Data collection often results in records that have missing values or variables. This investigation compares 3 different data imputation models and identifies their merits by using accuracy measures. Autoencoder Neural Networks, Principal components and Support Vector regression are used for prediction and combined with a genetic algorithm to then impute missing variables. The use of PCA improves the overall performance of the autoencoder network while the use of support vector regression shows promising potential for future investigation. Accuracies of up to 97.4 % on imputation of some of the variables were achieved.

The 2007 Spring Symposium Series was held Monday through Wednesday, March 26-28, 2007, at Stanford University, California. The titles of the nine symposia in this symposium series were (1) Control Mechanisms for Spatial Knowledge Processing in Cognitive/Intelligent Systems, (2) Game Theoretic and Decision Theoretic Agents, (3) Intentions in Intelligent Systems, (4) Interaction Challenges for Artificial Assistants, (5) Logical Formalizations of Commonsense Reasoning, (6) Machine Reading, (7) Multidisciplinary Collaboration for Socially Assistive Robotics, (8) Quantum Interaction, and (9) Robots and Robot Venues: Resources for AI Education.

Sourcing professionals buy several trillion dollars worth of goods and services yearly. We introduced a new paradigm called expressive commerceand applied it to sourcing. It combines the advantages of highly expressive human negotiation with the advantages of electronic reverse auctions. The idea is that supply and demand are expressed in drastically greater detail than in traditional electronic auctions and are algorithmically cleared. This creates a Pareto efficiency improvement in the allocation (a win-win between the buyer and the sellers), but the market-clearing problem is a highly complex combinatorial optimization problem. We developed the world's fastest tree search algorithms for solving it. We have hosted $35 billion of sourcing using the technology and created $4.4 billion of hard-dollar savings plus numerous harder-to-quantify benefits. The suppliers also benefited by being able to express production efficiencies and creativity, and through exposure problem removal. Supply networks were redesigned, with quantitative understanding of the trade-offs, and implemented in weeks instead of months.

Hackman delivered a talk entitled "Humans, Robots, and Teams" that leveraged work in The conference's outstanding paper award went to "Humanoid Robots as a Passive-Social Medium: A Field Experiment at a Train Station" by Kotaro The best student paper award went to Guy Hoffman and Cynthia Breazeal for their paper, titled "Effects of Anticipatory HRI-2007 was the second step "Speed Adaptation for a Robot Walking Spurred by included teamwork, social robotics, momentum has been built for HRI-advances in robotics technologies and adaptation, observation and metrics, 2008, which will be held in Amsterdam, communications, many researchers attention, user experience, and The Netherlands, March 12-15, are studying how to use these field testing. The 21st International FLAIRS Conference (FLAIRS-21) will be held May 15 - 17, 2008 at the Grand Bay Miami Hotel in the village of Coconut Grove, Miami, Florida, USA. The conference hotel is on the waterfront of Biscayne Bay close to downtown Miami and South Beach. FLAIRS-21 will feature technical papers, special tracks, and General Chair invited speakers on artificial intelligence. Architectures: Agents and distributed AI, Intelligent user interfaces, Natural lane@ict.usc.edu

The infrastructure of modern society is controlled by software systems that are vulnerable to attacks. Many such attacks, launched by "recreational hackers" have already led to severe disruptions and significant cost. It, therefore, is critical that we find ways to protect such systems and to enable them to continue functioning even after a successful attack. This article describes AWDRAT, a prototype middleware system for providing survivability to both new and legacy applications. AWDRAT stands for architectural differencing, wrappers, diagnosis, recovery, adaptive software, and trust modeling. AWDRAT uses these techniques to gain visibility into the execution of an application system and to compare the application's actual behavior to that which is expected. In the case of a deviation, AWDRAT conducts a diagnosis that determines which computational resources are likely to have been compromised and then adds these assessments to its trust model. The trust model in turn guides the recovery process, particularly by guiding the system in its choice among functionally equivalent methods and resources.AWDRAT has been applied to and evaluated on an example application system, a graphical editor for constructing mission plans. We describe a series of experiments that were performed to test the effectiveness of AWDRAT in recognizing and recovering from simulated attacks, and we present data showing the effectiveness of AWDRAT in detecting a variety of compromises to the application system (approximately 90 percent of all simulated attacks are detected, diagnosed, and corrected). We also summarize some lessons learned from the AWDRAT experiments and suggest approaches for comprehensive application protection methods and techniques.

Once the rules are formulated, This knowledge base is developed and maintained how does the stimuli generator ensure by knowledge engineers who are verification that all user-defined and validity rules, and as experts. Test templates are written by many expert knowledge rules as possible, are verification engineers who implement the test satisfied? How can the generator produce many significantly different tests from the plan. The generic engine, developed by software same test template? Finally, how is all this done engineers, accepts the architecture model, in an efficient manner as to not obstruct the expert knowledge, and test template and generates verification process?

(ISAIM 2008).

But less impressive are our and related AI TOPICS pages--at www. of it. July 10, 2007 task of fruit picking that currently employs not imply any endorsement whatsoever. Whether hypnotized by computer the migrant labor force. Farmers faster than the eye can scan them. Dow or console, players age 8 to 34 spend are'very, very nervous about the availability Jones and Reuters, the news providers, more time at this today than watching TV, and cost of labor in the near future,' says now offer electronically'tagged' news according to Nielsen. 'Most grew up addicted to also hopes to use algorithms to comb News.

We are very pleased to republish here extended versions of a sample of the papers drawn from the Innovative Applications of Artificial Intelligence Conference (IAAI-06), which was held July 17-20, 2006, in Boston, Massachusetts. Three of these articles describe deployed applications and two describe emerging applications.

We describe an application of AI search and information visualization techniques to the problem of school redistricting, in which students are assigned to home schools within a county or school district. This is a multicriteria optimization problem in which competing objectives, such as school capacity, busing costs, and socioeconomic distribution, must be considered. Because of the complexity of the decision-making problem, tools are needed to help end users generate, evaluate, and compare alternative school assignment plans. A key goal of our research is to aid users in finding multiple qualitatively different redistricting plans that represent different trade-offs in the decision space. We present heuristic search methods that can be used to find a set of qualitatively different plans, and give empirical results of these search methods on population data from the school district of Howard County, Maryland. We show the resulting plans using novel visualization methods that we have developed for summarizing and comparing alternative plans.