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.

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.

The Workshop on Applications of Ontologies and Problem-Solving Methods (PSMs), held in conjunction with the Thirteenth Biennial European Conference on Artificial Intelligence (ECAI-98), was held on 24 to 25 August 1998. Twenty-six people participated, and 16 papers were presented. Participants included scientists and practitioners from both the ontology and PSM communities. The first day was devoted to paper presentations and discussions. The second (half) day, a joint session was held with two other workshops: (1) Building, Maintaining, and Using Organizational Memories and (2) Intelligent Information Integration. The reason for the joint session was that in all three workshops, ontologies play a prominent role, and the goal was to bring together researchers working on related issues in different communities. The workshop ended with a discussion about the added value of a combined ontologies-PSM workshop compared to separate workshops.

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.

Damasio, Picard, and others have misinterpreted that too little emotion also can Likewise, it is now commonplace the evidence about brain wreak havoc (p.

Cox's well-known theorem justifying the use of probability is shown not to hold in finite domains. The counterexample also suggests that Cox's assumptions are insufficient to prove the result even in infinite domains. The same counterexample is used to disprove a result of Fine on comparative conditional probability.

This paper reports about an application of Bayes' inferred neural network classifiers in the field of automatic sleep staging. The reason for using Bayesian learning for this task is twofold. First, Bayesian inference is known to embody regularization automatically. Second, a side effect of Bayesian learning leads to larger variance of network outputs in regions without training data. This results in well known moderation effects, which can be used to detect outliers. In a 5 fold cross-validation experiment the full Bayesian solution found with R. Neals hybrid Monte Carlo algorithm, was not better than a single maximum a-posteriori (MAP) solution found with D.J. MacKay's evidence approximation. In a second experiment we studied the properties of both solutions in rejecting classification of movement artefacts.

In this paper, we discuss regularisation in online/sequential learning algorithms. In environments where data arrives sequentially, techniques such as cross-validation to achieve regularisation or model selection are not possible. Further, bootstrapping to determine a confidence level is not practical. To surmount these problems, a minimum variance estimation approach that makes use of the extended Kalman algorithm for training multi-layer perceptrons is employed. The novel contribution of this paper is to show the theoretical links between extended Kalman filtering, Sutton's variable learning rate algorithms and Mackay's Bayesian estimation framework. In doing so, we propose algorithms to overcome the need for heuristic choices of the initial conditions and noise covariance matrices in the Kalman approach.

It is especially suited for speech and image data which in many applieations have to be transmitted under low bandwidth/high noise level conditions. Following the idea of (Farvardin, 1990) and (Luttrell, 1989) of jointly optimizing the codebook and the data representation w.r.t. to a given channel noise we apply a deterministic annealing scheme (Rose, 1990; Buhmann, 1997) to the problem and develop a An Annealed Self-Organizing Map for Source Channel Coding 431 soft topographic vector quantization algorithm (STVQ) (cf.

We address the problem oflearning structure in nonlinear Markov networks with continuous variables. This can be viewed as non-Gaussian multidimensional density estimation exploiting certain conditional independencies in the variables. Markov networks are a graphical way of describing conditional independencies well suited to model relationships which do not exhibit a natural causal ordering. We use neural network structures to model the quantitative relationships between variables. The main focus in this paper will be on learning the structure for the purpose of gaining insight into the underlying process. Using two data sets we show that interesting structures can be found using our approach. Inference will be briefly addressed.