Eastman Kodak (Rochester, N.Y.), a manufacturer of imaging-related products, has developed an online neural network-based machine vision system for surface mount solder paste inspection. Caere (Los Gatos, Calif.), a provider of neural network-based optical character recognition (OCR) technology, has signed an agreement to supply IBM Ireland with OCR Readers for AN POST, Ireland's national postal service. Using a handheld wand, postal employees will be able to scan text and read bar codes from anywhere on a document. BrainTech (Scottsdale, Ariz.), a developer of neural network and fuzzy logic-based pattern recognition technologies, has signed a development agreement with Raven (Alexandria, Va.), a developer of acoustic systems for the U.S. Navy. BrainTech will integrate its pattern-matching recognition engine into Raven's new medical diagnostic systems.

Similar systems are being installed at other Texaco sites. Lear Astronics (Santa Monica and Ontario, Calif.) is combining neural networks with virtual reality to enhance its Autonomous Landing Guidance (ALG) system. Lear Astronics is using a neural network-based massively parallel coprocessor for real-time image processing in the ALG system, which enables commercial and military aircraft pilots to land in foggy conditions. Researchers at Georgia Tech (Atlanta, Ga.) have created intelligent agent software called the Technology Opportunities Analysis Knowbot (TOAK) that provides profiles of the latest technological trends and opportunities. TOAK navigates through multiple networks and across diverse computer systems to perform specific search tasks for the user.

General Electric's Research and Development Center (Schenectady, NY) has developed an expert system which is being used to increase the speed of design of new jet engines, electric motors, and other complex machines. The system, called Engineous, has been used to improve gas turbine designs, resulting in increased fuel efficiency for jet aircraft engines manufactured by GE. The Expert Sniffer, developed by Network General (Menlo Park, CA), is an expert system-based technology that automatically identifies network problems and recommends solutions to network managers. Software based on this technology will be a standard part of the Sniffer Network Analyzer, targeted initially for Ethernet and Token Ring environments. The objective of this project will be to define a standard execution environment for fuzzy systems.

By David Blanchard Color Tile Inc. (Fort Worth, Tex.), a home improvement retailer, has automated its help desk operations with an expert system. The system has reduced time spent by store personnel on the telephone seeking answers to point-of-sale technical problems, and it allows the help desk analysts to handle a wider range of responsibilities for the company. Traversum AB, a company that specializes in share and stock dealing in the Swedish stock exchange, has developed an intelligent system to advise on how to deal in stocks and shares. The company's "hit rate" of dealing correctly with stocks has reportedly increased from 60% to 90%. IntelliCorp Inc. (Mountain View, Calif.), an expert system vendor, and James Martin & Co. (Reston, Va.), a computer-aided software engineering (CASE) consulting group, have launched a field test program for an object-oriented information engineering environment.

A key feature of the AAMAS conference is its emphasis on ties to real-world applications. The focus of this article is to provide a broad overview of application-focused papers published at the AAMAS 2010 and 2011 conferences. More specifically, recent applications at AAMAS could be broadly categorized as belonging to research areas of security, sustainability, and safety. We outline the domains of applications, key research thrusts underlying each such application area, and emerging trends. This emphasis of trying to marry theory and practice at AAMAS goes all the way back to the origins of its predecessor conferences, such as the first International Conference on Autonomous Agents (Johnson 1997).

Today a robot can do the jobs of 10 workers. Steel mills are less dangerous. Sorting machines have made the movement of goods more efficient. New cars are turned out in much quicker fashion--all because of technological advances. Organized labor understands that, but, like [Dexter] Cato, feels left out of the discussion.

A common difficulty in diagnosing failures within Pratt & Whitney's F100-PW-100/200 gas turbine engine occurs when a fault in one part of a system--comprising an engine, an airframe, a test cell, and automated ground engine test set (AGETS) equipment--is manifested as an out-ofbound parameter elsewhere in the system. In such cases, the normal procedure is to run AGETS self-diagnostics on the abnormal parameter. However, because the self-diagnostics only test the specified local parameter, it will pass, leaving only the operators' experience and traditional fault-isolation manuals to locate the source of the problem in another part of the system. In such cases, the normal procedure is to run AGETS self-diagnostics on the abnormal parameter. However, because the self-diagnostics only test the parameter specified, it will pass because parameter tests are local tests that cannot uncover malfunctions in other parts of the system.

The need to save energy becomes even greater when considering an electric car, since heavy use of the climate-control system may exhaust the battery. In this article we consider a method for an automated agent to provide drivers with advice that will motivate them to reduce the energy consumption of their climate-control unit. Our approach takes into account both the energy consumption of the climatecontrol system and the expected comfort level of the driver. We therefore have built two models, one for assessing the energy consumption of the climate-control system as a function of the system's settings, and the other for modeling the human comfort level as a function of the climate-control system's settings. Using these models, the agent provides advice to the driver considering how to set the climate-control system.

DrillEdge utilizes case-based reasoning with temporal representations on streaming realtime data, pattern matching, and agent systems to predict problems and give advice on how to mitigate the problems. Decisions are supported through analyzing real-time data streams of parameters measured both on the surface and downhole when drilling. The real-time analysis identifies symptoms of problems, which are combined to provide best practices for how to handle the current situation. Verdande Technology has developed DrillEdge to reduce the cost and decrease the probability of failures in oil well drilling. Currently, DrillEdge continuously monitors around 30 oil well drilling operations in parallel for several customers and has been deployed commercially for two years.

In our business and personal lives, we've become accustomed to an ever-increasing number of devices connected through the Internet of Things (IoT). In addition, artificial intelligence technologies are more prevalent in various applications and tools. As these two technologies converge, they are creating a new "intelligence of things" that will impact how businesses can best serve customers. Early sensors and devices gathered and shared huge volumes of data. But they typically included little to no intelligence or self-awareness of their own.