Personalized technologies aim to enhance user experience by taking into account users' interests, preferences, and other relevant information. Persuasive technologies aim to modify user attitudes, intentions, or behavior through computer-human dialogue and social influence. While both personalized and persuasive technologies influence user interaction and behavior, we posit that this influence could be significantly increased if the two are combined to create personalized and persuasive systems. For example, the persuasive power of a one-size-fits-all persuasive intervention could be enhanced by considering the user being influenced and their susceptibility to the persuasion being offered. Likewise, personalized technologies could cash in on increased successes, in terms of user satisfaction, revenue, and user experience, if their services used persuasive techniques.

Stable matchings can be computed by deferred acceptance (DA) algorithms. However such algorithms become incomplete when complementarities exist among the agent preferences: they can fail to find a stable matching even when one exists. In this paper we examine stable matching problems arising from labour market with couples (SMP-C). The classical problem of matching residents into hospital programs is an example. Couples introduce complementarities under which DA algorithms become incomplete. In fact, SMP-C is NP-complete. Inspired by advances in SAT and integer programming (IP) solvers we investigate encoding SMP-C into SAT and IP and then using state-of-the-art SAT and IP solvers to solve it. We also implemented two previous DA algorithms. After comparing the performance of these different solution methods we find that encoding to SAT can be surprisingly effective, but that our encoding to IP does not scale as well. Using our SAT encoding we are able to determine that the DA algorithms fail on a non-trivial number of cases where a stable matching exists. The SAT and IP encodings also have the property that they can verify that no stable matching exists, something that the DA algorithms cannot do.

Motivated by recent progress on pricing in the AI literature, we study marketplaces that contain multiple vendors offering identical or similar products and unit-demand buyers with different valuations on these vendors. The objective of each vendor is to set the price of its product to a fixed value so that its profit is maximized. The profit depends on the vendor's price itself and the total volume of buyers that find the particular price more attractive than the price of the vendor's competitors. We model the behaviour of buyers and vendors as a two-stage full-information game and study a series of questions related to the existence, efficiency (price of anarchy) and computational complexity of equilibria in this game. To overcome situations where equilibria do not exist or exist but are highly inefficient, we consider the scenario where some of the vendors are subsidized in order to keep prices low and buyers highly satisfied.

In this paper, we propose a Weakly Supervised Matrix Factorization (WSMF) approach to the problem of image parsing with noisy tags, i.e., segmenting noisily tagged images and then classifying the regions only with image-level labels. Instead of requiring clean but expensive pixel-level labels as strong supervision in the traditional image parsing methods, we take noisy image-level labels as weakly-supervised constraints. Specifically, we first over-segment all the images into multiple regions which are initially labeled based upon the image-level labels. Moreover, from a low-rank matrix factorization viewpoint, we formulate noisily tagged image parsing as a weakly supervised matrix factorization problem. Finally, we develop an efficient algorithm to solve the matrix factorization problem. Experimental results show the promising performance of the proposed WSMF algorithm in comparison with the state-of-the-arts.

This paper considers the kinds of AI systems we want involved in art and art practice. We explore this relationship from three perspectives: as artists interested in expanding and developing our own creative practice; as AI researchers interested in building new AI systems that contribute to the understanding and development of art and art practice; and as audience members interested in experiencing art. We examine the nature of both art practice and experiencing art to ask how AI can contribute. To do so, we review the history of work in intelligent agents which broadly speaking sits in two camps: autonomous agents (systems that can exhibit intelligent behaviour independently) in one, and multi-agent systems (systems which interact with other systems in communities of agents) in the other. In this context we consider the nature of the relationship between AI and Art and introduce two opposing concepts: that of “Heroic AI”, to describe the situation where the software takes on the role of the lone creative hero and “Collaborative AI” where the system supports, challenges and provokes the creative activity of humans. We then set out what we believe are the main challenges for AI research in understanding its potential relationship to art and art practice.

The ability to perform reasoning on inconsistent data is a central problem both for AI and database research. One approach to deal with this situation is consistent query answering, where queries are answered over all possible repairs of the database. In general, the repair may be very distant from the original database. In this work we present a new approach where this distance is bounded and analyze its computational complexity. Our results show that in many (but not all) cases the complexity drops.

A wide range of AI problems, such as sensor placement, active learning, and network influence maximization, require sequentially selecting elements from a large set with the goal of optimizing the utility of the selected subset. Moreover, each element that is picked may provide stochastic feedback, which can be used to make smarter decisions about future selections. Finding efficient policies for this general class of adaptive optimization problems can be extremely hard. However, when the objective function is adaptive monotone and adaptive submodular, a simple greedy policy attains a 1-1/e approximation ratio in terms of expected utility. Unfortunately, many practical objective functions are naturally non-monotone; to our knowledge, no existing policy has provable performance guarantees when the assumption of adaptive monotonicity is lifted. We propose the adaptive random greedy policy for maximizing adaptive submodular functions, and prove that it retains the aforementioned 1-1/e approximation ratio for functions that are also adaptive monotone, while it additionally provides a 1/e approximation ratio for non-monotone adaptive submodular functions. We showcase the benefits of adaptivity on three real-world network data sets using two non-monotone functions, representative of two classes of commonly encountered non-monotone objectives.

Managing inconsistency in DL-Lite knowledge bases where the assertional base is prioritized is a crucial problem in many applications. This is especially true when the assertions are provided by multiple sources having different reliability levels. This paper first reviews existing approaches for selecting preferred repairs. It then focuses on suitable strategies for handling inconsistency in DL-Lite knowledge bases. It proposes new approaches based on the selection of only one preferred repair. These strategies have as a starting point the so-called non-defeated repair and add one of the following principles: deductive closure, consistency, cardinality and priorities. Lastly, we provide a comparative analysis followed by an experimental evaluation of the studied approaches.

We address an ensemble clustering problem, where reliable clusters are locally embedded in given multiple partitions. We propose a new nonnegative matrix factorization (NMF)-based method, in which locally reliable clusters are explicitly considered by using instance-wise weights over clusters. Our method factorizes the input cluster assignment matrix into two matrices H and W, which are optimized by iteratively 1) updating H and W while keeping the weight matrix constant and 2) updating the weight matrix while keeping H and W constant, alternatively. The weights in the second step were updated by solving a convex problem, which makes our algorithm significantly faster than existing NMF-based ensemble clustering methods. We empirically proved that our method outperformed a lot of cutting-edge ensemble clustering methods by using a variety of datasets.

The rapidly growing of Location-based Social Networks (LBSNs) provides a vast amount of check-in data, which enables many services, e.g., point-of-interest (POI) recommendation. In this paper, we study the next new POI recommendation problem in which new POIs with respect to users' current location are to be recommended. The challenge lies in the difficulty in precisely learning users' sequential information and personalizing the recommendation model. To this end, we resort to the Metric Embedding method for the recommendation, which avoids drawbacks of the Matrix Factorization technique. We propose a personalized ranking metric embedding method (PRME) to model personalized check-in sequences. We further develop a PRME-G model, which integrates sequential information, individual preference, and geographical influence, to improve the recommendation performance. Experiments on two real-world LBSN datasets demonstrate that our new algorithm outperforms the state-of-the-art next POI recommendation methods.