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Li C.M.,University of Picardie Jules Verne | Li C.M.,Huazhong University of Science and Technology | Zhu Z.,University of Picardie Jules Verne | Manya F.,Artificial Intelligence Research Institute IIIA | Simon L.,French Institute for Research in Computer Science and Automation
Artificial Intelligence

MinSAT is the problem of finding a truth assignment that minimizes the number of satisfied clauses in a CNF formula. When we distinguish between hard and soft clauses, and soft clauses have an associated weight, then the problem, called Weighted Partial MinSAT, consists in finding a truth assignment that satisfies all the hard clauses and minimizes the sum of weights of satisfied soft clauses. In this paper we describe a branch-and-bound solver for Weighted Partial MinSAT equipped with original upper bounds that exploit both clique partitioning algorithms and MaxSAT technology. Then, we report on an empirical investigation that shows that solving combinatorial optimization problems by reducing them to MinSAT is a competitive generic problem solving approach when solving MaxClique and combinatorial auction instances. Finally, we investigate an interesting correlation between the minimum number and the maximum number of satisfied clauses on random CNF formulae. © 2012 Elsevier B.V. Source

Ontanon S.,Artificial Intelligence Research Institute IIIA | Zhu J.,School of Visual Arts
Proceedings of the 7th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2011

Analogy-based Story Generation (ASG) is a relatively under-explored approach for story generation and computational narrative. In this paper, we present the SAM (Story Analogies through Mapping) algorithm as our attempt to expand the scope and complexity of stories generated by ASG. Comparing with existing work and our prior work, there are two main contributions of SAM: it employs 1) analogical reasoning both at the specific story content and general domain knowledge levels, and 2) temporal reasoning about the story (phase) structure in order to generate more complex stories. We illustrate SAM through a few example stories. Copyright © 2011, Association for the Advancement of Artificial. Source

Mares J.,Autonomous University of Barcelona | Torra V.,Artificial Intelligence Research Institute IIIA
Knowledge-Based Systems

Social networks have become an essential ingredient of interpersonal communication in the modern world. They enable users to express and share common interests, comment upon everyday events with all the people with whom they are connected. Indeed, the growth of social media has been rapid and has resulted in the adoption of social networks to meet specific communities of interest. However, this shared information space can prove to be dangerous in respect of user privacy issues. In addition to explicit "posts" there is much implicit semantic information that is not explicitly given in the posts that the user shares. For these and other reasons, the protection of information pertaining to each user needs to be supported. In this paper, we present a novel approach wherein the extraction of implicit and explicit information is derived from a small sample of a popular social network (Twitter) that seeks also to preserve user's privacy whilst maintaining information utility. © 2013 Elsevier B.V. All rights reserved. Source

Ontanon S.,Drexel University | Plaza E.,Artificial Intelligence Research Institute IIIA
AI Communications

We present a new approach lo learn from relational data based on re-representation of the examples. This approach, called property-based re-representation is based on a new analysis of the structure of refinement graphs used in ILP and relational learning in general. This analysis allows the characterization of relational examples by a set of multi-relational patterns called properties. Using them, we perform a property-based re-representation of relational examples that facilitates the development of relational learning techniques. Additionally, we show the usefulness of re-representation with a collection of experiments in the context of nearest neighbor classification. © 2015 - IOS Press and the authors. Source

Fernandez C.,University of Lleida | Manya F.,Artificial Intelligence Research Institute IIIA | Mateu C.,University of Lleida | Sole-Mauri F.,University of British Columbia
Environmental Modelling and Software

In a world where resources are scarce and urban areas consume the vast majority of these resources, it is vital to make cities greener and more sustainable. A smart city is a city in which information and communications technology are merged with traditional infrastructures, coordinated and integrated using new digital technologies. The increasing amount of waste generated, and the collection and treatment of waste poses a major challenge to modern urban planning in general, and to smart cities in particular. To cope with this problem, automated vacuum waste collection (AVWC) uses air suction on a closed network of underground pipes to transport waste from the drop off points scattered throughout the city to a central collection point, reducing greenhouse gas emissions and the inconveniences of conventional methods (odours, noise, etc.). Since a significant part of the cost of operating AVWC systems is energy consumption, we have developed a model with the aim of applying constraint programming technology to schedule the daily emptying sequences of the drop off points in such a way that energy consumption is minimized. In this paper we describe how the problem of deciding the drop off points that should be emptied at a given time can be modeled as a constraint integer programming (CIP) problem. Moreover, we report on experiments using real data from AVWC systems installed in different cities that provide empirical evidence that CIP offers a suitable technology for reducing energy consumption in AVWC. © 2013 Elsevier Ltd. Source

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