We consider the task of learning latent community structure from multiple correlated networks. First, we study the problem of learning the latent vertex correspondence between two edge-correlated stochastic block models, focusing on the regime where the average degree is logarithmic in the number of vertices. We derive the precise information-theoretic threshold for exact recovery: above the threshold there exists an estimator that outputs the true correspondence with probability close to 1, while below it no estimator can recover the true correspondence with probability bounded away from 0. As an application of our results, we show how one can exactly recover the latent communities using \emph{multiple} correlated graphs in parameter regimes where it is information-theoretically impossible to do so using just a single graph.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. FBI investigators identified Robert Jacob Hoopes as a suspect in the injury of an ICE officer during protests in Portland, Ore., using facial recognition software, according to a criminal complaint from the case. In the criminal complaint, an unidentified FBI special agent said that a photo shared on OregonLive.com -- the online version of The Oregonian -- was put into "commercially available facial recognition software." The software allegedly provided 30 possible comparison photos from public databases. FBI Portland reviewed the photos and found one from a Reed College SmugMug page called "Canyon Day April '23," in which a tattoo on the suspect's forearm is visible.

This new conference series promotes multidisciplinary research on tools and methodologies for efficiently capturing knowledge from a variety of sources and creating representations that can be (or eventually can be) useful for reasoning. The conference attracted researchers from diverse areas of AI, including knowledge representation, knowledge acquisition, intelligent user interfaces, problem solving and reasoning, planning, agents, text extraction, and machine learning. Knowledge acquisition has been a challenging area of research in AI, with its roots in early work to develop expert systems. Driven by the modern internet culture and knowledge-based industries, the study of knowledge capture has a renewed importance. Although there has been considerable work over the years in the area, activities have been distributed across several distinct research communities.

The workshop focused on possible solutions to both technical and organizational challenges to mobility and manipulation research. This article presents the highlights of that discussion along with the content of the accompanying exhibits. Fortunately, these applications can be successful through simple repetitive behaviors or remote human operation. However, useful autonomy needed for operation in general situations requires advanced mobility and manipulation. Opening doors, retrieving specific items, and maneuvering in cluttered environments are required for useful deployment in anything but the most controlled environment. The mobile manipulation skills necessary to perform tasks in arbitrary environments may not result from current approaches to robotics and AI. Moving toward true robot autonomy may require new paradigms, hardware, and ways of thinking. The goal of the AAAI 2008 Workshop on Mobility and Manipulation was not only to demonstrate current research successes to the AAAI community but also to road-map future mobility and manipulation challenges that create synergies between artificial intelligence and robotics. The half-day workshop included both a session on the exhibits and a panel discussion. The panel consisted of five prominent researchers who led a discussion of future directions for mobility and manipulation research.

Developing agents that could perceive the world, reason about what they perceive in relation to their own goals and acts, has been the Holy Grail of AI. Early attempts at such holistic intelligence (for example, SRI International's AI researchers turned their attention to component technologies for structuring a single agent, such as planning, knowledge representation, diagnosis, and learning. Although most of AI research was focused on single-agent issues, a small number of AI researchers gathered at the Massachusetts Institute of Technology Endicott House in 1980 for the First Workshop on Distributed AI. The main scientific goal of distributed AI (DAI) is to understand the principles underlying the behavior of multiple entities in the world, called agents and their interactions. The discipline is concerned with how agent interactions produce overall multiagent system (MAS) behavior.

Simulations are an excellent tool for studying AI. However, the simulation technology in use by, and designed for, the AI community often fails to take advantage of much of the work in the larger simulation community to produce stable, repeatable, and efficient simulations. The "thinking time" of an agent is tracked and reflected in the results of the agents' actions. Simulations are an excellent tool for studying AI. They can allow the systematic modification of parameters of the environment, execute the large number of trials often required for machine learning, and facilitate the interaction of agents created by different research groups.

AI (see AI Magazine, Vol. 6, All of the contributions in this book have a practical slant, showing how Al has been successfully applied to a wide spectrum of domains and tasks. They provide an excellent sampling of the types of applications coming on line. Systems architectures and development strategies are addressed along with tactical issues, payback data, and real benefits. To order call toll-free .I -800-356-0343 or 617-625-8569 Fax orders.

IAAI has been held annually since 1989 and has been collocated with the national (or international) AI conference since 1991. The proceedings were published in book form through 1992. Since 1993, a conference proceedings volume has been published, and selected papers have been republished as articles in AI Magazine. This introduction briefly discusses the 1995 IAAI award winners and presents goals and plans for next year's conference. IAAI features real, deployed AI applications, selected for their innovation.

This workshop brought together 20 computer scientists, psychologists, and human-computer interaction (HCI) researchers to exchange results and views on human error and judgment bias. Human error is typically studied when operators undertake actions, but judgment bias is an issue in thinking rather than acting. Both topics are generally ignored by the HCI community, which is interested in designs that eliminate human error and bias tendencies. As a result, almost no one at the workshop had met before, and the discussion for most participants was novel and lively. Many areas of previously unexamined overlap were identified.

The Third International Conference on Artificial Intelligence Planning Systems (AIPS-96) was held in Edinburgh, Scotland, from 29 to 31 May 1996. The main gathering of researchers in AI and planning and scheduling, the conference promoted the practical applications of planning technologies. Details of the conference papers and sessions are provided as well as information on the Defense Advanced Research Projects Agency-Rome Laboratory Planning Initiative. Previous conferences were held at the University of Maryland in June 1992 (AIPS-92), organized by Jim Hendler and Drew McDermott, and the University of Chicago in June 1994 (AIPS-94), organized by Kristian Hammond. The generation of plans and related fields, such as scheduling, resource allocation, and reasoning about action, have a long research tradition in AI.