Showing papers in "Ai Magazine in 1996"
TL;DR: An overview of this emerging field is provided, clarifying how data mining and knowledge discovery in databases are related both to each other and to related fields, such as machine learning, statistics, and databases.
Abstract: ■ Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. What is all the excitement about? This article provides an overview of this emerging field, clarifying how data mining and knowledge discovery in databases are related both to each other and to related fields, such as machine learning, statistics, and databases. The article mentions particular real-world applications, specific data-mining techniques, challenges involved in real-world applications of knowledge discovery, and current and future research directions in the field.
4,782 citations
TL;DR: A wide range of existing solutions to the metalevel control problem are outlined and current work that is aimed at increasing the applicability of anytime computation is described.
Abstract: Anytime algorithms give intelligent systems the capability to trade deliberation time for quality of results. This capability is essential for successful operation in domains such as signal interpretation, real-time diagnosis and repair, and mobile robot control. What characterizes these domains is that it is not feasible (computationally) or desirable (economically) to compute the optimal answer. This article surveys the main control problems that arise when a system is composed of several anytime algorithms. These problems relate to optimal management of uncertainty and precision. After a brief introduction to anytime computation, I outline a wide range of existing solutions to the metalevel control problem and describe current work that is aimed at increasing the applicability of anytime computation.
739 citations
TL;DR: The problem of formalizing context is surveyed and what is needed for an acceptable account of this abstract notion is explored.
Abstract: The importance of contextual reasoning is emphasized by various researchers in AI. (A partial list includes John McCarthy and his group, R. V. Guha, Yoav Shoham, Giuseppe Attardi and Maria Simi, and Fausto Giunchiglia and his group.) Here, we survey the problem of formalizing context and explore what is needed for an acceptable account of this abstract notion.
191 citations
TL;DR: This article reports on the progress of the checkers (8 3 8 draughts) program CHINOOK since 1992, which is the first time in history that a computer has won a human-world championship.
Abstract: In 1992, the seemingly unbeatable World Checker Champion Marion Tinsley defended his title against the computer program CHINOOK After an intense, tightly contested match, Tinsley fought back from behind to win the match by scoring four wins to CHINOOK's two, with 33 draws This match was the first time in history that a human world champion defended his title against a computer This article reports on the progress of the checkers (8 3 8 draughts) program CHINOOK since 1992 Two years of research and development on the program culminated in a rematch with Tinsley in August 1994 In this match, after six games (all draws), Tinsley withdrew from the match and relinquished the world championship title to CHINOOK,citing health concerns CHINOOK has since defended its title in two subsequent matches It is the first time in history that a computer has won a human-world championship
146 citations
TL;DR: It is argued that collaboration must be designed into systems from the start; it cannot be patched on and research on, and the development of, collaborative systems should itself be a collaborative endeavor.
Abstract: The construction of computer systems that are intelligent, collaborative problem-solving partners is an important goal for both the science of AI and its application. From the scientific perspective, the development of theories and mechanisms to enable building collaborative systems presents exciting research challenges across AI subfields. From the applications perspective, the capability to collaborate with users and other systems is essential if large-scale information systems of the future are to assist users in finding the information they need and solving the problems they have. In this address, it is argued that collaboration must be designed into systems from the start; it cannot be patched on. Key features of collaborative activity are described, the scientific base provided by recent AI research is discussed, and several of the research challenges posed by collaboration are presented. It is further argued that research on, and the development of, collaborative systems should itself be a collaborative endeavor -- within AI, across subfields of computer science, and with researchers in other fields.
111 citations
TL;DR: Self-modeling and self-configuration, autonomic functions coordinated through symbolic reasoning, and compositional, model-based programming are the three key elements of a model- based autonomous system architecture that is taking us into the new millennium.
Abstract: A new generation of sensor-rich, massively distributed, autonomous systems are being developed that have the potential for profound social, environmental, and economic change. These systems include networked building energy systems, autonomous space probes, chemical plant control systems, satellite constellations for remote ecosystem monitoring, power grids, biospherelike life-support systems, and reconfigurable traffic systems, to highlight but a few. To achieve high performance, these immobile robots (or immobots) will need to develop sophisticated regulatory and immune systems that accurately and robustly control their complex internal functions. Thus, immobots will exploit a vast nervous system of sensors to model themselves and their environment on a grand scale. They will use these models to dramatically reconfigure themselves to survive decades of autonomous operation. Achieving these large-scale modeling and configuration tasks will require a tight coupling between the higher-level coordination function provided by symbolic reasoning and the lower-level autonomic processes of adaptive estimation and control. To be economically viable, they will need to be programmable purely through high-level compositional models. Self-modeling and self-configuration, autonomic functions coordinated through symbolic reasoning, and compositional, model-based programming are the three key elements of a model-based autonomous system architecture that is taking us into the new millennium.
102 citations
TL;DR: This article describes the evolution of this system from a research project in machine learning to a robust driving system capable of executing tactical driving maneuvers such as lane changing and intersection navigation.
Abstract: Giving robots the ability to operate in the real world has been, and continues to be, one of the most difficult tasks in AI research. Since 1987, researchers at Carnegie Mellon University have been investigating one such task. Their research has been focused on using adaptive, vision-based systems to increase the driving performance of the Navlab line of on-road mobile robots. This research has led to the development of a neural network system that can learn to drive on many road types simply by watching a human teacher. This article describes the evolution of this system from a research project in machine learning to a robust driving system capable of executing tactical driving maneuvers such as lane changing and intersection navigation.
44 citations
TL;DR: This work focuses on the automation of the cataloging effort of a major sky survey and the availability of digital libraries in general, and the SKICAT system automates the reduction and analysis of the three terabytes worth of images.
Abstract: The value of scientific digital-image libraries seldom lies in the pixels of images For large collections of images, such as those resulting from astronomy sky surveys, the typical useful product is an online database cataloging entries of interest We focus on the automation of the cataloging effort of a major sky survey and the availability of digital libraries in general The SKICAT system automates the reduction and analysis of the three terabytes worth of images, expected to contain on the order of 2 billion sky objects For the primary scientific analysis of these data, it is necessary to detect, measure, and classify every sky object SKICAT integrates techniques for image processing, classification learning, database management, and visualization The learning algorithms are trained to classify the detected objects and can classify objects too faint for visual classification with an accuracy level exceeding 90 percent This accuracy level increases the number of classified objects in the final catalog threefold relative to the best results from digitized photographic sky surveys to date Hence, learning algorithms played a powerful and enabling role and solved a difficult, scientifically significant problem, enabling the consistent, accurate classification and the ease of access and analysis of an otherwise unfathomable data set
41 citations
TL;DR: The goal of the Workshop on Adaptation and Learning in Multiagent Systems was to focus on research that addresses unique requirements for agents learning and adapting to work in the presence of other agents.
Abstract: The goal of the Workshop on Adaptation and Learning in Multiagent Systems was to focus on research that addresses unique requirements for agents learning and adapting to work in the presence of other agents. Recognizing the applicability and limitations of current machine-learning research as applied to multiagent problems and developing new learning and adaptation mechanisms particularly targeted to this class of problems were the primary research issues that we wanted the authors to address. This article outlines the presentations that were made at the workshop and the success of the workshop in meeting the established goals. Issues that need to be better understood are also presented.
40 citations
TL;DR: The major components of the robot’s navigation architecture are described, which were the culmination of a three-month design and implementation period for an indoor navigation system for topological maps.
Abstract: ■ LOLA’s entry in the Office Delivery event of the 1995 Robot Competition and Exhibition, held in conjunction with the Fourteenth International Joint Conference on Artificial Intelligence, was the culmination of a three-month design and implementation period for an indoor navigation system for topological maps. This article describes the major components of the robot’s navigation architecture. It also summarizes the experiences and lessons learned from the competition.
32 citations
TL;DR: This article presents an innovative research project where computationally elegant algorithms based on the integration of a novel connectionist computing model, mathematical optimization, and a massively parallel computer architecture are used to automate the complex process of engineering design.
Abstract: This article presents an innovative research project (sponsored by the National Science Foundation, the American Iron and Steel Institute, and the American Institute of Steel Construction) where computationally elegant algorithms based on the integration of a novel connectionist computing model, mathematical optimization, and a massively parallel computer architecture are used to automate the complex process of engineering design.
TL;DR: CHIP and its various systems are described and the ways in which these elements combined to produce an effective entry to the robot competition are described.
Abstract: The University of Chicago's robot, CHIP, is part of the Animate Agent Project, aimed at understanding the software architecture and knowledge representations needed to build a general-purpose robotic assistant. CHIP's strategy for the Office Cleanup event of the 1995 Robot Competition and Exhibition was to scan an entire area systematically and, as collectible objects were identified, pick them up and deposit them in the nearest appropriate receptacle. This article describes CHIP and its various systems and the ways in which these elements combined to produce an effective entry to the robot competition.
TL;DR: Woodrow Wilson (Woody) Bledsoe died on 4 October 1995 of ALS, more commonly known as Lou Gehrig's disease, one of the founders of AI, making early contributions in pattern recognition and automated reasoning.
Abstract: Woodrow Wilson (Woody) Bledsoe died on 4 October 1995 of ALS, more commonly known as Lou Gehrig's disease. Woody was one of the founders of AI, making early contributions in pattern recognition and automated reasoning. He continued to make significant contributions to AI throughout his long career. His legacy consists not only of his scientific work but also of several generations of scientists who learned from Woody the joy of scientific research and the way to go about it. Woody's enthusiasm, his perpetual sense of optimism, his can-do attitude, and his deep sense of duty to humanity offered those who knew him the hope and comfort that truly good and great men do exist.
TL;DR: A citation analysis was conducted covering a time period of 5 years to compile 15,600 citations to 1,244 different journals, and the journals are ranked in two ways involving the magnitude and the duration of scientific impact each has had in the field of AI.
Abstract: A significant and growing area of business-computing research is concerned with AI. Knowledge about which journals are the most influential forums for disseminating AI research is important for business school faculty, students, administrators, and librarians. To date, there has been only one study attempting to rank AI journals from a business-computing perspective. It used a subjective methodology, surveying opinions of business faculty about a prespecified list of 30 journals. Here, we report the results of a more objective study. We conducted a citation analysis covering a time period of 5 years to compile 15,600 citations to 1,244 different journals. Based on these data, the journals are ranked in two ways involving the magnitude and the duration of scientific impact each has had in the field of AI.
TL;DR: The 1995 Robot Competition and Exhibition was held in Montreal, Canada, in conjunction with the 1995 International Joint Conference on Artificial Intelligence, designed to demonstrate state-of-the-art autonomous mobile robots, highlighting such tasks as goal-directed navigation, feature detection, object recognition, identification, and physical manipulation.
Abstract: The 1995 Robot Competition and Exhibition was held in Montreal, Canada, in conjunction with the 1995 International Joint Conference on Artificial Intelligence. The competition was designed to demonstrate state-of-the-art autonomous mobile robots, highlighting such tasks as goal-directed navigation, feature detection, object recognition, identification, and physical manipulation as well as effective human-robot communication. The competition consisted of two separate events: (1) Office Delivery and (2) Office Cleanup. The exhibition also consisted of two events: (1) demonstrations of robotics research that was not related to the contest and (2) robotics focused on aiding people who are mobility impaired. There was also a Robotics Forum for technical exchange of information between robotics researchers. Thus, this year's events covered the gamut of robotics research, from discussions of control strategies to demonstrations of useful prototype application systems.
TL;DR: The general consensus was that hybrid connectionist-symbolic models constitute a promising avenue to the development of more robust, more powerful, and more versatile architectures for both cognitive modeling and intelligent systems.
Abstract: The Workshop on Connectionist-Symbolic Integration: From Unified to Hybrid Approaches was held on 19 to 20 August 1995 in Montreal, Canada, in conjunction with the Fourteenth International Joint Conference on Artificial Intelligence. The focus of the workshop was on learning and architectures that feature hybrid representations and support hybrid learning. The general consensus was that hybrid connectionist-symbolic models constitute a promising avenue to the development of more robust, more powerful, and more versatile architectures for both cognitive modeling and intelligent systems.
TL;DR: A case study of Reuter experience in putting a global knowledge organization in place, building knowledge bases at multiple distributed sites, deploying these knowledge bases in multiple sites around the world, and maintaining and enhancing knowledge bases within a global organizational framework is presented in this article.
Abstract: Reuters is a worldwide company focused on supplying financial and news information to its more than 40,000 subscribers around the world. To enhance the quality and consistency of its customer- support organization, Reuters embarked on a global knowledge development and reuse project. The resulting system is in operational use in North America, Europe, and Asia. The system supports 38 Reuter products worldwide. This article presents a case study of Reuter experience in putting a global knowledge organization in place, building knowledge bases at multiple distributed sites, deploying these knowledge bases in multiple sites around the world, and maintaining and enhancing knowledge bases within a global organizational framework. This project is the first to address issues in multicountry knowledge development and maintenance and multicountry knowledge deployment. These issues are critical for global companies to understand, address, and resolve to effectively gain the benefits of global knowledge systems.
TL;DR: Many key cement properties are captured within the Fourier transform infrared spectra of cement powders and can be predicted from these spectra using suitable neural network techniques, which provide the basis for a valuable quality control tool now finding commercial use in the oil field.
Abstract: Inherent batch-to-batch variability, aging, and contamination are major factors contributing to variability in oil-field cement-slurry performance. Of particular concern are problems encountered when a slurry is formulated with one cement sample and used with a batch having different properties. Such variability imposes a heavy burden on performance testing and is often a major factor in operational failure. We describe methods that allow the identification, characterization, and prediction of the variability of oil-field cements. Our approach involves predicting cement compositions, particle-size distributions, and thickening-time curves from the diffuse reflectance infrared Fourier transform spectrum of neat cement powders. Predictions make use of artificial neural networks. Slurry formulation thickening times can be predicted with uncertainties of less than 10 percent. Composition and particle-size distributions can be predicted with uncertainties a little greater than measurement error, but general trends and differences between cements can be determined reliably. Our research shows that many key cement properties are captured within the Fourier transform infrared spectra of cement powders and can be predicted from these spectra using suitable neural network techniques. Several case studies are given to emphasize the use of these techniques, which provide the basis for a valuable quality control tool now finding commercial use in the oil field.
TL;DR: The ideas and design philosophy that went into LOLA borrow heavily from those of previous competitors' to which it is greatly indebted, and these methods and ideas are discussed here.
Abstract: LOLA won the Office Cleanup event at the 1995 Robot Competition and Exhibition, held as part of the Fourteenth International Conference on Artificial Intelligence The event called for a robot to pick up trash in an unstructured environment and sort it such that the recyclable trash winded up in the recycle bin and the regular trash in the trash bin The only allowable information lola was given beforehand were model-based descriptions of the trash and recyclables, which it located using color vision Much of LOLA's success can be attributed to the simple, fast algorithms and methods that also model sensor uncertainty The ideas and design philosophy that went into LOLA borrow heavily from those of previous competitors' to which we are greatly indebted These methods and ideas are discussed here
TL;DR: A short description of CAIR-2's hardware, system and control architecture, realtime vision, and speech recognizer is presented.
Abstract: CAIR-2 from the Korea Advanced Institute of Science and Technology (KAIST) placed first in the Office Delivery event at the 1995 Robot Competition and Exhibition, held in conjunction with the Fourteenth International Joint Conference on Artificial Intelligence (IJCAI-95). CAIR-2 is a totally self-contained and autonomous mobile robot, and its control architecture incorporates both behavior-based and planner-based approaches. In this article, we present a short description of CAIR-2's hardware, system and control architecture, realtime vision, and speech recognizer.
TL;DR: The goals of the meeting were to understand limitations of current reinforcement-learning systems and define promising directions for further research, clarify the relationships between reinforcement learning and existing work in engineering fields, such as operations research, and identify potential industrial applications of reinforcement learning.
Abstract: Reinforcement learning has become one of the most actively studied learning frameworks in the area of intelligent autonomous agents This article describes the results of a three-day meeting of leading researchers in this area that was sponsored by the National Science Foundation Because reinforcement learning is an interdisciplinary topic, the workshop brought together researchers from a variety of fields, including machine learning, neural networks, AI, robotics, and operations research Thirty leading researchers from the United States, Canada, Europe, and Japan, representing from many different universities, government, and industrial research laboratories participated in the workshop The goals of the meeting were to (1) understand limitations of current reinforcement-learning systems and define promising directions for further research; (2) clarify the relationships between reinforcement learning and existing work in engineering fields, such as operations research; and (3) identify potential industrial applications of reinforcement learning
TL;DR: The author begins with an historical review of the conference, then goes on to discuss the role of the IAAI conference, including an examination of the relationship between AI scientific research and the application of AI technology.
Abstract: This article is a reflection on the goals and focus of the Innovative Applications of Artificial Intelligence (IAAI) Conference. The author begins with an historical review of the conference. He then goes on to discuss the role of the IAAI conference, including an examination of the relationship between AI scientific research and the application of AI technology. He concludes with a presentation of the new vision for the IAAI conference.
TL;DR: The COMET system applies a model-based reasoning approach to the analysis of accounting systems and their controls to automatically analyze the effectiveness of the controls in detecting potential errors.
Abstract: An important problem faced by auditors is gauging how much reliance can be placed on the accounting systems that process millions of transactions to produce the numbers summarized in a company's financial statements. Accounting sys-ems contain internal controls, procedures designed to detect and correct errors and irregularities that can occur in the processing of transactions. In a complex accounting system, it can be an extremely difficult task for the auditor to anticipate the possible errors that can occur and evaluate the effectiveness of the controls at detecting them. An accurate analysis must take into account the unique features of each company's business processes. To cope with this complexity and variability, the COMET system applies a model-based reasoning approach to the analysis of accounting systems and their controls. An auditor uses COMET to create a hierarchical flowchart model that describes the intended processing of business transactions by an accounting system and the operation of its controls. COMET uses the constructed model to automatically analyze the effectiveness of the controls in detecting potential errors. Price Waterhouse auditors have used COMET on a variety of real audits in several countries around the world.
TL;DR: The volume provides broad coverage of experimental design and statistics, ranging from a gentle introduction of basic ideas to a detailed presentation of advanced techniques, often combined with illustrative examples of their application to the empirical study of AI.
Abstract: Paul Cohen's book Empirical Methods for Artificial Intelligence aims to encourage this trend by providing AI practitioners with the knowledge and tools needed for careful empirical evaluation. The volume provides broad coverage of experimental design and statistics, ranging from a gentle introduction of basic ideas to a detailed presentation of advanced techniques, often combined with illustrative examples of their application to the empirical study of AI. The book is generally well written, clearly organized, and easy to understand; it contains some mathematics -- but not enough to overwhelm readers. Examples come from AI work on planning, machine learning, natural language, and diagnosis.
TL;DR: An expert system named BMES was developed to diagnose the failures of information delivery in a system, called COMLINK, that distributes a daily stream of documents released by the Office of Media Affairs.
Abstract: As part of a collaboration with the White House Office of Media Affairs, members of the Artificial Intelligence Laboratory at the Massachusetts Institute of Technology designed a system, called COMLINK, that distributes a daily stream of documents released by the Office of Media Affairs. Approximately 4,000 direct subscribers receive information from this service, but more than 100,000 people receive the information through redistribution channels. The information is distributed through e-mail and the World Wide Web. In such a large-scale distribution scheme, there is a constant problem of subscriptions becoming invalid because the user's e-mail account has terminated. These invalid subscriptions cause a backwash of hundreds of bounced-mail messages each day that must be processed by the operators of the COMLINK system. To manage this annoying but necessary task, an expert system named BMES was developed to diagnose the failures of information delivery.
TL;DR: The supply chain integrated ordering network (SCION) depot-bookings system automates the planning and scheduling of perishable and nonperishable commodities and the vehicles that carry them into J. Sainsbury depots as mentioned in this paper.
Abstract: The supply-chain integrated ordering network (SCION) depot-bookings system automates the planning and scheduling of perishable and nonperishable commodities and the vehicles that carry them into J. Sainsbury depots. This initiative is strategic, enabling the business to make the key move from weekly to daily ordering. The system is mission critical, managing the inward flow of commodities from suppliers into J. Sainsbury's depots. The system leverages AI techniques to provide a business solution that meets challenging functional and performance needs. The SCION depot-bookings system is operational, providing schedules for 22 depots across the United Kingdom.
TL;DR: The goal of the Fourth International Conference on User Modeling (UM94) was to bring together user-modeling researchers from different parts of the field to discuss and learn about each other's research, contrast approaches, and develop a basis for future research and collaboration.
Abstract: The goal of the Fourth International Conference on User Modeling (UM94) was to bring together user-modeling researchers from different parts of the field to discuss and learn about each other's research, contrast approaches, and develop a basis for future research and collaboration. A broad international audience of more than 110 people attended the conference, which featured system demonstrations, paper and poster sessions, three tutorials, and a set of special interest group meetings.
TL;DR: It is argued that recent trends in KR instead demonstrate the benefits of the interplay between science and engineering, a lesson from which all AI could benefit.
Abstract: As a field, knowledge representation has often been accused of being off in a theoretical no-man's land, removed from, and largely unrelated to, the central issues in AI. This article argues that recent trends in KR instead demonstrate the benefits of the interplay between science and engineering, a lesson from which all AI could benefit. This article grew out of a survey talk on the Third International Conference on Knowledge Representation and Reasoning (KR-92) (Nebel, Rich, and Swartout 1992) that I presented at the Thirteenth International Joint Conference on Artificial Intelligence (IJCAI-93).
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TL;DR: The 1994 Workshop on Case-Based Reasoning focused on the evaluation of CBR theories, models, systems, and system components, with the consensus that a balance between novel innovations and evaluations could maximize progress.
Abstract: The 1994 Workshop on Case-Based Reasoning (CBR) focused on the evaluation of CBR theories, models, systems, and system components The CBR community addressed the evaluation of theories and implemented systems, with the consensus that a balance between novel innovations and evaluations could maximize progress
TL;DR: The Association for the Advancement of Artificial Intelligence (AAAI) held its 1995 Fall Symposia Series on 10 to 12 November in Cambridge, Massachusetts and this article contains summaries of the eight symposia that were conducted.
Abstract: The Association for the Advancement of Artificial Intelligence (AAAI) held its 1995 Fall Symposia Series on 10 to 12 November in Cambridge, Massachusetts. This article contains summaries of the eight symposia that were conducted: (1) Active Learning; (2) Adaptation of Knowledge for Reuse; (3) AI Applications in Knowledge Navigation and Retrieval; (4) Computational Models for Integrating Language and Vision; (5) Embodied Language and Action Symposium; (6) Formalizing Context; (7) Genetic Programming; and (8) Rational Agency: Concepts, Theories, Models, and Applications.