Doran Chakraborty

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In the Trading Agent Competition Ad Auctions Game, agents compete to sell products by bidding to have their ads shown in a search engine’s sponsored search results. We report on the winning agent from the first (2009) competition, TacTex. TacTex operates by estimating the full game state from limited information, using these estimates to make predictions,(More)
This paper introduces Learn Structure and Exploit RMax (LSE-RMax), a novel model based structure learning algorithm for ergodic factored-state MDPs. Given a planning horizon that satisfies a condition, LSE-RMax provably guarantees a return very close to the optimal return, with a high certainty, without requiring any prior knowledge of the in-degree of the(More)
In recent years, great strides have been made towards creating autonomous agents that can learn via interaction with their environment. When considering just an individual agent, it is often appropriate to model the world as being stationary, meaning that the same action from the same state will always yield the same (possibly stochastic) effects. However,(More)
This paper focuses on learning in the presence of a Markovian teammate in Ad hoc teams. A Markovian teammate’s policy is a function of a set of discrete feature values derived from the joint history of interaction, where the feature values transition in a Markovian fashion on each time step. We introduce a novel algorithm “Learning to Cooperate with a(More)
Knowledge transfer between expert and novice agents is a challenging problem given that the knowledge representation and learning algorithms used by the novice learner can be fundamentally different from and inaccessible to the expert trainer. We are particularly interested in team tasks, robotic or otherwise, where new teammates need to replace currently(More)
The problem of decentralized control occurs frequently in realistic domains where agents have to cooperate to achieve a universal goal. Planning for domain-level joint strategy takes into account the uncertainty of the underlying environment in computing near-optimal joint-strategies that can handle the intrinsic domain uncertainty. However, uncertainty(More)
Future agent applications will increasingly represent human users autonomously or semi-autonomously in strategic interactions with similar entities. Hence, there is a growing need to develop algorithmic approaches that can learn to recognize commonalities in opponent strategies and exploit such commonalities to improve strategic response. Recently a(More)