For instance, phrase generation problem involves domains having more than d 10, 000 values. Thus, We propose improved algorithms for defining the we cannot use an algorithm whose time or space complexity most common operations on Multi-Valued Decision is mainly based on Ω(nd), where n is the number of nodes Diagrams (MDDs): creation, reduction, complement, of the MDDs. Therefore, we need to improve the algorithms intersection, union, difference, symmetric performing the main operations on MDDs: creation, reduction difference, complement of union and complement and combinations. of intersection. Then, we show that with these algorithms The new creation algorithm we propose, exploits the origin and thanks to the recent development of an of the definition of the MDD. If the MDD represents an automaton efficient algorithm establishing arc consistency for (like with a regular constraint) or a repeated pattern MDD based constraints (MDD4R), we can simply (like with dynamic programming), then its creation may be solve some problems by modeling them as a set of sped-up.

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.

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.

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.

We consider problems of sequential robot manipulation (aka. combined task and motion planning) where the objective is primarily given in terms of a cost function over the final geometric state, rather than a symbolic goal description. In this case we should leverage optimization methods to inform search over potential action sequences. We propose to formulate the problem holistically as a 1st-order logic extension of a mathematical program: a non-linear constrained program over the full world trajectory where the symbolic state-action sequence defines the (in-)equality constraints. We tackle the challenge of solving such programs by proposing three levels of approximation: The coarsest level introduces the concept of the effective end state kinematics, parametrically describing all possible end state configurations conditional to a given symbolic action sequence. Optimization on this level is fast and can inform symbolic search. The other two levels optimize over interaction keyframes and eventually over the full world trajectory across interactions. We demonstrate the approach on a problem of maximizing the height of a physically stable construction from an assortment of boards, cylinders and blocks.

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.