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Historical Projects

A number of major projects are now considered completed work; their goals have been met, and our research attention has moved on to new areas.

DENDRAL (1965-83)

The DENDRAL Project was one of the earliest expert systems. DENDRAL began as an effort to explore the mechanization of scientific reasoning and the formalization of scientific knowledge by working within a specific domain of science, organic chemistry. Another concern was to use AI methodology to understand better some fundamental questions in the philosophy of science, including the process by which explanatory hypotheses are discovered or judged adequate. After more than a decade of collaboration among chemists, geneticists, and computer scientists, DENDRAL had become not only a successful demonstration of the power of rule-based expert systems but also a significant tool for molecular structure analysis, in use in both academic and industrial research labs. Using a plan-generate-test search paradigm and data from mass spectrometry and other sources, DENDRAL proposes plausible candidate structures for new or unknown chemical compounds. Its performance rivals that of human experts for certain classes of organic compounds and has resulted in a number of papers that were published in the chemical literature. Although no longer a topic of academic research, the most recent version of the interactive structure generator, GENOA, has been licensed by Stanford University for commercial use.

META-DENDRAL (1970-76)

META-DENDRAL is an inductive prograrn that automatically formulates new rules for DENDRAL to use in explaining data about unknown chemical compounds. Using the plan- generate-test paradigm, META-DENDRAL has successfully formulated rules of mass spectrometry, both by rediscovering existing rules and by proposing entirely new rules. Although META-DENDRAL is no longer an active program, its contributions to ideas about learning and discovery are being applied to new domains. Among these ideas are that induction can be automated as heuristic search; that, for efflciency, search can be broken into two steps--approximate and refined; that learning must be able to cope with noisy and incomplete data; and that learning multiple concepts at the same time is sometimes inescapable.

MYCIN (1972-80)

MYCIN is an interactive program that diagnoses certain infectious diseases, prescribes antimicrobial therapy, and can explain its reasoning in detail. In a controlled test, its performance equalled that of specialists. In addition, the MYCIN program incorporated several important AI developments. MYCIN extended the notion that the knowledge base should be separate from the inference engine, and its rule-based inference engine was built on a backward-chaining, or goal-directed, control strategy. Since it was designed as a consultant for physicians, MYCIN was given the ability to explain both its line of reasoning and its knowledge. Because of the rapid pace of developments in medicine, the knowledge base was designed for easy augmentation. And because medical diagnosis often involves a degree of uncertainty, MYCIN's rules incorporated certainty factors to indicate the importance (i.e., likelihood and risk) of a conclusion. Although MYCIN was never used routinely by physicians, it has substantially influenced other AI research. At the HPP, MYCIN led to work in TEIRESIAS, EMYCIN, PUFF, CENTAUR, VM, GUIDON, and SACON, all described below, and to ONCOCIN and ROGET. The book Rule-Based Expert Sytem: The MYCIN Experiment at the Stanford Heuristic Programming Project describes the decade of research on MYCIN and its descendants.

TEIRESIAS (1974-77)

The knowledge acquisition program TEIRESIAS was built to assist domain experts in refining the MYCIN knowledge base. TEIRESIAS developed the concept of metalevel knowledge, i.e., knowledge by which a program can not only use its knowledge directly, but can examine it, reason about it, and direct its use. TEIRESIAS makes clear the line of reasoning used in making a diagnosis and aids physician experts in modifying or adding to the knowledge base. Much of this was incorporated into the EMYCIN framework. The flexibility and understandability that TEIRESIAS introduced into the knowledge base debugging process have been models for the design of many expert systems.

EMYCIN (1974-79)

The core inference engine of MYCIN, together with a knowledge engineering interface, was developed under the name EMYCIN, or "Essential MYCIN." It is a domain-independent framework that can be used to build rule-based expert systems for consultation problems such as those encountered in diagnosis or troubleshooting. EMYCIN continues to be a primary example of software that can facilitate building expert systems and has been used in a variety of domains, both medical (e.g., PUFF) and nonmedical (e.g., SACON). The system has been widely distributed in the U.S. and abroad and is the basis for the Texas Instruments software system called Personal Consultant.

PUFF (1977-79)

The PUFF system was the first program built using EMYCIN. PUFF's domain is the interpretation of pulmonary function tests for patients with lung disease. The program can diagnose the presence and severity of lung disease and produce reports for the patient's file. Once the rule set for this domain had been developed and debugged, PUFF was transferred to a minicomputer at Pacific Medical Center in San Francisco, where it is used routinely to aid with interpretation of pulmonary function tests. A version of PUFF has been licensed for commercial use.

CENTAUR (1977-80)

The CENTAUR system was designed to experiment with an expert system that combines both rule- and frame-based approaches to represent and use knowledge about medicine and medical diagnostic strategies. For purposes of comparison, CENTAUR was developed for the same task domain as PUFF, interpretation of pulmonary function tests. CENTAUR performed well, demonstrating the effectiveness of this representation and control methodology.

VM (1977-81)

The Ventilator Manager (VM) program interprets online quantitative data in the intensive care unit (ICU) and advises physicians on the management of post-surgical patients needing a mechanical ventilator to help them breathe. While based on the MYCIN architecture, VM was redesigned to allow for the description of events that change over time. Thus, it can monitor the progress of a patient, interpret data in the context of the patient's present and past condition, and suggest adjustments to therapy. VM was tested in the surgical ICU at Pacific Medical Center in San Francisco. Some of the program's concepts have been built directly into more recent respiratory monitoring devices.

GUIDON (1977-81)

GUIDON is an experimental program intended to make available to students the expertise contained in EMYCIN-based systems. GUIDON incorporates separate knowledge bases for the domain itself and for tutoring, and engages the student in a dialogue that presents dornain knowledge in an organized way over a number of sessions. Using the MYCIN knowledge base as the domain to be taught, work in GUIDON explored several issues in intelligent computer-assisted instruction (ICAI), including means for structuring and planning a dialogue, generating teaching material, constructing and verifying a model of what the student knows, and explaining expert reasoning. Although GUIDON was successful in many respects, it also revealed that the diagnostic strategies and some of the medical knowledge that were contained implicitly in the MYCIN rules had to be made explicit in order for students to understand and remember them easily. As a result, a new expert system, NEOMYCIN, has been developed.

SACON (1977-78)

SACON (for Structural Analysis CONsultant) was implemented as a test of the EMYCIN framework in an engineering context. SACON advised structural engineers on the use of MARC, a large structural analysis program, and has served as a prototype of many advisory systems.

MOLGEN (1975-84)

The MOLGEN project has applied AI methods to research in molecular biology. Initial work focused on acquiring and representing the expert knowledge needed to design and simulate experiments in the domain. This led to the development of UNITS, described below. The second phase of research resulted in two expert systems, representing distinct approaches to the design of genetic experiments. One system used "skeletal plans," which are abstracted outlines of experiment designs that can be applied to specific experimental goals and environments. The other system was based on planning with constraints, in which planning decisions are made in the spaces of overall strategy, domain-independent decisions, and domain-dependent laboratory decisions, and the interaction of separate steps or subproblems of an experiment constitute constraints on the overall problem. These two systems were later synthesized into a third system, called SPEX. Current work, known as MOLGEN-II (see the section "The Heuristic Programming Project"), is investigating the process of theory formation in molecular biology.

UNITS (1975-81)

The frame-based UNITS system was developed in the MOLGEN project as a general- purpose knowledge representation, acquisition, and manipulation tool. Designed for use by domain experts with little previous knowledge of computers, it provides an interface that allows the expert to describe both factual and heuristic knowledge. It contains both domain- independent and domain-specific components, including modified English rules for describing the procedural knowledge. UNITS has been licensed by Stanford University for commercial development.

AM (1974-80)

The AM program explored machine learning by discovery in the domain of elementary mathematics. Using a framework of 243 heuristic rules, AM successfully proposed plausible new mathematical concepts, gathered data about them, noticed regularities, and, completing this cycle, found ways of shortening the statement of those hypotheses by making new definitions. However, AM was not able to generate new heuristics. This failing was found to be inherent in the design of AM; related work on discovering new heuristics was done as part of EURISKO.

EURISKO (1978-84)

A successor to AM, EURISKO has also investigated automatic discovery, with a particular emphasis on heuristics, their representation, and the part played by analogy in their discovery. Several hundred heuristics, mostly related to functions, design, and simulation, guide EURISKO in applying its knowledge in several domains. In each domain, the program has three levels of task to perform: working at the domain level to solve problems; inventing new domain concepts; and synthesizing new heuristics that are specific and powerful enough to aid in handling tasks in the domain. EURISKO has been applied to elementary mathematics; programming, where it has uncovered several Lisp bugs; naval fleet design, where it has reigned undefeated in the Traveller Trillion Credit Squadron tournament; VLSI design, where it has come up with some novel and potentially useful three-dimensional devices; oil-spill cleanup; and a few other domains.

RLL (1978-80)

RLL (for Representation Language Language) is a prototype tool for building customized representation languages. RLL is self-descriptive, i.e., it is itself described in terms of RLL units. It has been used as the underlying language for EURISKO and other systems.

Contract Nets (1976-79)

The Contract Nets architecture is an early contribution to work on computer architectures for parallel computation. Recently, it has received much attention in the emerging literature on multiprocessor architectures for symbolic computation. In the Contract Nets architecture, problem solving is distributed among decentralized and loosely coupled processors. These processors communicate about task distribution and answers to subproblems through an interactive negotiation analogous to contract negotiation in the building trades: the "contract" is given to the processor that can handle the task at the lowest system cost, and failure to complete a task results in its reassignment to another processor.

CRYSALIS (1976-83)

The CRYSALIS project explored the power of the blackboard model in interpreting X-ray data from crystallized proteins. The overall strategy was to piece together the three- dimensional molecular structure of a protein by successively refining descriptions of the structure. Although the knowledge base was developed for only a small part of the problem, the blackboard model with its hierarchical control structure was shown to be very powerful for solving such highly complex problems. Results from CRYSALIS are currently being incorporated in other KSL work and have contributed to improved models of control.

AGE (1976-82)

The AGE (for Attempt to GEneralize) project sought to develop a software laboratory for building knowledge-based programs. AGE-1, the knowledge engineering tool that resulted, is designed for building programs that use the blackboard problem-solving framework. It can aid in the construction, debugging, and running of a program. AGE-1 has been used in a number of academic laboratories and for various applications in industry and the defense community.

QUIST (1978-81)

QUIST combines AI and conventional database technology in a system that optimizes queries to large relational databases. QUIST uses heuristics embodying semantic knowledge about the contents of the database to make inferences about the meanings of the terms in a query. It reformultes the original query into an equivalent one whose answer can be found in the database more efficiently. Then conventional query optimization techniques are used to plan an efflcient sequence of retrieval operations.

GLisp (1982-83)

GLisp is a programming language that allows programs to be written in terms of objects and their properties and behavior. The GLisp compiler converts such programs into efficient Lisp code. The compiler has been released to outside users, along with the GEV window-based data inspector, which displays data according to their GLisp description. GLisp is now being distributed from the University of Texas.

Model of Endorsement (1982-85)

The model of endorsement represents and reasons with heuristic knowledge under uncertainty. Instead of associating numerical weights with evidence, the model of endorsement discriminates kinds of evidence and distinguishes the importance of different evidence-gathering situations. Thus, this model's significance is that it examines the question of how to reason about uncertainty, as well as with it. in expert systems.

AI Handbook (1975-82)

The Handbook of Artificial Intelligence was a community effort by KSL (formerly HPP) students and researchers plus collaborators around the country. It describes the fundamental ideas, useful techniques, and exemplary programs from the first 25 years of AI research. Designed for scientists and engineers with no AI background. the three-volume Handbook book contains some 200 articles organized into 15 chapters. Chapters cover such topics as

General Readings

Clancey, W. J., and E. H. Shortliffe. Readings in Medical Artificial Intelligence: The First Decade. Reading, MA: Addison-Wesley, 1984. Feigenbaum, E. A., and P. McCorduck. The Fifth Generation: Artificial Intelligence and Japan's Computer Challenge to the World. Reading, MA: Addison-Wesley, 1983.

Hayes-Roth, F., D. A. Waterman, and D. Lenat, eds. Building Expert Sytems. Reading, MA: Addison-Wesley, 1983. Barr, A., E. A. Feigenbaum, and P. Cohen, eds. The Handbook of Artificial Intelligence, Volumes 1-3. Los Altos, CA: Kaufmann, 1981, 1982. Feigenbaum, E. A. The art of artificial intelligence: 1. Themes and case studies of knowledge engineering. Proceedings IJCAI-77, pp. 1014-1029. (Also published in AFIPS Conf Proceedings: 1978 Computer Conference. Montvale, NJ: AFIPS Press, 1978.)

AGE

Aiello, N., C. Bock, H. P. Nii, and W. White. AGE reference manual. Memo HPP-81-24 (Knowledge Systems Laboratory), October 1981.

Aiello, N., C. Bock, H. P. Nii, and W. White. Joy of AGing: an introduction to AGl system. Memo HPP-81-23 (Knowledge Systems Laboratory), October 1981.

Nii, H. P. Introduction to knowledge engineering, blackboard model, and AGE. Memo HPP- 80-29 (Knowledge Systems Laboratory), March 1980.

Nii, H. P., and N. Aiello. AGE: a knowledge-based program for building knowledge-based programs. Proceedings IJCAI-79, pp. 645-655.

AM

Davis, R., and D. Lenat. Knowledge-Based Systems in Artificial Intelligence: AM and TEIRESIAS. New York: McGraw-Hill, 1982. Blackboard Architecture Blackboard Architecture Hayes-Roth, B. The blackboard model of control. Artificial Intelligence, in press. Hayes-Roth, B. BB-l: an environment for building blackboard systems. Memo HPP-8416 (Knowledge Systems Laboratory), 1984.

Hayes-Roth, B. The blackboard architecture: a general framework for problem-solving? Memo HPP-83-30 (Knowledge Systems Laboratory), May 1983.

CENTAUR

Aikins, J. S. Prototypical knowledge for expert systems. Artificial Intelligence 20(2):163-210 (1983).Contract Nets Smith, R. G. A framework for problem solving in a distributed processing environment. Memo HPP-78-28 (Knowledge Systems Laboratory), December 1978. Also Stanford CS Report STAN-CS-78-700, 1978.

CRYSALIS

Engelmore, R., and A. Terry. Structure and function of the CRYSALIS system. Proceeding IJCA1-79, pp. 25256.

DART/HELIOS

Foyster, G. HELIOS user's manual. Memo HPP-84-34 (Knowledge Systems Laboratory), August 1984. Singh, N. MARS: a multiple abstraction rule-based simulator. Memo HPP-83-43 (Knowledge Systems Laboratory), December 1983. Joyce, R. Reasoning about time-dependent behavior in a system for diagnosing digital hardware faults. Memo HPP-83-37 (Knowledge Systems Laboratory), August 1983.

Genesereth, M. R. Diagnosis using hierarchical design models. Proceedings AAA1-82, pp. 278-283.

Genesereth, M. R. The use of design descriptions in automated diagnosis. Memo HPP-81-20 (Knowledge Systems Laboratory), January 1981.

DENDRAL

[There have been more than 100 publications about DENDRAL, describing both the chemical results obtained using the program and the AI issues explored.]

Lindsay, R. K., B. G. Buchanan, E. A. Feigenbaum, and J. Lederberg. Application of Artificial Intelligence for Chemistry: The DENDRAL Project. New York: McGraw-Hill, 1980.

Gray, N. A. B., D. H. Smith, T. H. Varkony, R. E. Carhart, and B. G. Buchanan. Use of a computer to identify unknown compounds: the automation of scientific inference. Chapter 7 in G. R. Waller and O. C. Dermer, eds., Biomedical Application of Mass Spectrometry. New York: Wiley, 1980.

Buchanan, B. G., and E. A. Feigenbaum. DENDRAL and META-DENDRAL: their applications dimensions. Artificial Intelligence 11:5-24 (1978).

META-DENDRAL

Buchanan, B. G., and T. Mitchell. Model directed learning of production rules. In D. A. Waterman and F. Hayes-Roth, eds., Pattern-Directed Inference System. New York: Academic Press, 1978.

EURISKO

Lenat, D. EURISKO: a program that learns new heuristics and domain concepts. Artificial Intelligence 21(2):61-98 (1983).

Lenat, D. Theory formation by heuristic search. Artificial Intelligence 21(1):31-59 (1983). Lenat, D. The nature of heuristics. Artificial Intelligence 19(2): 189-249 (1981) .

GLisp

Novak, G. S., Jr. GLisp: a high-level language for AI programming. Proceedings AAAI-82, pp. 238-241.

Novak, G. S., Jr. GLisp user's manual. Memo HPP-82-1 (Knowledge Systems Laboratory), January 1982.

GUIDON

Hasling, D., W. J. Clancey, and G. Rennels. Strategic explanations for a diagnostic consultation system. International Journal of Man-Machine Studies 20(1):3-19 (1984).

London, B., and W. J. Clancey. Plan recognition strategies in student modeling: prediction and description. Proceedings AAAI-82, pp. 335-338.

Clancey, W. J. lutoring rules for guiding a case method dialogue. International Journal of Man-Machine Studies 11:25-49 (1979).

Clancey, W. J. Dialogue management for rule-based tutorials. Proceedings IJCAI-79, pp. 155-161.

Intelligent Agent

Rosenschein, J., and M. R. Genesereth. Communication and cooperation. Memo HPP-84-5 (Knowledge Systems Laboratory), March 1984.

Finger, J. J., and M. R. Genesereth. RESIDUE: a deductive approach to design. Memo HPP-83-46 (Knowledge Systems Laboratory), December 1983.

MacKinlay, J. Intelligent presentation of information: the generation problem of user interfaces. Memo HPP-83-34 (Knowledge Systems Laboratory), March 1983.

Finger, J. J. Sensory planning. Memo HPP-82-12 (Knowledge Systems Laboratory), April 1982.

KBVLSI

Brown, H., C. Tong, and G. Foyster. PALLADIO: an exploratory environment for circuit design. Computer 16(12):41-56 (1983).

Knowledge Acquisition

Bennett, J. S. ROGET: a knowledge-based system for acquiring the conceptual structure of an expert system. Journal of Automated Reasoning 1(1):49-74 (1985)

Dietterich, T. G. Constraint propagation techniques for theory-driven data interpretation. Memo HPP 84-46 (Knowledge Systems Laboratory), December 1984.

Dietterich, T. G., and B. G. Buchanan. The role of the critic in learning systems. In O. Selfridge, E. Rissland, and M. Arbib, eds, Adaptive Control of Ill-Defined Systems. New York: Plenum, 1984. (NATO Advanced Workshop on Adaptive Control of Ill-Defined Systems; Devon, England, June 1981.) Also Memo HPP-81-19 (Knowledge Systems Laboratory), January 1981.

Buchanan, B. G., T. M. Mitchell, R. G. Smith, and C. R. Johnson, Jr. Models of learning systems. Encyclopedia of Computer Science and Technology 11 (1978).

Mitchell, T. M. Version spaces: an approach to concept learning. Memo HPP-79-2 (Knowledge Systems Laboratory), January 1979. Also Stanford CS Report STAN-CS-78-711, 1978.

Model of Endorsement

Cohen, P. Heuristic Reasoning about Uncertainty: An AI Approach. Boston: Pitman, 1985.

MOLGEN

Friedland, P., and L. Kedes. Discovering the secrets of DNA. To appear in joint issue ACM/Computer, October 1985.

Friedland, P., and Y. Iwasaki. The concept and implementation of skeletal plans. Journal of Automated Reasoning 1(2) (in press). ach, R., Y. Iwasaki, and P. Friedland. Intelligent computational assistance for experiment design. Nucleic Acids Research, January 1984.

Iwasaki, Y., and P. Friedland. SPEX: a second-generation experiment design system.Proceedings AAA1-82, pp. 341-344. Stefik, M. Planning with constraints. Memo HPP-80-2 (Knowledge Systems Laboratory), January 1980. Also Stanford CS Report STAN-CS-80-784, 1980.

Friedland, P. Knowledge-based experiment design in molecular genetics. Proceedings IJCA1-79, pp. 285-287.

MYCIN and EMYCIN

Buchanan, B. G., and E. H. Shortliffe. Rule-Based Expert Systems: The MYCIN Experiments oJ the Stanford Heuristic Programming Project. Reading, MA: Addison-Wesley, 1984. van Melle, W. System Aids in Constructing Conultation Programs: EMYCIN. Ann Arbor, MI: UMI Research Press, 1982.

MRS

Smith, D. E., and M. R. Genesereth. Controlling infinite chains of inference. Memo HPP-84-6 (Knowledge Systems Laboratory), February 1984.

Genesereth, M. R., and D. E. Smith. Partial programs. Memo HPP-841 (Knowledge Systems Laboratory), January 1984.

Genesereth, M. R. An overview of meta-level architecture. Proceedings AAA1-8, pp. 119- 124.

Genesereth, M. R., R. Greiner, and D. E. Smith. A meta-level representation system. Memo HPP-83-28 (Knowledge Systens Laboratory), May 1983.

NEOMYCIN

Clancey, W. J. Methodology for building an intelligent tutoring system. In W. Kintsch, J.R. Miller, and P.G. Polson, eds., Method and Tactics in Cognitive Science. Hillsdale, NJ: Lawrence Erlbaum Associates, 1984.

Clancey, W. J. Acquiring, representing, and evaluating a competence model of diagnosis. In M.T.H. Chi, R. Glaser, and M. Farr, eds., The Nature of Expertise, in preparation. Also Memo HPP-84-2 (Knowledge Systems Laboratory), February 1984.

Clancey, W. J. The epistemology of a rule-based expert system: a framework for explanation.

Artificial Intelligence 20(3): 215-251(1983).

Clancey, W. J. The advantages of abstract control knowledge in expert system design.

Proceedings AAA1-8, pp. 74-78.

ONCOCIN

Bischoff, M. B., E. H. Shortliffe, A. C. Scott, R. W. Carlsen, and C. D. Jacobs. Integration of a computer-based consultant into the clinical setting. Proceedings of the Seventh Annual

Symposium on Computer Applications in Medical Care, pp. 149-152 (October 1983). Tsuji, S., and E. H. Shortliffe. Graphical access to the knowledge base of a medical consultation system. Proceedings of AAMSI Congress 1983, pp. 551-555.

Langlotz, C. P., and E. H. Shortliffe. Adapting a consultation system to critique user plans. International Journal of Man-Machine Studies 19(5):479-496 (1983).

Gerring, P. E., E. H. Shortliffe, and W. van Melle. The interviewer reasoner model: an approach to improving system responsiveness in interactive AI systems. AI Magazine 3(4):24-27 (1982).

Suwa, M., A. C. Scott, and E. H. Shortliffe. An approach to verifying completeness and consistency in a rule-based expert system. AI Magazine 3(4):16-21 (1982).

Shortliffe, E. H., A. C. Scott, M. B. Bischoff, A. B. Campbell, W. van Melle, and C. D.

Jacobs. ONCOCIN: an expert system for oncology protocol management. Proceedings IJCA1-81, pp. 876-881.

PATHFINDER

Horvitz, E. J., D. E. Heckerman, B. N. Nathwani, and L. M. Fagan. Diagnostic strategies in the hypothesis-directed PATHFINDER system. First Conference on Artificial Intelligence Applications, pp. 630-636 (IEEE Computer Society, 1984).

PIXIE

Sleeman, D. H. Basic algebra revisited: a study with 14-year-olds. International Journal of Man-Machine Studies, in press. Also Memo HPP-83-9 (Knowledge Systems Laboratory), February 1983.

Sleeman, D. H. A user modelling front end subsystem. International Journal of Man- Machine Studies, in press.

Sleeman, D. H. Inferring (mal)rules from pupils' protocols. Proceedings of the 1982 European AI Conference, pp. 160-164.

Sleeman, D. H. Inferring student models for intelligent computer-aided instruction. In R. S. Michalski, J. G. Carbonell, and T. M. Mitchell, eds., Machine Learning. Palo Alto, CA: Tioga Press, 1982.

Sleeman, D. H., and J. S. Brown. Intelligent tutoring systems: an overview. In D. H. Sleeman and J. S. Brown, eds., Intelligent Tutoring Systems. New York: Academic Press, 1982.

PUFF

Aikins, J. S., J. C. Kunz, E. H. Shortliffe and R. J. Fallat. PUFF: an expert system for interpretation of pulmonary function data. Computers and Biomedical Research 16:199-208 (1983).

QUIST

King, J.J. Query optimization by semantic reasoning. Stanford CS Report STAN-CS-81-861, 1981.

RADIX

Blum, R. L. Representation of empirically derived causal relationships. Proceedings NCA1-8, pp. 268-271.

Blum, R. L. Discovery, confirmation, and incorporation of causal relationships from a large time- oriented database: the RX Project. Computers and Biomedical Research 15(2):164-187 (1982).

Blum, R. L. Discovery and representation of causal relationships from a large time-oriented database: the RX Project. In D. A. B. Lindberg and P. L. Reichertz, eds., Medical Informatics 19 (1982).

RLL

Greiner, R., and D. B. Lenat. A representation language language. Proceedings of AAA1-80, pp. 165-169.

SACON

Bennett, J. S., and R. S. Engelmore. SACON: a knowledge-based consultant for structural analysis. Proceedings IJCA1-79, pp. 47-49.

SOAR

Rosenbloom, P. S., J. E. Laird, J. McDermott, A. Newell, and E. Orciuch. Rl-SOAR: an experiment in knowledge-intensive programming in a problem-solving architecture. Proceedings of the IEEE Workshop in Principles of Knowledge-Based Systems, 1984.

Laird, J. E., P. S. Rosenbloom, and A. Newell. Towards chunking as a general learning mechanism. Proceeding AAA1-84, pp. 188-192.

TEIRESIAS

Davis, R., and D. Lenat. Knowledge-Based Systems in Artificial Intelligence: AM and TEIRESIAS. New York: McGraw-Hill, 1982.

UNITS

Smith, R., and P. Friedland. Unit package user's guide. Memo HPP-80-28 (Knowledge Systems Laboratory), December 1980.

Stefik, M. An examination of a frame-structured representation system. Proceedings IJCA1- 79, pp. 845-852.

VM

Fagan, L. M. VM: representing time-dependent relations in a medical setting. Memo HPP 831 (Knowledge Systems Laboratory), June 1980.

Osborn, J., L. M. Fagan, R. Fallat, D. McClung, and R. Mitchell. Managing the data from respiratory measurements. Medical Instrumentation 13:6 (1979).