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We extend Q-learning to a noncooperative multiagent context, using the framework of general-sum 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)
In this paper, we adopt general-sum stochas-tic games as a framework for multiagent reinforcement learning. Our work extends previous work by Littman on zero-sum stochas-tic games to a broader framework. We design a multiagent Q-learning method under this framework, and prove that it converges to a Nash equilibrium under speciied conditions. This algorithm(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)
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 bootstrap-ping to expand the seed list, and output(More)
We propose a novel collaborative filtering method for top- $$n$$ n recommendation task using bicustering neighborhood approach. Our method takes advantage of local biclustering structure for a more precise and localized collaborative filtering. Using several important properties from the field of Formal Concept Analysis, we build user-specific biclusters(More)
We apply collaborative recommendation algorithms to photography in order to produce personalized suggestions for locations in the geocoordinate space where mobile users can take photos. We base our work on a collection of 3 million geotagged, publicly-available Flickr.com digital photos on which we applied a series of steps: first, unique locations are(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. We implement various levels of recursive model in a(More)
Stroke is the third leading cause of death and the principal cause of serious long-term disability in the United States. Accurate prediction of stroke is highly valuable for early intervention and treatment. In this study, we compare the Cox proportional hazards model with a machine learning approach for stroke prediction on the Cardiovascular Health Study(More)