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.

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.

Casting neural network weights in symbolic terms is crucial for interpreting and explaining the behavior of a network. Additionally, in some domains, a symbolic description may lead to more robust generalization. We present a principled approach to symbolic rule extraction based on the notion of weight templates, parameterized regions of weight space corresponding to specific symbolic expressions. With an appropriate choice of representation, we show how template parameters may be efficiently identified and instantiated to yield the optimal match to a unit's actual weights.

Casting neural network weights in symbolic terms is crucial for interpreting and explaining the behavior of a network. Additionally, in some domains, a symbolic description may lead to more robust generalization. We present a principled approach to symbolic rule extraction based on the notion of weight templates, parameterized regions of weight space corresponding to specific symbolic expressions. With an appropriate choice of representation, we show how template parameters may be efficiently identified and instantiated to yield the optimal match to a unit's actual weights.

Casting neural network weights in symbolic terms is crucial for interpreting and explaining the behavior of a network. Additionally, in some domains, a symbolic description may lead to more robust generalization. We present a principled approach to symbolic rule extraction based on the notion of weight templates, parameterized regions of weight space corresponding to specific symbolic expressions. With an appropriate choice of representation, we show how template parameters may be efficiently identified and instantiated to yield the optimal match to a unit's actual weights. Depending on the requirements of the application domain, our method can accommodate arbitrary disjunctions and conjunctions with O(k) complexity, simple n-of-m expressions with O(k!)

The Financial Crimes Enforcement Network (FIN-CEN) AI system (FAIS) links and evaluates reports of large cash transactions to identify potential money laundering. The objective of FAIS is to discover previously unknown, potentially high-value leads for possible investigation. FAIS integrates intelligent human and software agents in a cooperative discovery task on a very large data space. It is a complex system incorporating several aspects of AI technology, including rule-based reasoning and a blackboard. FAIS consists of an underlying database (that functions as a black-board), a graphic user interface, and several preprocessing and analysis modules. FAIS has been in operation at FINCEN since March 1993; a dedicated group of analysts process approximately 200,000 transactions a week, during which time over 400 investigative support reports corresponding to over $1 billion in potential laundered funds were developed. FAIS's unique analytic power arises primarily from a change in view of the underlying data from a transaction-oriented perspective to a subject-oriented (that is, person or organization) perspective.

Daewoo Shipbuilding Company, one of the largest shipbuilders in the world, has experienced great deal of trouble with the planning and scheduling of its production process. To solve the problems, from 1991 to 1993, Korea Advanced Institute of Science and Technology (KAIST) and Daewoo jointly conducted the Daewoo Shipbuilding Scheduling (das) Project. To integrate the scheduling expert systems for shipbuilding, we used a hierarchical scheduling architecture. To automate the dynamic spatial layout of objects in various areas of the shipyard, we developed spatial scheduling expert systems. For reliable estimation of person-hour requirements, we implemented the neural network-based person-hour estimator. In addition, we developed the paneled-block assembly shop scheduler and the long-range production planner. For this large-scale project, we devised a phased development strategy consisting of three phases: (1) vision revelation, (2) data-dependent realization, and (3) prospective enhancement. The DAS systems were successfully launched in January 1994 and are actively being used as indispensable systems in the shipyard, resulting in significant improvement in productivity and visible and positive effects in many areas.

We describe a machine learning method for predicting the value of a real-valued function, given the values of multiple input variables. The method induces solutions from samples in the form of ordered disjunctive normal form (DNF) decision rules. A central objective of the method and representation is the induction of compact, easily interpretable solutions. This rule-based decision model can be extended to search efficiently for similar cases prior to approximating function values. Experimental results on real-world data demonstrate that the new techniques are competitive with existing machine learning and statistical methods and can sometimes yield superior regression performance.

Learning and reasoning are both aspects of what is considered to be intelligence. Their studies within AI have been separated historically, learning being the topic of machine learning and neural networks, and reasoning falling under classical (or symbolic) AI. However, learning and reasoning are in many ways interdependent. This paper discusses the nature of some of these interdependencies and proposes a general framework called FLARE, that combines inductive learning using prior knowledge together with reasoning in a propositional setting. Several examples that test the framework are presented, including classical induction, many important reasoning protocols and two simple expert systems.

Countrywide loan-underwriting expert system (clues) is an advanced, automated mortgage-underwriting rule-based expert system. The system was developed to increase the production capacity and productivity of Countrywide branches, improve the consistency of underwriting, and reduce the cost of originating a loan. The system receives selected information from the loan application, credit report, and appraisal. It then decides whether the loan should be approved or whether it requires further review by a human underwriter. If the system approves the loan, no further review is required, and the application is funded. clues has been in operation since February 1993 and is currently processing more than 8500 loans each month in over 300 decentralized branches around the country.