In his model, requirements needs, it must be able to represent is done unintentionally; a problem for filling system knowledge solver attempting to solve a gaps also direct explanation generation what these needs are. Ram proposed problem simply stores a trace of its by guiding retrieval and revision representations that include processing without attention to its of explanations during case-based the desired knowledge (possibly partially future relevance. However, Ng's previously explanation construction (Leake specified) and the reason that mentioned studies show that 1992). In the context of analogical the knowledge is sought. Leake for a different class of task, learning mapping, Thagard pointed out that focused on the representation of the goals have a strong effect on the goals, semantic constraints, and syntactic knowledge required to resolve anomalies learning performance of human constraints all affect analogical (which depends on a vocabulary learners. A future question is to identify mapping (Holyoak and Thagard 1989) of anomaly characterization structures the limits of goal-driven processing and the retrieval of potential analogs to describe the information in human learners.

My Ph.D. dissertation develops and implements a new model of multiagent coordination, called JOINT RESPONSIBILITY (Jennings 1992b), based on the notion of joint intentions. The responsibility framework was devised specifically for coordinating behavior in complex, unpredictable, and dynamic environments, such as industrial control. The need for such a principled model became apparent during the development and the application of a general-purpose cooperation framework (GRATE) to two real-world industrial applications.

They government contacts indicated the Bonnie Dorr has volunteered to serve also hope to increase the submissions importance of forming a mission as symposium cochair.

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.

In his recent papers, entitled Intelligence without Representation and Intelligence without Reason, Brooks argues for mobile robots as the foundation of AI research. This article argues that even if we seek to investigate complete agents in real-world environments, robotics is neither necessary nor sufficient as a basis for AI research. The article proposes real-world software environments, such as operating systems or databases, as a complementary substrate for intelligent-agent research and considers the relative advantages of software environments as test beds for AI. First, the cost, effort, and expertise necessary to develop and systematically experiment with software artifacts are relatively low. Second, software environments circumvent many thorny but peripheral research issues that are inescapable in physical environments. Brooks's mobile robots tug AI toward a bottom-up focus in which the mechanics of perception and mobility mingle inextricably with or even supersede core AI research. In contrast, the softbots (software robots) I advocate facilitate the study of classical AI problems in real-world (albeit, software) domains. For example, the UNIX softbot under development at the University of Washington has led us to investigate planning with incomplete information, interleaving planning and execution, and a host of related high-level issues.

Terminological knowledge representation systems (TKRSs) are tools for designing and using knowledge bases that make use of terminological languages (or concept languages). We analyze from a theoretical point of view a TKRS whose capabilities go beyond the ones of presently available TKRSs. The new features studied, often required in practical applications, can be summarized in three main points. First, we consider a highly expressive terminological language, called ALCNR, including general complements of concepts, number restrictions and role conjunction. Second, we allow to express inclusion statements between general concepts, and terminological cycles as a particular case. Third, we prove the decidability of a number of desirable TKRS-deduction services (like satisfiability, subsumption and instance checking) through a sound, complete and terminating calculus for reasoning in ALCNR-knowledge bases. Our calculus extends the general technique of constraint systems. As a byproduct of the proof, we get also the result that inclusion statements in ALCNR can be simulated by terminological cycles, if descriptive semantics is adopted.

To support the goal of allowing users to record and retrieve information, this paper describes an interactive note-taking system for pen-based computers with two distinctive features. First, it actively predicts what the user is going to write. Second, it automatically constructs a custom, button-box user interface on request. The system is an example of a learning-apprentice software- agent. A machine learning component characterizes the syntax and semantics of the user's information. A performance system uses this learned information to generate completion strings and construct a user interface. Description of Online Appendix: People like to record information. Doing this on paper is initially efficient, but lacks flexibility. Recording information on a computer is less efficient but more powerful. In our new note taking softwre, the user records information directly on a computer. Behind the interface, an agent acts for the user. To help, it provides defaults and constructs a custom user interface. The demonstration is a QuickTime movie of the note taking agent in action. The file is a binhexed self-extracting archive. Macintosh utilities for binhex are available from mac.archive.umich.edu. QuickTime is available from ftp.apple.com in the dts/mac/sys.soft/quicktime.

This workshop brought together 20 computer scientists, psychologists, and human-computer interaction (HCI) researchers to exchange results and views on human error and judgment bias. Human error is typically studied when operators undertake actions, but judgment bias is an issue in thinking rather than acting. Both topics are generally ignored by the HCI community, which is interested in designs that eliminate human error and bias tendencies. As a result, almost no one at the workshop had met before, and the discussion for most participants was novel and lively. Many areas of previously unexamined overlap were identified. An agenda of research needs was also developed.

PROSE is a knowledge-based configurator platform for telecommunications products. Its outstanding feature is a product knowledge base written in C-classIC, a frame-based knowledge representation system in the KL-ONE family of languages. It is one of the first successful products using a KL-ONE style language. Unlike previous configurator applications, the PROSE knowledge base is in a purely declarative form that provides developers with the ability to add knowledge quickly and consistently. The PROSE architecture is general and is not tied to any specific telecommunications product. As such, it is being reused to develop configurators for several different products. Finally, PROSE not only generates configurations from just a few high-level parameters, but it can also verify configurations produced manually by customers, engineers, or salespeople. The same product knowledge, encoded in C-classIC, supports both the generation and the verification of product configurations.

PITCH EXPERT was developed to make expertise available to mill-site engineers to solve pitch problems in kraft pulp mills. These problems have been estimated to cause losses to the Canadian pulp and paper industry in excess of $80 million each year. The design of the system took into account not only the complexity of the process interactions and the need for accuracy and completeness of recommendations but also the ongoing need for training mill personnel and the requirement that the system be maintainable and expandable without the constant involvement of the developers. PITCH EXPERT is now accessible by modem, and the savings achieved through use of the system covered the development costs within six months of release.