This paper describes a novel approach based on a combination of techniques in AI, parallel computing, and network science to address an important problem in social sciences and public health: planning and responding in the event of epidemics. Spread of infectious disease is an important societal problem -- human behavior, social networks, and the civil infrastructures all play a crucial role in initiating and controlling such epidemic processes.  We specifically consider the economic and social effects of realistic interventions  proposed and adopted by public health officials and behavioral changes  of  private citizens in the event of a ``flu-like'' epidemic. Our results provide new insights for developing robust public policies that can prove useful for epidemic planning.

Realistic rendering is the process of generating a 2D image from an abstract description of a 3D scene, aiming at achieving the quality of a photo. The quality of the generated image depends on the accuracy with which the employed render method simulates the behaviour of the light particles through the scene. According to the current practice, it is up to the user to choose optimal settings of input parameters for these methods in terms of time-efficiency, as well as image quality. This is an iterative trial and error process, even for expert users. This paper describes a novel approach based on techniques from the field of data mining and genetic computing to assist the user in the selection of render parameters. Experimental results are presented which show the benefits of this approach.

In this paper, we present a prototype application — the Technology Trend Tracker — to measure the reality of technology trends of interest using information on the Web to inform decisions such as when to develop training, when to invest in expertise, and more. This prototype performs this task by integrating several artificial intelligence technologies in an innovative way. These technologies include rich semantic representations, a natural language understanding module, and a flexible semantic matcher. We use our system to augment Accenture's annual technology vision survey and show how our system performs well on measuring the reality of technology trends from this survey. We also show why our system performs well through an ablation study.

Considerable progress has been made over the last 15 years on building adaptive control systems to assist pilots in flying damaged aircraft. Once a pilot has regained control of a damaged aircraft, the next problem is to determine the best site for an emergency landing.  In general, the decision depends on many factors including the actual control envelope of the aircraft, distance to the site, weather en route, characteristics of the approach path, characteristics of the runway or landing site, and emergency facilities at the site.  All of these influence the risk to the aircraft, to the passengers and crew, and to people and property on the ground.  We describe an emergency landing planner that takes these various factors into consideration and proposes possible routes and landing sites to the pilot, ordering them according to estimated risk.   We give an overview of the system architecture and input data, describe our modeling of risk, describe how we search the space of landing sites and routes, and give a preliminary performance assessment for characteristic emergency scenarios using the current research prototype.

The goal of the Innovative Applications of Artificial Intelligence (IAAI) conference is to highlight new, innovative, systems and application areas of AI technology and to point out the often-overlooked difficulties involved in deploying complex technology to end users. Those of us who have ventured out of the realm of pure research and tried to build applications to be used by our fellow humans realize that it takes a lot more than just brilliant algorithms to make an application survive in the real world. Each application that succeeds is worth celebrating and the teams behind them are due wholehearted congratulations. It is in this spirit that we bring you this special issue covering select applications from the IAAI conference held last year in Chicago.

Data analysis and data mining are concerned with unsupervised pattern finding and structure determination in data sets. The data sets themselves are explicitly linked as a form of representation to an observational or otherwise empirical domain of interest. "Structure" has long been understood as symmetry which can take many forms with respect to any transformation, including point, translational, rotational, and many others. Beginning with the role of number theory in expressing data, we show how we can naturally proceed to hierarchical structures. We show how this both encapsulates traditional paradigms in data analysis, and also opens up new perspectives towards issues that are on the order of the day, including data mining of massive, high dimensional, heterogeneous data sets. Linkages with other fields are also discussed including computational logic and symbolic dynamics. The structures in data surveyed here are based on hierarchy, represented as p-adic numbers or an ultrametric topology.

Presentations are one of the most common and effective ways of communicating the overview of a work to the audience. Given a technical paper, automatic generation of presentation slides reduces the effort of the presenter and helps in creating a structured summary of the paper. In this paper, we propose the framework of a novel system that does this task. Any paper that has an abstract and whose sections can be categorized under introduction, related work, model, experiments and conclusions can be given as input. As documents in LaTeX are rich in structural and semantic information we used them as input to our system. These documents are initially converted to XML format. This XML file is parsed and information in it is extracted. A query specific extractive summarizer has been used to generate slides. All graphical elements from the paper are made well use of by placing them at appropriate locations in the slides. These slides are presented in the document order.

This summer's AAAI Conference on Artificial Intelligence (AAAI-08) and its sister Conference on Innovative Applications of AI (IAAI-08) continued their long tradition of being a focal point of AI. This year's conferences were held in Chicago at the Hyatt Regency McCormick Place, July 13-17, 2008. The multidimensional conference offerings included nine invited talks, 251 technical papers, 22 innovative applications of AI papers, three competitions (poker, AI video, and general game playing), three special tracks (AI and the web, integrated intelligence, and physically grounded AI), 15 tutorials, 15 workshops, and 11 intelligent system demonstrations, as well as a number of awards, a doctoral consortium, student poster session and programs, and a vendor exhibit. This translated into a plethora of choices for the 921 conference attendees. An additional 175 people exclusively attended the tutorials, workshops, or exhibit.

This article explains the benefits of preferences for AI systems and draws a picture of current AI research on preference handling. It thus provides an introduction to the topics covered by this special issue on preference handling.

Automated Planning is an old area of AI that focuses on the development of techniques for finding a plan that achieves a given goal from a given set of initial states as quickly as possible. In most real-world applications, users of planning systems have preferences over the multitude of plans that achieve a given goal. These preferences allow to distinguish plans that are more desirable from those that are less desirable. Planning systems should therefore be able to construct high-quality plans, or at the very least they should be able to build plans that have a reasonably good quality given the resources available.In the last few years we have seen a significant amount of research that has focused on developing rich and compelling languages for expressing preferences over plans. On the other hand, we have seen the development of planning techniques that aim at finding high-quality plans quickly, exploiting some of the ideas developed for classical planning. In this paper we review the latest developments in automated preference-based planning. We also review various approaches for preference representation, and the main practical approaches developed so far.