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In this paper we adopt general sum stochas tic games as a framework for multiagent re inforcement learning Our work extends pre vious work by Littman on zero sum stochas tic games to a broader framework We de sign a multiagent Q learning method under this framework and prove that it converges to a Nash equilibrium under speci ed condi tions This algorithm(More)
We extend Q-learning to a noncooperative multiagent context, using the framework of generalsum stochastic games. A learning agent maintains Q-functions over joint actions, and performs updates based on assuming Nash equilibrium behavior over the current Q-values. This learning protocol provably converges given certain restrictions on the stage games(More)
Learning in a multiagent environment is complicated by the fact that as other agents learn, the environment effectively changes. Moreover, other agents' actions are often not directly observable, and the actions taken by the learning agent can strongly bias which range of behaviors are encountered. We define the concept of a conjectural equilibrium, where(More)
We present a named entity recognition (NER) system for extracting product attributes and values from listing titles. Information extraction from short listing titles present a unique challenge, with the lack of informative context and grammatical structure. In this work, we combine supervised NER with bootstrapping to expand the seed list, and output(More)
We analyze the problem of learning about other agents in a class of dynamic multiagent systems, where performance of the primary agent depends on behavior of the others. We consider an online version of the problem, where agents must learn models of the others in the course of continual interactions. Various levels of recursive models are implemented in a(More)
Learning in a multiagent environment is complicated by the fact that as other agents learn, the environment eeectively changes. Moreover, other agents' actions are often not directly observable , and the actions taken by the learning agent can strongly bias which range of behaviors are encountered. We deene the concept of a conjectural equilibrium, where(More)
Recommending new items to existing users has remained a challenging problem due to absence of user’s past preferences for these items. The user personalized non-collaborative methods based on item features can be used to address this item cold-start problem. These methods rely on similarities between the target item and user’s previous preferred items.(More)