space compression across agents has(More)