experience and, with Dr.Roy Black, had developed a beef cattle management model at Michigan State University (Fox and Black, 1984). The Michigan State University model was later programmed into Basic for a Radio Shack TRS 80 and became Cornell Cattle System 1. Charlie had little modeling experience, but always had an appreciation for the value of computers in solving complex mathematical problems as evidenced by the acquisition at the University of Maine of a computer from the nose cone of a Trident missile (Hoover, 2003). These men recognized that current ruminant nutrition models, such as the NRC models, lacked integration between the animal and its environment and lacked integration between physiological functions and metabolic processes.

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This paper presents the Visual Haskell language. Visual Haskell is intended as i) a means of visualising Haskell programs, and ii) as a complementary programming notation to standard Haskell. The syntax of Visual Haskell is specified by first giving a translation from Haskell into an intermediate

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to point out the particular problems and present the first ideas of a way to overcome them. 1 Introduction The theory structure as it exists in\Omega megas database today is sufficient for general proofs in rather abstract domains. But for theorem proving in more specific domains or if we combine

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The report presents a model to investigate the collective behaviour of a colony when memory of the agents interferes with the usual interaction between individuals. The approach is developed in the framework of a simple situation consisting in tandem recruitment in a population of conservative agents but it is susceptible to be applied also to a wider range of problems. The principle of the method is inspired by the work of Leuthausser and Tarazona who developed the correspondence between a model for macromolecular time evolution and the equilibrium statistical mechanics of a higher dimensional Ising system. 1 Introduction Most investigations on the collective behaviour of social groups assume that the individuals are memoryless with the consequence that the time evolution of a population is exclusively ruled by the interaction mechanisms between the individuals. This report proposes a technique to combine, within the same model, both the interactions between individuals and the mem...

, will explain the discrepancy between the observed and correct system behaviour. We propose a general theory for this problem. The theory requires only that the system be described in a suitable logic. Moreover, there are many such suitable logics, e.g. first-order, temporal, dynamic, etc. As a result

In recent years the development of ontologies—explicit formal specifications of the terms in the domain and relations among them (Gruber 1993)—has been moving from the realm of Artificial-Intelligence laboratories to the desktops of domain experts. Ontologies have become common on the World-Wide Web. The ontologies on the Web range from large taxonomies categorizing Web sites (such as on Yahoo!) to categorizations of products for sale and their features (such as on Amazon.com). The WWW Consortium (W3C) is developing the Resource Description Framework (Brickley and Guha 1999), a language for encoding knowledge on Web pages to make it understandable to electronic agents searching for information. The Defense Advanced Research Projects Agency (DARPA), in conjunction with the W3C, is developing DARPA Agent Markup Language (DAML) by extending RDF with more expressive constructs aimed at facilitating agent interaction on the Web (Hendler and McGuinness 2000). Many disciplines now develop standardized ontologies that domain experts can use to share and annotate information in their fields. Medicine, for example, has produced large, standardized, structured vocabularies such as SNOMED (Price and Spackman 2000) and the semantic network of the Unified Medical Language System (Humphreys and Lindberg 1993). Broad general-purpose ontologies are

Abstract. Reservoir Computing is a new paradigm to use artificial neural networks. Despite its promising performances, it has still some drawbacks: as the reservoir is created randomly, it needs to be large enough to be able to capture all the features of the data. We propose here a method to start with a large reservoir and then reduce its size by pruning out neurons. We then apply this method on a prototypical and a real problem. Both applications show that it allows to improve the performance for a given number of neurons. 1