Simulated annealing is a popular method for approaching the solution of a global optimization problem. Existing results on its performance apply to discrete combinatorial optimization where the optimization variables can assume only a finite set of possible values. We introduce a new general formulation of simulated annealing which allows one to guarantee finite-time performance in the optimization of functions of continuous variables. The results hold universally for any optimization problem on a bounded domain and establish a connection between simulated annealing and up-to-date theory of convergence of Markov chain Monte Carlo methods on continuous domains. This work is inspired by the concept of finite-time learning with known accuracy and confidence developed in statistical learning theory.

Discrete temporal transitions occur in a variety of domains, but this work is mainly motivated by applications in molecular biology: explaining and analyzing observed transcriptome and proteome time series by literature and database knowledge. The starting point of a formal concept analysis model is presented. The objects of a formal context are states of the interesting entities, and the attributes are the variable properties defining the current state (e.g. observed presence or absence of proteins). Temporal transitions assign a relation to the objects, defined by deterministic or non-deterministic transition rules between sets of pre- and postconditions. This relation can be generalized to its transitive closure, i.e. states are related if one results from the other by a transition sequence of arbitrary length. The focus of the work is the adaptation of the attribute exploration algorithm to such a relational context, so that questions concerning temporal dependencies can be asked during the exploration process and be answered from the computed stem base. Results are given for the abstract example of a game and a small gene regulatory network relevant to a biomedical question.

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.

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. 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.

We report on random stimuli generation for hardware verification at IBM as a major applica-tion of various artificial intelligence technologies, including knowledge representation, expert systems, and constraint satisfaction. For more than a decade we have developed several related tools, with huge payoffs. Research and development around this application are still thriving, as we continue to cope with the ever-increasing complexity of modern hardware systems and demanding business environments.

Machine translation (MT) was one of the first applications of artificial intelligence technology that was deployed to solve real-world problems. In the late 1990s, Ford Vehicle Operations began working with Systran Software Inc. to adapt and customize its machine-translation technology in order to translate Ford's vehicle assembly build instructions from English to German, Spanish, Dutch, and Portuguese. The assembly build instructions at Ford contain text written in a controlled language as well as unstructured remarks and comments. The MT system has already translated more than 7 million instructions into these languages and is an integral part of the overall manufacturing process-planning system used to support Ford's assembly plants in Europe, Mexico and South America.

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. 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 show the resulting plans using novel visualization methods that we have developed for summarizing and comparing alternative plans.

The second international conference on Human-Robot Interaction (HRI-2007) was held in Arlington, Virginia, March 9-11, 2007. The theme of the conference was "Robot as Team Member" and included posters and paper presentations on teamwork, social robotics, adaptation, observation and metrics, attention, user experience, and field testing. One hundred seventy-five researchers and practitioners attended the conference, and many more contributed to the conference as authors or reviewers. HRI-2008 will be held in Amsterdam, The Netherlands from March 12-15, 2008.

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.