This paper offers an approach to extensible knowledge representation and reasoning for a family of formalisms known as Description Logics. The approach is based on the notion of adding new concept constructors, and includes a heuristic methodology for specifying the desired extensions, as well as a modularized software architecture that supports implementing extensions. The architecture detailed here falls in the normalize-compared paradigm, and supports both intentional reasoning (subsumption) involving concepts, and extensional reasoning involving individuals after incremental updates to the knowledge base. The resulting approach can be used to extend the reasoner with specialized notions that are motivated by specific problems or application areas, such as reasoning about dates, plans, etc. In addition, it provides an opportunity to implement constructors that are not currently yet sufficiently well understood theoretically, but are needed in practice. Also, for constructors that are provably hard to reason with (e.g., ones whose presence would lead to undecidability), it allows the implementation of incomplete reasoners where the incompleteness is tailored to be acceptable for the application at hand.

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.

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.

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

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.

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.

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.

Q-Iearning is a popular reinforcement learning (RL) algorithm whose convergence is well demonstrated in the literature (Jaakkola et al., 1994; Tsitsiklis, 1994; Littman and Szepesvari, 1996; Szepesvari and Littman, 1996). Our aim in this paper is to provide an upper bound for the convergence rate of (lookup-table based) Q-Iearning algorithms. Although, this upper bound is not strict, computer experiments (to be presented elsewhere) and the form of the lemma underlying the proof indicate that the obtained upper bound can be made strict by a slightly more complicated definition for R. Our results extend to learning on aggregated states (see (Singh et al., 1995» and other related algorithms which admit a certain form of asynchronous stochastic approximation (see (Szepesv iri and Littman, 1996». Present address: Associative Computing, Inc., Budapest, Konkoly Thege M. u. 29-33, HUNGARY-1121 The Asymptotic Convergence-Rate of Q-leaming

Applications of Gaussian mixture models occur frequently in the fields of statistics and artificial neural networks.

In contrast to the maximum likelihood approach which finds only a single estimate for the regression parameters, the Bayesian approach yields a distribution of weight parameters, p(wID), conditional on the training data D, and predictions are ex- ·Present address: SNN, University of Nijmegen, Geert Grooteplein 21, Nijmegen, The Netherlands.