To not only proving new mathematical obtain broader input, especially from countries results by computer but also formally verifying outside North America, a call for commentaries the correctness of (certain properties of) computer was issued to the automated deduction community.

TACAIR-SOAR is an intelligent, rule-based system that generates believable humanlike behavior for large-scale, distributed military simulations. The innovation of the application is primarily a matter of scale and integration. The system is capable of executing most of the airborne missions that the U.S. military flies in fixed-wing aircraft. It accomplishes its missions by integrating a wide variety of intelligent capabilities, including real-time hierarchical execution of complex goals and plans, communication and coordination with humans and simulated entities, maintenance of situational awareness, and the ability to accept and respond to new orders while in flight. The system is currentl y deployed at the Oceana Naval Air Station WISSARD (what-if simulation system for advanced research and development) Lab and the Air Force Research Laboratory in Mesa, Arizona. Its most dramatic use was in the Synthetic Theater of War 1997, which was an operational training exercise that ran for 48 continuous hours during which TACAIR-SOAR flew all U.S. fixed-wing aircraft.

We describe an AI system (CTSHIV) that connects the scientific AIDS literature describing specific human immunodeficiency virus (HIV) drug resistances directly to the customized treatment strategy of a specific HIV patient. Rules in the CTSHIV knowledge base encode knowledge about sequence mutations in the HIV genome that have been found to result in drug resistance to the HIV virus. Rules are applied to the actual HIV sequences of the virus strains infecting the specific patient undergoing clinical treatment to infer current drug resistance. A rule-directed search through mutation sequence space identifies nearby drug-resistant mutant strains that might arise. The possible combination drug-treatment regimens currently approved by the U.S. Food and Drug Administration are considered and ranked by their estimated ability to avoid identified current and nearby drug-resistant mutants. The highest-ranked treatments are recommended to the attending physician. The result is more precise treatment of individual HIV patients and a decreased tendency to select for drug-resistant genes in the global HIV gene pool. Initial results from a small human clinical trial are encouraging, and further clinical trials are planned. From an AI viewpoint, the case study demonstrates the extensibility of knowledge-based systems because it illustrates how existing encoded knowledge can be used to support new knowledge-based applications that were unanticipated when the original knowledge was encoded.

Turbine engine diagnostics (TED) is a diagnostic expert system to aid the M1 Abrams tank mechanic find-and-fix problems in the AGT-1500 turbine engine. TED was designed to provide the apprentice mechanic with the ability to diagnose and repair the turbine engine like an expert mechanic. The expert system was designed and built by the U.S. Army Research Laboratory and the U.S. Army Ordnance Center and School. This article discusses the relevant background, development issues, reasoning method, system overview, test results, return on investment, and fielding history of the project. Limited fielding began in 1994 to select U.S. Army National Guard units and complete fielding to all M1 Abrams tank maintenance units started in 1997 and will finish by the end of 1998. The Army estimates that TED will save roughly $10 million a year through improved diagnostic accuracy and reduced waste. The development and fielding of the TED program represents the Army's first successful fielded maintenance system in the area of AI. Several reasons can be given for the success of the TED program: an appropriate domain with proper scope, a close relationship with the expert, extensive user involvement, and others that are discussed in this article.

We describe an application of a dynamic replanning technique in a highly dynamic and complex domain: the military aeromedical evacuation of patients to medical treatment facilities. U.S. Transportation Command (USTRANSCOM) is the U.S. Department of Defense (DoD) agency responsible for evacuating patients during wartime and peace. Doctrinally, patients requiring extended treatment must be evacuated by air to a suitable medical treatment facility. The Persian Gulf War was the first significant armed conflict in which this concept was put to a serious test. The results were far from satisfactory -- about 60 percent of the patients ended up at the wrong destinations. In early 1993, the DoD tasked USTRANSCOM to consolidate the command and control of medical regulation and aeromedical evacuation operations. The ensuing analysis led to TRAC2ES (TRANSCOM regulating and command and control evacuation system), a decision support system for planning and scheduling medical evacuation operations. Probably the most challenging aspect of the problem has to do with the dynamics of a domain in which requirements and constraints continuously change over time. Continuous dynamic replanning is a key capability of TRAC2ES. This article describes the application and the AI approach we took in providing this capability.

The expert system maintains a stable kiln temperature and has facilitated the standardization of control procedures. American Airlines (Fort Worth, Tex.) consortium representing 60 percent of Irvine Sensors (Costa Mesa, Calif.) has utilized speech-recognition technology all newspapers circulated in the United has received a contract from the U.S. to enhance its automated Kingdom, has developed an intelligent Army Space and Missile Defense Command flight information system, The technology will be used to classified ads. Calif.) has initiated a fingerprintbased to improve its underwriting process. Cooper Tire & Rubber (Findlay, that provides participants with access The company's rule-based paperless Ohio) has implemented a genetic to online banking services. The biometric personal lines processing application algorithm-based system to optimize software matches the fingerprint automates the procedure for evaluating its supply chain.

Affective cues are a natural way that humans give feedback to learning systems. My students and I currently use tools of expression recognition to gather data to hone the abilities of our research systems, always with the consent nontechnical users are in the majority, of those involved. However, Sloman's to Aaron Sloman for his their feelings and fears demand not remarks imply that I favor Sloman was one I use the expression emotion recognition even the relatively benign intrusions, of the first in the AI community to only when established as shorthand such as emotional agents that jiggle write about the role of emotion in for the unwieldy but more accurate about on the screen, smiling at you in computing (Sloman and Croucher description "inference of an an annoying and inappropriate fashion, 1981), and I value his insight into theories emotional state from observations of costing you precious time while of emotional and intelligent systems. The Although inappropriate use of affect largely on some details related to computer cannot directly read internal might be the most common affront unknown features of human emotion; thoughts or feelings, and therefore, with this technology, there are also hence, I don't think the review captures there is no "emotion detector" as potentially more serious problems the flavor of the book. It can detect certain expressions (chapter 4.) he does raise interesting points, as well that arise in conjunction with an Sloman writes that in lieu of being as potential misunderstandings, both internal state: pressure profiles of hooked up to emotion-sensing of which I am grateful for the opportunity banging on a mouse, video signals of devices, he would prefer us all to to comment on. What Sloman misses in more. The aphorism "if you detect in the foreseeable future is teacher and pupil." These users tend to not desires. In contexts where humans wake-up call to us: Current forms of understand the limits of the technology; interact with computers naturally and computer-mediated interaction limit they are already so amazed at what socially (Reeves and Nass 1996), we affective communication. For example, the computer computer, "Does it know that I don't might speed up if we seem Sloman's review might seem confusing like it?" At one time, I would have discounted bored, offer an alternate explanation if in places whether or not you've read such remarks, but now that we appear confused, and try to my book. When the athlete rattles off her list of feelings to the public eye, she rattles off not just what she thinks she feels but able to a misunderstanding about what or otherwise. In this flurry of comes from the Latin sentire, the root of modulation, which indeed exist, thoughts and feelings, she anticipates the words sentiment and sensation.) Sentic especially given an incomplete understanding an event and concludes, "The thought modulation, such as voice inflection, of the phenomena.

The relationship between the Bayesian approach and the minimum description length approach is established. We sharpen and clarify the general modeling principles MDL and MML, abstracted as the ideal MDL principle and defined from Bayes's rule by means of Kolmogorov complexity. The basic condition under which the ideal principle should be applied is encapsulated as the Fundamental Inequality, which in broad terms states that the principle is valid when the data are random, relative to every contemplated hypothesis and also these hypotheses are random relative to the (universal) prior. Basically, the ideal principle states that the prior probability associated with the hypothesis should be given by the algorithmic universal probability, and the sum of the log universal probability of the model plus the log of the probability of the data given the model should be minimized. If we restrict the model class to the finite sets then application of the ideal principle turns into Kolmogorov's minimal sufficient statistic. In general we show that data compression is almost always the best strategy, both in hypothesis identification and prediction.

In this paper we present TDLeaf(lambda), a variation on the TD(lambda) algorithm that enables it to be used in conjunction with minimax search. We present some experiments in both chess and backgammon which demonstrate its utility and provide comparisons with TD(lambda) and another less radical variant, TD-directed(lambda). In particular, our chess program, ``KnightCap,'' used TDLeaf(lambda) to learn its evaluation function while playing on the Free Internet Chess Server (FICS, fics.onenet.net). It improved from a 1650 rating to a 2100 rating in just 308 games. We discuss some of the reasons for this success and the relationship between our results and Tesauro's results in backgammon.

We prove that the Canonical Distortion Measure (CDM) [2, 3] is the optimal distance measure to use for I nearest-neighbour (l-NN) classification, andshow that it reduces to squared Euclidean distance in feature space for function classes that can be expressed as linear combinations of a fixed set of features.