Architecture) provides a generic domain independent computational architecture suitable for command, planning and execution applications.

Assessing probabilities in such situations seems difficult.

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DB DB error m error m no smo othing The p erformance impro v ed considerably for DB F or DB the impro v emen t is less signi can t and the opti m um w as obtained for less smo othing than for DB This is exp ected since the n um b er of training patterns in DB is m uc h larger than in DB v ersus A higher p erformance gain can b e exp ected for more selec tiv e hin ts than smo othing suc ha s i n v ariance to small rotations or scaling of the digits SLD Better p erformance migh tb e a c hiev ed with other sim ilarit y functions K x x Figure sho ws the decision b oundary obtained with a second order p olynomial and a radial basis function RBF maxim um margin classi er with K x x e x p k x x k The decision b oundary of the p olynomial classi er is m uc h closer to one of the t w o classes This is a consequence of the non linear transform from space to x space of p olynomials whic h realizes a p osition dep enden t scaling of distance Radial Basis F unctions do not exhibit this problem The ...

The title of this paper, "On Seeing Robots", leaves substantial scope for playful exploration. The simple ambiguity is, of course, between describing robots that see their worlds and systems that see robots. These categories are not exclusive: I also combine them and discuss robots that see robots and even robots that see themselves. Furthermore, the title is designed to echo, and pay homage to, a classic vision paper entitled "On Seeing Things" by Max Clowes [1] as I have done once before [2]. But the context, the arguments and the conclusions are new; the comparison is used explicitly here to show the difference between the classical approach and an emerging situated approach to robotic perception. The most important reading of the title is that the paper is about how we see robots; it is about the computational paradigms, the assumptions, the architectures and the tools we use to design and build robots.

"We report results from large-scale experiments in satisfiability testing. As has been observed by others, testing the satisfiability of random formulas often appears surprisingly easy. Here we show that by using the right distribution of instances, and appropriate parameter values, it is possible to generate random formulas that are hard, that is, for which satisfiability testing is quite difficult. Our results provide a benchmark for the evaluation of satisfiability-testing procedures." Proc. AAAI-92.

"We introduce a greedy local search procedure called GSAT for solving propositional satisfiability problems. Our experiments show that this procedure can be used to solve hard, randomly generated problems that are an order of magnitude larger than those that can be handled by more traditional approaches such as the Davis-Putnam procedure or resolution. We also show that GSAT can solve structured satisfiability problems quickly. In particular, we solve encodings of graph coloring problems, N-queens, and Boolean induction. General application strategies and limitations of the approach are also discussed. GSAT is best viewed as a model-finding procedure. Its good performance suggests that it may be advantageous to reformulate reasoning tasks that have traditionally been viewed as theorem-proving problems as model-finding tasks." Proc. AAAI-92.

Pragmatism, taken not just as a philosophical movement but as a way of addressing problems, strongly influenced the debate on the foundations of probability during the first half of the twentieth century. Upholders of different interpretations of probability such as Hans Reichenbach, Ernest Nagel, Rudolf Carnap, Frank Ramsey, and Bruno de Finetti, acknowledged their debt towards pragmatist philosophers, including Charles Sanders Peirce, William James, Clarence Irving Lewis, William Dewey and Giovanni Vailati. In addition, scientist-philosophers like Ernst Mach, Ludwig Boltzmann, Henri Poincaré, Pierre Duhem, and Karl Pearson, who heralded a conception of science and knowledge at large that was close to pragmatism, were very influential in that debate. Among the main interpretations of probability - frequentism, propensionism, logicism and subjectivism -, the latter is no doubt the closest to the pragmatist outlook. This paper concentrates on three representatives of the subjective theory, namely Frank Ramsey, Bruno de Finetti and Émile Borel.

This srudy has demonstrated how artificial neural networks (ANNs) can be used to characterize seismic sources using high-frequency regional seismic data. We have taken the novel approach of using ANNs as a research tool for obtaining seismic source information, specifically depth of focus for earthquakes and ripple-fire characteristics for economic blasts, rather than as just a feature classifier between earthquake and explosion populations. Overall, we have found that ANNs have potential applications to seismic event characterization and identification, beyond just as a feature classifier. In future studies, these techniques should be applied to actual data of regional seismic events recorded at the new regional seismic arrays. The results of this study indicates that an ANN should be evaluated as part of an operational seismic event identification system. 1 INTRODUCTION ANNs have usually been used as pattern matching algorithms, and recent studies have applied ANNs to standard classification between classes of earthquakes and explosions using wavefonn features (Dowla, et al, 1989), (Dysart and Pulli, 1990).

This srudy has demonstrated how artificial neural networks (ANNs) can be used to characterize seismic sources using high-frequency regional seismic data. We have taken the novel approach of using ANNs as a research tool for obtaining seismic source information, specifically depth of focus for earthquakes and ripple-fire characteristics for economic blasts, rather than as just a feature classifier between earthquake and explosion populations. Overall, we have found that ANNs have potential applications to seismic event characterization and identification, beyond just as a feature classifier. In future studies, these techniques should be applied to actual data of regional seismic events recorded at the new regional seismic arrays. The results of this study indicates that an ANN should be evaluated as part of an operational seismic event identification system. 1 INTRODUCTION ANNs have usually been used as pattern matching algorithms, and recent studies have applied ANNs to standard classification between classes of earthquakes and explosions using wavefonn features (Dowla, et al, 1989), (Dysart and Pulli, 1990).