Recent developments in fitness landscape analysis include the study of Local Optima Networks (LON) and applications of the Elementary Landscapes theory. This paper represents a first step at combining these two tools to explore their ability to forecast the performance of search algorithms. We base our analysis on the Quadratic Assignment Problem (QAP) and conduct a large statistical study over 600 generated instances of different types. Our results reveal interesting links between the network measures, the autocorrelation measures and the performance of heuristic search algorithms.

Generalized relational theories with null values in the sense of Reiter are first-order theories that provide a semantics for relational databases with incomplete information. In this paper we show that any such theory can be turned into an equivalent logic program, so that models of the theory can be generated using computational methods of answer set programming. As a step towards this goal, we develop a general method for calculating stable models under the domain closure assumption but without the unique name assumption.

Local Optima Networks (LONs) have been recently proposed as an alternative model of combinatorial fitness landscapes. The model compresses the information given by the whole search space into a smaller mathematical object that is the graph having as vertices the local optima and as edges the possible weighted transitions between them. A new set of metrics can be derived from this model that capture the distribution and connectivity of the local optima in the underlying configuration space. This paper departs from the descriptive analysis of local optima networks, and actively studies the correlation between network features and the performance of a local search heuristic. The NK family of landscapes and the Iterated Local Search metaheuristic are considered. With a statistically-sound approach based on multiple linear regression, it is shown that some LONs' features strongly influence and can even partly predict the performance of a heuristic search algorithm. This study validates the expressive power of LONs as a model of combinatorial fitness landscapes.

We are proud to introduce this special issue of the Journal of Theory and Practice of Logic Programming (TPLP), dedicated to the full papers accepted for the 28th International Conference on Logic Programming (ICLP). The ICLP meetings started in Marseille in 1982 and since then constitute the main venue for presenting and discussing work in the area of logic programming.

Quick Summary is an innovate implementation of an automatic document summarizer that inputs a document in the English language and evaluates each sentence. The scanner or evaluator determines criteria based on its grammatical structure and place in the paragraph. The program then asks the user to specify the number of sentences the person wishes to highlight. For example should the user ask to have three of the most important sentences, it would highlight the first and most important sentence in green. Commonly this is the sentence containing the conclusion. Then Quick Summary finds the second most important sentence usually called a satellite and highlights it in yellow. This is usually the topic sentence. Then the program finds the third most important sentence and highlights it in red. The implementations of this technology are useful in a society of information overload when a person typically receives 42 emails a day (Microsoft). The paper also is a candid look at difficulty that machine learning has in textural translating. However, it speaks on how to overcome the obstacles that historically prevented progress. This paper proposes mathematical meta-data criteria that justify the place of importance of a sentence. Just as tools for the study of relational symmetry in bio-informatics, this tool seeks to classify words with greater clarity. "Survey Finds Workers Average Only Three Productive Days per Week." Microsoft News Center. Microsoft. Web. 31 Mar. 2012.

A key feature of the AAMAS conference is its emphasis on ties to real-world applications. The focus of this article is to provide a broad overview of application-focused papers published at the AAMAS 2010 and 2011 conferences. More specifically, recent applications at AAMAS could be broadly categorized as belonging to research areas of security, sustainability and safety. We outline the domains of applications, key research thrusts underlying each such application area, and emerging trends.

He has been chairman/president the MIT Artificial Intelligence Lab. Board of Trustees, as well as treasurer 100 Americans most likely to shape Manuela Veloso, incoming AAAI President, of SSAISB and ECCAI. He is presently the next century; TIME Digital selected and Eric Horvitz, AAAI Past editor-in-chief of the AAAI Press, Spatial her as a member of the Cyber-Elite; President and Awards Committee Cognition and Computation, and the World Economic Forum honored Chair, presented the AAAI Awards in the Artificial Intelligence Journal. He was her with the title Global Leader for Tomorrow; August at AAAI-12 in Toronto. She holds bachelor's and or 1-650-328-3123.)

I Have a Robot, and I'm Not Afraid to Use It! The AAMAS community is investing efforts to encourage robotics research within itself. An annual robotics special track, an associated robotics workshop (Autonomous Robots and Multirobot Systems), and a series of exciting AAMAS-sponsored plenary speakers and awards over a number of years are drawing roboticists in. The number of robotics papers is increasing. There are fruitful interactions with the other communities within AAMAS, such as virtual agents, game theory, and machine learning. Robots are being used both to inspire AAMAS research as well as to conduct it. I posit that the growing success of robotics at AAMAS is due not only to the nurturing efforts of the AAMAS community, but mainly to the increasing recognition of an important, deeper, truth: robots are agents.

Negotiation is a complex emotional decision-making process aiming to reach an agreement to exchange goods or services. From an agent technological perspective creating negotiating agents that can support humans with their negotiations is an interesting challenge. Already more than a decade, negotiating agents can outperform human beings (in terms of deal optimality) if the negotiation space is well-understood. However, the inherent semantic problem and the emotional issues involved make that negotiation cannot be handled by artificial intelligence alone, and a human-machine collaborative system is required. This article presents research goals, challenges, and an approach to create the next generation of negotiation support agents.

This article gives an introduction to agent-based modeling and simulation (ABMS). After a general discussion about modeling and simulation, we address the basic concept of ABMS, focusing on its generative and bottom-up nature, its advantages as well as its pitfalls. The subsequent part of the article deals with application-oriented aspects, including selected tools and well-known applications. In order to illustrate the benefits of using ABMS, we focus on several aspects of a well-known area related to simulation of complex systems, namely traffic. At the end, a brief look into future challenges is given.