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.

We introduce a model for noise-robust analog computations with discrete time that is flexible enough to cover the most important concrete cases, such as computations in noisy analog neural nets and networks of noisy spiking neurons. We show that the presence of arbitrarily small amounts of analog noise reduces the power of analog computational models to that of finite automata, and we also prove a new type of upper bound for the VC-dimension of computational models with analog noise. 1 Introduction Analog noise is a serious issue in practical analog computation. However there exists no formal model for reliable computations by noisy analog systems which allows us to address this issue in an adequate manner. The investigation of noise-tolerant digital computations in the presence of stochastic failures of gates or wires had been initiated by [von Neumann, 1956]. We refer to [Cowan, 1966] and [Pippenger, 1989] for a small sample of the nllmerous results that have been achieved in this direction. The same framework (with stochastic failures of gates or wires) hac; been applied to analog neural nets in [Siegelmann, 1994].

We introduce a model for noise-robust analog computations with discrete time that is flexible enough to cover the most important concrete cases, such as computations in noisy analog neural nets and networks of noisy spiking neurons. We show that the presence of arbitrarily small amounts of analog noise reduces the power of analog computational models to that of finite automata, and we also prove a new type of upper bound for the VC-dimension of computational models with analog noise. 1 Introduction Analog noise is a serious issue in practical analog computation. However there exists no formal model for reliable computations by noisy analog systems which allows us to address this issue in an adequate manner. The investigation of noise-tolerant digital computations in the presence of stochastic failures of gates or wires had been initiated by [von Neumann, 1956]. We refer to [Cowan, 1966] and [Pippenger, 1989] for a small sample of the nllmerous results that have been achieved in this direction. The same framework (with stochastic failures of gates or wires) hac; been applied to analog neural nets in [Siegelmann, 1994].

A neural network model of 3-D lightness perception is presented which builds upon the FACADE Theory Boundary Contour System/Feature Contour System of Grossberg and colleagues. Early ratio encoding by retinal ganglion neurons as well as psychophysical results on constancy across different backgrounds (background constancy) are used to provide functional constraints to the theory and suggest a contrast negation hypothesis which states that ratio measures between coplanar regions are given more weight in the determination of lightness of the respective regions.

A neural network model of 3-D lightness perception is presented which builds upon the FACADE Theory Boundary Contour System/Feature Contour System of Grossberg and colleagues. Early ratio encoding by retinal ganglion neurons as well as psychophysical results on constancy across different backgrounds (background constancy) are used to provide functional constraints to the theory and suggest a contrast negation hypothesis which states that ratio measures between coplanar regions are given more weight in the determination of lightness of the respective regions.

A neural network model of 3-D lightness perception is presented which builds upon the FACADE Theory Boundary Contour System/Feature ContourSystem of Grossberg and colleagues. Early ratio encoding by retinal ganglion neurons as well as psychophysical resultson constancy across different backgrounds (background constancy) are used to provide functional constraints to the theory and suggest a contrast negation hypothesis which states that ratio measures between coplanar regions are given more weight in the determination of lightness of the respective regions.

Georgia Tech's approach differed from other The contest required competing by the robots to collect trash; (3) cooperative robot entries to clean up a messy office behaviors provide for cooperation between strewn with trash. Wads of paper, Styrofoam robots; (4) temporal sequencing coordinates coffee cups, and soda cans were placed by transitions between distinct operating states judges throughout the contest arena along for each robot and achieves the desired goal with wastebaskets, where they hoped the state; (5) fast vision locates soda cans, wastebaskets, robots would deposit the trash. During competitive robot hardware and specifies behavioral states trials, each robot was to gather and throw and transitions between them; and (6) a realtime away as much trash as possible in 10 minutes. The task proved processing are outlined in the next section. The article closes trash in a wastebasket. Unfortunately, the with strategies used and lessons learned at the computational overhead was so great that competition. If a robot was The 10-pound robots were built using off-theshelf near an item of trash or a wastebasket, it components at a cost of approximately could signal its intent to pick up or throw $1700 each.

This project has entered into a joint agreement agreements with Nestor Inc. (Providence, will involve simulating fires in buildings with software giant Microsoft Corp. R.I.) to institute pilot projects and assessing how a VR system as the first step toward installing (Redmond, Wash.) that will enable Planet is an expert system that individual cardholders and banks provides strategic and detailed planning against losses due to fraud, making for financial audits. It produces Bank Bandeirantes (Sao Paulo, Brazil) use of neural networks to learn a cardholder's risk assessments for a variety of financial has teamed up with another Brazilian pattern of credit card use. Venus is a flowcharting tool analysis expert system that operates in Cal.) will team up with VRl Entertainment for auditors who specialize in auditing real-time. The intelligent system has Inc. (Boulder, Colo.) to computer systems running deliver virtual reality to the home via helped the bank experience dramatic SIRIUS (SWIFT's Intelligent examines in detail a P 1 the key AI technologies: telecommunications operators Contact Lionheart Publishing system supervises the 150 switches Inc,2555 Cumberland Parkway, an event manager and hypertext Suite 299, Atlanta, GA 30339, (404) 434-manuals, and fields hundreds of telephone and 350 connections that make up 2187, FAX: (404) 432-6969. Wash.) have teamed up to develop Intelligent Alarming, which integrates Metropolitan Federal Bank (Edina, Mitek Systems (San Diego, Cal.) has industrial automation software Minn.) has deployed an automated completed a test phase for an automatic with an expert system.

We report on the spring 1992 symposium on diagrammatic representations in reasoning and problem solving sponsored by the Association for the Advancement of Artificial Intelligence. The symposium brought together psychologists, computer scientists, and philosophers to discuss a range of issues covering both externally represented diagrams and mental images and both psychology -- and AI-related issues. In this article, we develop a framework for thinking about the issues that were the focus of the symposium as well as report on the discussions that took place. We anticipate that traditional symbolic representations will increasingly be combined with iconic representations in future AI research and technology and that this symposium is simply the first of many that will be devoted to this topic.

Abduction is inference to the best explanation.