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.

As real logic programmers normally use cut (!), an effective learning procedure for logic programs should be able to deal with it. Because the cut predicate has only a procedural meaning, clauses containing cut cannot be learned using an extensional evaluation method, as is done in most learning systems. On the other hand, searching a space of possible programs (instead of a space of independent clauses) is unfeasible. An alternative solution is to generate first a candidate base program which covers the positive examples, and then make it consistent by inserting cut where appropriate. The problem of learning programs with cut has not been investigated before and this seems to be a natural and reasonable approach. We generalize this scheme and investigate the difficulties that arise. Some of the major shortcomings are actually caused, in general, by the need for intensional evaluation. As a conclusion, the analysis of this paper suggests, on precise and technical grounds, that learning cut is difficult, and current induction techniques should probably be restricted to purely declarative logic languages.

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.

The Association for the Advancement of Artificial Intelligence (AAAI) held its 1993 Spring Symposium Series on March 23-25 at Stanford University. This article contains summaries of the eight symposia that were conducted: AI and Creativity, AI and NP-Hard Problems, Building Lexicons for Machine Translation, Case-Based Reasoning and Information Retrieval, Foundations of Automatic Planning, Innovative Applications of Massive Parallelism, Reasoning about Mental States, and Training Issues in Incremental Learning. Technical reports of the symposia AI and Creativity, Building Lexicons for Machine Translation, Case-Based Reasoning and Information Retrieval, Foundations of Automatic Planning, Innovative Applications of Massive Parallelism, Reasoning about Mental States, and Training Issues in Incremental Learning are available from AAAI.

In 1991, Ford Motor Company began deployment of CAPE (computer-aided parts estimating system), a highly advanced knowledge-based system designed to generate, evaluate, and cost automotive part manufacturing plans. CAPE is a highly significant system for Ford of Europe in terms of the business needs it satisfies and the corporate acceptance of AI applications: First, CAPE represents a major investment, with significant person-years of effort spent on predeployment development alone. Second, CAPE is the first large-scale production expert system to be deployed within Ford of Europe. CAPE reduces estimating response time by 50 percent.

My dissertation concerns the application of constraint-based reasoning to parametric engineering design (Murtagh 1991).1 It deals with the practical application of constraint networks, using automated reasoning to overcome some of the blind spots in conventional iterative design.

The Fifth Annual Conference on Innovative Applications of Artificial Intelligence (IAAI) certainly reflected the current state of the commercialization of AI. A new crop of award-winning applications stood as a testimonial to the continuing inroads AI is making into business, saving hundreds of millions of dollars annually. In addition, attendees were introduced to a number of new AI technologies, such as data mining, that are coming out of the research lab and being readied for use.

This article describes Compaq QUICKSOURCE, an electronic problem-solving and information system for Compaq's line of networked printers. A major goal in designing this system was to empower Compaq's customers with expert system technology, allowing them to solve advanced network printer problems entirely on their own. In its first-generation system, SMART, the objective was to provide expert knowledge to Compaq's help-desk operation to better and more quickly answer customer calls and problems. Because the product would be used by a diverse and heterogeneous set of users, a significant amount of human factors research and analysis was performed as part of system design and implementation.