This paper deals with the distributed processing in the search for an optimum classification model using evolutionary product unit neural networks. For this distributed search we used a cluster of computers. Our objective is to obtain a more efficient design than those net architectures which do not use a distributed process and which thus result in simpler designs. In order to get the best classification models we use evolutionary algorithms to train and design neural networks, which require a very time consuming computation. The reasons behind the need for this distribution are various. It is complicated to train this type of nets because of the difficulty entailed in determining their architecture due to the complex error surface. On the other hand, the use of evolutionary algorithms involves running a great number of tests with different seeds and parameters, thus resulting in a high computational cost

Collaborative filtering is a rapidly advancing research area. Every year several new techniques are proposed and yet it is not clear which of the techniques work best and under what conditions. In this paper we conduct a study comparing several collaborative filtering techniques -- both classic and recent state-of-the-art -- in a variety of experimental contexts. Specifically, we report conclusions controlling for number of items, number of users, sparsity level, performance criteria, and computational complexity. Our conclusions identify what algorithms work well and in what conditions, and contribute to both industrial deployment collaborative filtering algorithms and to the research community.

The Association for the Advancement of e are pleased to introduce Worldwide AI -- a new column in AI Magazine Artificial Intelligence now serves a global audience, and our members, meeting participants, councilors, and officers reside in countries throughout the world. Worldwide AI is designed to meet our expanded audience's interests. In the columns that will appear in this and forthcoming issues, readers will find a continuing source of news and information on significant research projects and accomplishments, academic and community events, and experiences fielding notable applications of AI. We expect that increased awareness about AI activities around the world will fuel new opportunities for communication and collaboration. The inaugural columns in this issue of Worldwide AI describe artificial intelligence trends in India and South Africa.

Posters, the AAAI/SIGART Doctoral of AAAI; submit papers to the conference Consortium, and more!

Conference on Case-Based Reasoning (ICCBR) has continuously been the preeminent international meeting on case-based reasoning (CBR). Through 2009, ICCBR had been a biennial conference, held in alternation with its sister conference, the European Conference on Case-Based Reasoning (ECCBR), which was located in Europe. At the 2009 ICCBR, the ICCBR Program Committee elected to extend an offer of consolidation with ECCBR. The offer was accepted by the ECCBR 2010 organizers and they considered it approved by the ECCBR community, as the two conferences shared a majority of Program Committee members. Therefore, starting in 2010, ICCBR and ECCBR are merged in a single conference series, called ICCBR.

ICINCO 2012 will be held July 28-31, 2012 in Rome, Italy This page includes forthcoming AAAI sponsored conferences, conferences presented Sixth International RuleML Symposium by AAAI Affiliates, and conferences held in cooperation with AAAI. RuleML-2012 will be Magazine also maintains a calendar listing that includes nonaffiliated conferences held August 27-31, 2012 in Montpellier, at www.aaai.org/Magazine/calendar.php. Knowledge Engineering and Knowledge ICWSM-12 will be held June 4-7 at Flairs-2012 will be held May 23-25, Management. AAAI-12 will be Representation and Reasoning. Twenty-Fourth Innovative Applications Twenty-Second International Conference of Artificial Intelligence Conference. on Automated Planning and IAAI-12 will be held July Scheduling.

The AAAI-11 workshop program was held Sunday and Monday, August 7–18, 2011, at the Hyatt Regency San Francisco in San Francisco, California USA. The AAAI-11 workshop program included 15 workshops covering a wide range of topics in artificial intelligence. The titles of the workshops were Activity Context Representation: Techniques and Languages; Analyzing Microtext; Applied Adversarial Reasoning and Risk Modeling; Artificial Intelligence and Smarter Living: The Conquest of Complexity; AI for Data Center Management and Cloud Computing; Automated Action Planning for Autonomous Mobile Robots; Computational Models of Natural Argument; Generalized Planning; Human Computation; Human-Robot Interaction in Elder Care; Interactive Decision Theory and Game Theory; Language-Action Tools for Cognitive Artificial Agents: Integrating Vision, Action and Language; Lifelong Learning; Plan, Activity, and Intent Recognition; and Scalable Integration of Analytics and Visualization. This article presents short summaries of those events.

According to a 2008 OECD review of national policies for education in South Africa, typically only 15 percent to 18 percent of secondary school students who sit for their final year exams every year qualify automatically for university-level education; and this number seems to be decreasing as more students choose to complete subjects on so-called standard grade instead of higher grade, a trend that is especially apparent for mathematics and science, the two fields with critical skills shortages in the country. The South African tertiary education sector is quite small for a country with a population of around 50 million, with 11 "traditional" universities, 6 technical universities, and 6 comprehensive universities. The latter university types focus on more technical or vocational education. The public sector also funds 16 research institutions. In spite of these obstacles, South African universities participate in world-class research activities in many fields and range among the best on the African continent.

Of course, this is far from the first attempt to plot a course toward human-level AGI: arguably this was the goal of the founders of the field of artificial intelligence in the 1950s, and has been pursued by a steady stream of AI researchers since, even as the majority of the AI field has focused its attention on more narrow, specific subgoals. The ideas presented here build on the ideas of others in innumerable ways, but to review the history of AI and situate the current effort in the context of its predecessors would require a much longer article than this one. Thus we have chosen to focus on the results of our AGI roadmap discussions, acknowledging in a broad way the many debts owed to many prior researchers. References to the prior literature on evaluation of advanced AI systems are given by Laird (Laird et al. 2009) and Geortzel and Bugaj (2009), which may in a limited sense be considered prequels to this article. We begin by discussing AGI in general and adopt a pragmatic goal for measuring progress toward its attainment. An initial capability landscape for AGI The heterogeneity of general intelligence in will be presented, drawing on major themes from humans makes it practically impossible to develop developmental psychology and illuminated by a comprehensive, fine-grained measurement system mathematical, physiological, and informationprocessing for AGI. While we encourage research in defining perspectives. The challenge of identifying such high-fidelity metrics for specific capabilities, appropriate tasks and environments for measuring we feel that at this stage of AGI development AGI will be taken up. Several scenarios will a pragmatic, high-level goal is the best we can be presented as milestones outlining a roadmap agree upon. I advocate beginning with a system that has minimal, although extensive, built-in capabilities. Many variant approaches have been proposed A classic example of the narrow AI approach was for achieving such a goal, and both the AI and AGI IBM's Deep Blue system (Campbell, Hoane, and communities have been working for decades on Hsu 2002), which successfully defeated world chess the myriad subgoals that would have to be champion Gary Kasparov but could not readily achieved and integrated to deliver a comprehensive apply that skill to any other problem domain without AGI system.

Modern SAT solvers are routinely used as core solving engines in vast numbers of different AI and industrial applications. In this short article, we will provide an overview of the SAT solver competitions. The solvers), and another one based on wall clock time, second SAT competition took place during the second which promotes solvers using all available Dimacs challenge in 1993 (Johnson and Trick resources to answer as quickly as possible (for 1996). Another SAT competition took place in answers incorrectly if it reports satisfiable but Beijing in 1996, organized by James Crawford. Each survey propagation (Braunstein and Zecchina category is defined through the type of instances 2004), a new approach to efficiently solve randomly used as benchmarks.