Information extraction is the task of automaticallypicking up information of interest from an unconstrained text. Informationof interest is usually extracted in two steps. First, sentence level processing locates relevant pieces of information scatteredthroughout the text; second, discourse processing merges coreferential information to generate the output. In the first step, pieces of information are locally identified without recognizing any relationships among them. A key word search or simple patternsearch can achieve this purpose. The second step requires deeperknowledge in order to understand relationships among separately identified pieces of information. Previous information extraction systems focused on the first step, partly because they were not required to link up each piece of information with other pieces. To link the extracted pieces of information and map them onto a structuredoutput format, complex discourse processing is essential. This paperreports on a Japanese information extraction system that merges information using a pattern matcher and discourse processor. Evaluationresults show a high level of system performance which approaches human performance.

However, extended object-oriented oday, object-oriented programming important and powerful programming Italy, Sweden, the United languages and systems have paradigm, especially for Kingdom, and the United States were been developed that are adequate to the development of complex systems, invited to the workshop. This article handle AI applications. AI, raised and the major points made programming, a case of objectoriented however, is looking for knowledge during the presentations of the eight programming that has a representation and programming papers in the workshop's four sessions. AI, does not satisfy distributed AI applications and uses constructs (for The workshop started with an requirements because it lacks representation, example, frames) and notions (for introduction by Ibrahim in which he communication, and organization. Ibrahim posed a to the object-based concurrent The one-day workshop entitled number of questions related to the programming paradigm to close the Objects and AI, held in Sydney, Australia, theme of the workshop and asked gap with distributed AI, such as the on 25 August 1991 in conjunction the participants to address some of introduction of more powerful object with the 1991 International these questions during their talks and representations, a social theory of Joint Conference on Artificial Intelligence, discussion.

The Hong Kong-based Mass Transit Railway Corp. (MTRC) has developed the Station Management Expert e Norwegian Police Data Center help predict aircraft fires and other System (SMES). SMES is an intelligent utilized an expert system to catastrophes. The police put and risk factors from the records functions and advising the controller the intelligent application online to of the National Transportation Safety of actions to take in case of emergency. The system is installed in Ya Ma at the games while complying with Carnegie Group and Westinghouse Tei Station as a test site, and the complex national employment regulations. Electric (both in Pittsburgh, Penn.) are MTRC plans to expand its use Plans are to deploy and network working with Pittsburgh area medical throughout the subway system as it the expert system into every law centers to develop an intelligent proves to be successful. The network Martin Marietta (Bethesda, Md.) is developed a neural network application will gather and organize data on using a real-time expert system to that has improved the efficiency clinical diagnoses, treatment, clinical build the Traffic Operations Center of its direct mail marketing efforts by and research findings, and patient (TOC) component of its Intelligent 35%.

This article summarizes the findings of a 1992 study of knowledge-based systems research and applications in Japan. Representatives of universities and businesses were chosen by the Japan Technology Evaluation Center to investigate the state of the technology in Japan relative to the United States. The panel's report focused on applications, tools, and research and development in universities and industry and on major national projects.

He knew the challenges opinion, as one of the premier natural language were great and would require the research groups in the world. He gave efforts of many people. He had a genius for one of us (Barbara Grosz) her first AI job, even bringing these people together. In doing so, he took a of people who had known Don over the risk of a magnitude that she fully appreciated years to send us reminiscences. Although only years later when she herself was hiring each person's story differed, a striking commonality research associates.

We recommend doesn't carry the day. The robot swallow something that's not particularly that those inclined to study must be able to use Q to arrive at its savory: a contradiction.

The information in this article was originally presented as a keynote invited talk by Susan Lederman at the Thirteenth International Joint Conference on Artificial Intelligence in Chambery, France; it is based primarily on a joint research program that we conducted. We explain how the scientific study of biological systems offers a complementary approach to the more formal analytic methods favored by roboticists; such study is also relevant to a number of classical problems addressed by the AI field. We offer an example of the scientific approach that is based on a selection of our experiments and empirically driven theoretical work on human haptic (tactual) object processing; the nature and role of active manual exploration is of particular concern. We further suggest how this program with humans can be modified and extended to guide the development of highlevel manual exploration strategies for robots equipped with a haptic perceptual system.

The theory revision problem is the problem of how best to go about revising a deficient domain theory using information contained in examples that expose inaccuracies. In this paper we present our approach to the theory revision problem for propositional domain theories. The approach described here, called PTR, uses probabilities associated with domain theory elements to numerically track the ``flow'' of proof through the theory. This allows us to measure the precise role of a clause or literal in allowing or preventing a (desired or undesired) derivation for a given example. This information is used to efficiently locate and repair flawed elements of the theory. PTR is proved to converge to a theory which correctly classifies all examples, and shown experimentally to be fast and accurate even for deep theories.

So far there has been no general method for relating extracellular electrophysiological measured activity of neurons in the associative cortex to underlying network or "cognitive" states. We propose to model such data using a multivariate Poisson Hidden Markov Model. We demonstrate the application of this approach for temporal segmentation of the firing patterns, and for characterization of the cortical responses to external stimuli. Using such a statistical model we can significantly discriminate two behavioral modes of the monkey, and characterize them by the different firing patterns, as well as by the level of coherency of their multi-unit firing activity. Our study utilized measurements carried out on behaving Rhesus monkeys by M. Abeles, E. Vaadia, and H. Bergman, of the Hadassa Medical School of the Hebrew University. 1 Introduction Hebb hypothesized in 1949 that the basic information processing unit in the cortex is a cell-assembly which may include thousands of cells in a highly interconnected network[l].

Learning curves show how a neural network is improved as the number of t.raiuing examples increases and how it is related to the network complexity. The present paper clarifies asymptotic properties and their relation of t.wo learning curves, one concerning the predictive loss or generalization loss and the other the training loss. The result gives a natural definition of the complexity of a neural network. Moreover, it provides a new criterion of model selection.