Commitments help model interactions in multiagent systems in a computationally realizable yet high-level manner without compromising the autonomy and heterogeneity of the member agents. Recent work shows how to combine commitments with goals and apply planning methods to enable agents to determine their actions. However, previous approaches to modeling commitments are confined to propositional representations, which limits their applicability in practical cases. We propose a first-order representation and reasoning technique that accommodates templatic commitments and goals that may be applied repeatedly with differing bindings for domain objects. Doing so not only leads to a more perspicuous modeling, but also supports many practical patterns.

We study the stability of cooperative games played over an interaction network, in a model that was introduced by Myerson ['77]. We show that the cost of stability of such games (i.e., the subsidy required to stabilize the game) can be bounded in terms  of natural parameters of their underlying interaction networks. Specifically, we prove that if the treewidth of the interaction network H is k , then the relative cost of stability of any game played over H is at most k + 1, and if the pathwidth of H is k ', then the relative cost of stability is at most k '. We show that these bounds are tight for all k ≥ 2 and all k ' ≥ 1, respectively.

The increasing use of group discounts has provided opportunities for buying groups with diverse preferences to coordinate their behavior in order to exploit the best offers from multiple vendors. We analyze this problem from the viewpoint of the vendors, asking under what conditions a vendor should adopt a volume-based price schedule rather than posting a fixed price, either as a monopolist or when competing with other vendors. When vendors have uncertainty about buyers' valuations specified by a known distribution, we show that a vendor is always better off posting a fixed price, provided that buyers' types are i.i.d. and that other vendors also use fixed prices. We also show that these assumptions cannot be relaxed: if buyers are not i.i.d., or other vendors post discount schedules, then posting a schedule may yield higher profit for the vendor. We provide similar results under a distribution-free uncertainty model, where vendors minimize their maximum regret over all type realizations.

We motivate the integration of programming by example and natural language programming by developing a system for specifying programs for simple text editing operations based on regular expressions. The programs are described with unconstrained natural language instructions, and providing one or more examples of input/output. We show that natural language allows the system to deduce the correct program much more often and much faster than is possible with the input/output example(s) alone, showing that natural language programming and programming by example can be combined in a way that overcomes the ambiguities that both methods suffer from individually, while providing a more natural interface to the user.

This paper presents a new approach to representation discovery in reinforcement learning (RL) using basis adaptation. We introduce a general framework for basis adaptation as {\em nonlinear separable least-squares value function approximation} based on finding Frechet gradients of an error function using variable projection functionals. We then present a scalable proximal gradient-based approach for basis adaptation using the recently proposed mirror-descent framework for RL. Unlike traditional temporal-difference (TD) methods for RL, mirror descent based RL methods undertake proximal gradient updates of weights in a dual space, which is linked together with the primal space using a Legendre transform involving the gradient of a strongly convex function. Mirror descent RL can be viewed as a proximal TD algorithm using Bregman divergence as the distance generating function. We present a new class of regularized proximal-gradient based TD methods, which combine feature selection through sparse L1 regularization and basis adaptation. Experimental results are provided to illustrate and validate the approach.

Images are usually associated with multiple labels and comprised of multiple views, due to each image containing several objects (e.g. a pedestrian, bicycle and tree) and multiple visual features (e.g. color, texture and shape). Currently available tools tend to use either labels or features for classification, but both are necessary to describe the image properly. There have been recent successes in using vector-valued functions, which construct matrix-valued kernels, to explore the multi-label structure in the output space. This has motivated us to develop multi-view vector-valued manifold regularization (MV$^3$MR) in order to integrate multiple features. MV$^3$MR exploits the complementary properties of different features, and discovers the intrinsic local geometry of the compact support shared by different features, under the theme of manifold regularization. We validate the effectiveness of the proposed MV$^3$MR methodology for image classification by conducting extensive experiments on two challenge datasets, PASCAL VOC' 07 and MIR Flickr.

This paper presents a unified framework for intra-view and inter-view constraint propagation on multi-view data. Pairwise constraint propagation has been studied extensively, where each pairwise constraint is defined over a pair of data points from a single view. In contrast, very little attention has been paid to inter-view constraint propagation, which is more challenging since each pairwise constraint is now defined over a pair of data points from different views. Although both intra-view and inter-view constraint propagation are crucial for multi-view tasks, most previous methods can not handle them simultaneously. To address this challenging issue, we propose to decompose these two types of constraint propagation into semi-supervised learning subproblems so that they can be uniformly solved based on the traditional label propagation techniques. To further integrate them into a unified framework, we utilize the results of intra-view constraint propagation to adjust the similarity matrix of each view and then perform inter-view constraint propagation with the adjusted similarity matrices. The experimental results in cross-view retrieval have shown the superior performance of our unified constraint propagation.

Recent years have witnessed the success of hashingtechniques in approximate nearest neighbor search. Inpractice, multiple hash tables are usually employed toretrieve more desired results from all hit buckets ofeach table. However, there are rare works studying theunified approach to constructing multiple informativehash tables except the widely used random way. In thispaper, we regard the table construction as a selectionproblem over a set of candidate hash functions. Withthe graph representation of the function set, we proposean efficient solution that sequentially applies normal-ized dominant set to finding the most informative andindependent hash functions for each table. To furtherreduce the redundancy between tables, we explore thereciprocal hash tables in a boosting manner, where thehash function graph is updated with high weights em-phasized on the misclassified neighbor pairs of previoushash tables. The construction method is general andcompatible with different types of hashing algorithmsusing different feature spaces and/or parameter settings.Extensive experiments on two large-scale benchmarksdemonstrate that the proposed method outperforms bothnaive construction method and state-of-the-art hashingalgorithms, with up to 65.93% accuracy gains.

Hierarchical classification (HC) plays an significant role in machine learning and data mining. However, most of the state-of-the-art HC algorithms suffer from high computational costs. To improve the performance of solving, we propose a stochastic perceptron (SP) algorithm in the large margin framework. In particular, a stochastic choice procedure is devised to decide the direction of next iteration. We prove that after finite iterations the SP algorithm yields a sub-optimal solution with high probability if the input instances are separable. For large-scale and high-dimensional data sets, we reform SP to the kernel version (KSP), which dramatically reduces the memory space needed. The KSP algorithm has the merit of low space complexity as well as low time complexity. The experimental results show that our KSP approach achieves almost the same accuracy as the contemporary algorithms on the real-world data sets, but with much less CPU running time.

The implication problem of probabilistic conditional independencies is investigated in the presence of missing data. Here, graph separation axioms fail to hold for saturated conditional independencies, unlike the known idealized case with no missing data. Several axiomatic, algorithmic, and logical characterizations of the implication problem for saturated conditional independencies are established. In particular, equivalences are shown to the implication problem of a propositional fragment under Levesque's situations, and that of Lien's class of multivalued database dependencies under null values.