Eugene Nudelman

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We present GAMUT^1, a suite of game generators designed for testing game-theoretic algorithms. We explain why such a generator is necessary, offer a way of visualizing relationships between the sets of games supported by GAMUT, and give an overview of GAMUTýs architecture. We highlight the importance of using comprehensive test data by benchmarking(More)
We present two simple search methods for computing a sample Nash equilibrium in a normal-form game: one for 2player games and one for n-player games. We test these algorithms on many classes of games, and show that they perform well against the state of the art– the Lemke-Howson algorithm for 2-player games, and Simplicial Subdivision and Govindan-Wilson(More)
We propose a new approach to understanding the algorithm-specific empirical hardness of NP-Hard optimization problems. In this work we focus on the empirical hardness of the winner determination problem—an optimization problem arising in combinatorial auctions—when solved by ILOG’s CPLEX software. We consider nine widely-used problem distributions and(More)
It is well known that the ratio of the number of clauses to the number of variables in a random k-SAT instance is highly correlated with the instance’s empirical hardness. We consider the problem of identifying such features of random SAT instances automatically using machine learning. We describe and analyze models for three SAT solvers—kcnfs, oksolver and(More)
We study a simple, yet rich subclass of congestion games that we call singleton games. These games are exponentially more compact than general congestion games. In contrast with some other compact subclasses, we show tractability of many natural game-theoretic questions, such as finding a sample or optimal Nash equilibrium. For bestand better-response(More)
Inspired by the success of recent work in the constraint programming community on typical-case complexity, in [3] we developed a new methodology for using machine learning to study empirical hardness of hard problems on realistic distributions. In [2] we demonstrated that this new approach can be used to construct practical algorithm portfolios. In brief,(More)
Is it possible to predict how long an algorithm will take to solve a previously-unseen instance of an NP-complete problem? If so, what uses can be found for models that make such predictions? This article provides answers to these questions and evaluates the answers experimentally. We propose the use of supervised machine learning to build(More)
In this paper we present an approach to the task of generating and resolving referring expressions (REs) for conversational mobile robots. It is based on a spatial knowledge base encompassing both robotand human-centric representations. Existing algorithms for the generation of referring expressions (GRE) try to find a description that uniquely identifies(More)
Hard computational problems are often solvable by multiple algorithms that each perform well on different problem instances. We describe techniques for building an algorithm portfolio that can outperform its constituent algorithms, just as the aggregate classifiers learned by boosting outperform the classifiers of which they are composed. We also provide a(More)
Shoham In this chapter we consider the empirical hardness of the winner determination problem. We identify distribution-nonspecific features of data instances and then use statistical regression techniques to learn, evaluate and interpret a function from these features to the predicted hardness of an instance, focusing mostly on ILOG's CPLEX solver. We also(More)