Equilibrium computation in games is currently considered one of the most challenging issues in AI. In this paper, we provide, to the best of our knowledge, the first algorithm to compute a Selten's extensive-form perfect equilibrium (EFPE) with two--player games. EFPE refines the Nash equilibrium requiring the equilibrium to be robust to slight perturbations of both players' behavioral strategies. Our result puts the computation of an EFPE into the PPAD class, leaving open the question whether or not the problem is hard. Finally, we experimentally evaluate the computational time spent to find an EFPE and some relaxations of EFPE.

Effective access to knowledge within large declarative memory stores is one challenge in the development and understanding of long-living, generally intelligent agents. We focus on a sub-component of this problem: given a large store of knowledge, how should an agent's task-independent memory mechanism respond to an ambiguous cue, one that pertains to multiple previously encoded memories. A large body of cognitive modeling work suggests that human memory retrievals are biased in part by the recency and frequency of past memory access. In this paper, we evaluate the functional benefit of a set of memory retrieval heuristics that incorporate these biases, in the context of the word sense disambiguation task, in which an agent must identify the most appropriate word meaning in response to an ambiguous linguistic cue. In addition, we develop methods to integrate these retrieval biases within a task-independent declarative memory system implemented in the Soar cognitive architecture and evaluate their effectiveness and efficiency in three commonly used semantic concordances.

We prove that it is NP-hard for a coalition of two manipulators to compute how to manipulate the Borda voting rule. This resolves one of the last open problems in the computational complexity of manipulating common voting rules. Because of this NP-hardness, we treat computing a manipulation as an approximation problem where we try to minimize the number of manipulators. Based on ideas from bin packing and multiprocessor scheduling, we propose two new approximation methods to compute manipulations of the Borda rule. Experiments show that these methods significantly outperform the previous best known approximation method. We are able to find optimal manipulations in almost all the randomly generated elections tested. Our results suggest that, whilst computing a manipulation of the Borda rule by a coalition is NP-hard, computational complexity may provide only a weak barrier against manipulation in practice.

We introduce a bidding language for expressing negative value externalities in position auctions for online advertising. The unit-bidder constraints (UBC) language allows a bidder to condition a bid on its allocated slot and on the slots allocated to other bidders. We introduce a natural extension of the Generalized Second Price (GSP) auction, the expressive GSP (eGSP) auction, that induces truthful revelation of constraints for a rich subclass of unit-bidder types, namely downward-monotonic UBC. We establish the existence of envy-free Nash equilibrium in eGSP under a further restriction to a subclass of exclusion constraints, for which the standard GSP has no pure strategy Nash equilibrium. The equilibrium results are obtained by reduction to equilibrium analysis for reserve price GSP (Even-Dar et al. 2008). In considering the winner determination problem, which is NP-hard, we bound the approximation ratio for social welfare in eGSP and provide parameterized complexity results.

The standard approach to computing an optimal mixed strategy to commit to is based on solving a set of linear programs, one for each of the follower's pure strategies. We show that these linear programs can be naturally merged into a single linear program; that this linear program can be interpreted as a formulation for the optimal correlated strategy to commit to, giving an easy proof of a result by von Stengel and Zamir that the leader's utility is at least the utility she gets in any correlated equilibrium of the simultaneous-move game; and that this linear program can be extended to compute optimal correlated strategies to commit to in games of three or more players. (Unlike in two-player games, in games of three or more players, the notions of optimal mixed and correlated strategies to commit to are truly distinct.) We give examples, and provide experimental results that indicate that for 50x50 games, this approach is usually significantly faster than the multiple-LPs approach.

Recently there is an increase in smaller, domain-specific search engines that scour the deep web finding information that general-purpose engines are unable to discover. These search engines play a crucial role in the new generation of search paradigms where federated search engines (FSEs) integrate search results from heterogeneous sources. In this paper we pose, for the first time, the problem to design a revenue mechanism that ensures profits both to individual search engines and FSEs as a mechanism design problem. To this end, we extend the sponsored search auction models and we discuss possibility and impossibility results on the implementation of an incentive compatible mechanism. Specifically, we develop an execution-contingent VCG (where payments depend on the observed click behavior) that satisfies both individual rationality and weak budget balance in expectation.

This paper studies how automated agents can persuade humans to behave in certain ways. The motivation behind such agent's behavior resides in the utility function that the agent's designer wants to maximize and which may be different from the user's utility function. Specifically, in the strategic settings studied, the agent provides correct yet partial information about a state of the world that is unknown to the user but relevant to his decision. Persuasion games were designed to study interactions between automated players where one player sends state information to the other to persuade it to behave in a certain way. We show that this game theory based model is not sufficient to model human-agent interactions, since people tend to deviate from the rational choice. We use machine learning to model such deviation in people from this game theory based model. The agent generates a probabilistic description of the world state that maximizes its benefit and presents it to the users. The proposed model was evaluated in an extensive empirical study involving road selection tasks that differ in length, costs and congestion. Results showed that people's behavior indeed deviated significantly from the behavior predicted by the game theory based model. Moreover, the agent developed in our model performed better than an agent that followed the behavior dictated by the game-theoretical models.

In the existing methods for solving matrix completion, such as singular value thresholding (SVT), soft-impute and fixed point continuation (FPCA) algorithms, it is typically required to repeatedly implement singular value decompositions (SVD) of matrices.When the size of the matrix in question is large, the computational complexity of finding a solution is costly. To reduce this expensive computational complexity, we apply Kronecker products to handle the matrix completion problem. In particular, we propose using Kronecker factorization, which approximates a matrix by the Kronecker product of several matrices of smaller sizes. Weintroduce Kronecker factorization into the soft-impute framework and devise an effective matrix completion algorithm.Especially when the factorized matrices have about the samesizes, the computational complexity of our algorithm is improved substantially.

Automated feature discovery is a fundamental problem in machine learning. Although classical feature discovery methods do not guarantee optimal solutions in general, it has been recently noted that certain subspace learning and sparse coding problems can be solved efficiently, provided the number of features is not restricted a priori. We provide an extended characterization of this optimality result and describe the nature of the solutions under an expanded set of practical contexts. In particular, we apply the framework to a semi-supervised learning problem, and demonstrate that feature discovery can co-occur with input reconstruction and supervised training while still admitting globally optimal solutions. A comparison to existing semi-supervised feature discovery methods shows improved generalization and efficiency.

Transfer learning allows knowledge to be extracted from auxiliary domains and be used to enhance learning in a target domain. For transfer learning to be successful, it is critical to find the similarity between auxiliary and target domains, even when such mappings are not obvious. In this paper, we present a novel algorithm for finding the structural similarity between two domains, to enable transfer learning at a structured knowledge level. In particular, we address the problem of how to learn a non-trivial structural similarity mapping between two different domains when they are completely different on the representation level. This problem is challenging because we cannot directly compare features across domains. Our algorithm extracts the structural features within each domain and then maps the features into the Reproducing Kernel Hilbert Space (RKHS), such that the "structural dependencies" of features across domains can be estimated by kernel matrices of the features within each domain. By treating the analogues from both domains as equivalent, we can transfer knowledge to achieve a better understanding of the domains and improved performance for learning. We validate our approach on synthetic and real-world datasets.