Traditionally, text classifiers are built from labeled training examples. Labeling is usually done manually by human experts (or the users), which is a labor intensive and time co...
We study the recognized open problem of designing revenuemaximizing combinatorial auctions. It is unsolved even for two bidders and two items for sale. Rather than pursuing the pu...
Evaluating text fragments for positive and negative subjective expressions and their strength can be important in applications such as single- or multi- document summarization, do...
We describe a formal framework for diagnosis and repair problems that shares elements of the well known partially observable MDP and cost-sensitive classification models. Our cost...
Michael L. Littman, Nishkam Ravi, Eitan Fenson, Ri...
As artificial intelligence (AI) systems and behavior models in military simulations become increasingly complex, it has been difficult for users to understand the activities of co...
Mark G. Core, H. Chad Lane, Michael van Lent, Dave...
This paper introduces a hierarchical Markov model that can learn and infer a user's daily movements through the commue model uses multiple levels of abstraction in order to b...
Voting (or rank aggregation) is a general method for aggregating the preferences of multiple agents. One voting rule of particular interest is the Kemeny rule, which minimizes the...
Vincent Conitzer, Andrew J. Davenport, Jayant Kala...