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S. Gass (1981)
Operations Research Mathematics and Models, 25
E. Bradley (1992)
Taming chaotic circuits
K. Koning (1996)
Qualitative reasoning: Modeling and simulation with incomplete knowledgeArtificial Intelligence in Medicine, 8
H. Abelson, Michael Eisenberg, Matthew Halfant, J. Katzenelson, E. Sacks, G. Sussman, J. Wisdom, K. Yip (1989)
Intelligence in scientific computingCommun. ACM, 32
Daniel Weld (1992)
Reasoning about Model AccuracyArtif. Intell., 56
Daniel Weld, J. Kleer (1990)
Readings in qualitative reasoning about physical systems
Benjamin Kaipers (1989)
Qualitative simulation, 29
Kk, A. Weigend, N. Gershenfeld (1994)
Time Series Prediction: Forecasting the Future and Understanding the Past. Proceedings of the NATO Advanced Research Workshop on a Comparative Time Series Analysis Held in Santa Fe, New Mexico, 14-17 May 1992.Journal of the American Statistical Association, 89
G. Gouesbet, J. Maquet (1992)
Construction of phenomenological models from numerical scalar time seriesPhysica D: Nonlinear Phenomena, 58
Kenneth Forbus (1984)
Qualitative Process TheoryArtif. Intell., 24
D. Berleant, B. Kuipers (2003)
Combined Qualitative and Numerical Simulation with Q 3 ° '
H. Sorenson (1985)
Kalman filtering : theory and application
D. Dvorak, B. Kuipers (1989)
Model-Based Monitoring of Dynamic Systems
N. Gershenfeld, A. Weigand (1993)
The Future of Time Series, 3
R. Stallman, G. Sussman (1976)
Forward Reasoning and Dependency-Directed Backtracking in a System for Computer-Aided Circuit AnalysisArtif. Intell., 9
E. Bradley (1995)
AUTONOMOUS EXPLORATION AND CONTROL OF CHAOTIC SYSTEMSCybernetics and Systems, 26
D. Bobrow (1984)
Qualitative Reasoning about Physical Systems: An IntroductionArtif. Intell., 24
(1996)
FY96 Publications and Submissions
P. Hayes (1995)
The second naive physics manifesto
M. Schetzen (1974)
A theory of non-linear system identificationInternational Journal of Control, 20
P. Langley, H. Simon, Gary Bradshaw, J. Zytkow (1987)
Scientific Discovery: Computational Explorations of the Creative Processes
(1992)
Reasoningaboumt modeh accuracy. Artificial Intelligence56:255—300
L. Davidroper (1987)
Numerical recipes: The art of scientific computingBulletin of Mathematical Biology, 49
B. Kuipers (1986)
Qualitative SimulationArtif. Intell., 29
B. Kuipers (1987)
Abstraction by Time-Scale in Qualitative Simulation
Pandurang Nayak (1992)
Causal Approximations
Y. Iwasaki, H. Simon (1989)
Causality in Device BehaviorArtif. Intell., 29
(1992)
Constructionof pimenmomenological
P. Hayes (1985)
Formal Theories of the Commonsense World
D.G. Bobrow (1985)
Qualitative Reasoning about Physical Systems
Mehmet Dincbas, Pascal Hentenryck, H. Simonis, A. Aggoun, T. Graf, F. Berthier (1988)
The Constraint Logic Programming Language CHIP
D. Bobrow (1994)
Artificial Intelligence in Perspective
Brian Falkenhainer, Kenneth Forbus (1991)
Compositional Modeling: Finding the Right Model for the JobArtif. Intell., 51
J. Glynn (1989)
Numerical Recipes: The Art of Scientific ComputingComputers & Geosciences, 15
J. Crawford, A. Farquhar, B. Kuipers (1990)
QPC: A Compiler from Physical Models into Qualitative Differential Equations
L. Ljung (1987)
System Identification: Theory for the User
P. Boggs, J. Donaldson, R. Byrd, Bobby Schnabel (1989)
Algorithm 676: ODRPACK: software for weighted orthogonal distance regressionACM Trans. Math. Softw., 15
Randall Davis (1984)
Diagnostic Reasoning Based on Structure and BehaviorArtif. Intell., 24
(1987)
editor
J. Kleer (1987)
An Assumption-Based TMSArtif. Intell., 28
Herbert Goldstein (1999)
Classical mechanicsPhysics Education, 9
K. Yip (1993)
Model Simplification by Asymptotic Order of Magnitude ReasoningArtif. Intell., 80
P. Langley, H. Simon, Gary Bradshaw, J. Zytkow (1987)
Scientific discovery: compulalional explorations of the creative process
J. Amsterdam (1993)
Automated qualitative modeling of dynamic physical systems
B. Char, K. Geddes, G. Gonnet, Benton Leong, M. Monagan (1993)
Maple V Language Reference Manual
J. Kleer, B. Williams (1987)
Diagnosing Multiple FaultsArtif. Intell., 32
P. Struss (1994)
Testing Physical Systems
Kenneth Forbus (1989)
The qualitative process engine
J. Doyle (1979)
A Truth Maintenance SystemArtif. Intell., 12
B. Faltings, Peter Strauss (1993)
Recent advances in qualitative physics
P.T. Boggs, J.R. Donaldson, R.H. Byrd, R.B. Schnabel (1989)
Algorithm 676 — ODRPACK: Software for orthogonal distance regressionACM Trans. Math. Software, 15
Jacques Cohen (1990)
Constraint logic programming languagesCommun. ACM, 33
R. Kelsey, William Clinger, J. Rees (1986)
Revised3 report on the algorithmic language schemeACM SIGPLAN Notices, 21
(1988)
Readings in Cognitive Science
(1960)
A new approach to filtering amid prediction problems
David Leake (2001)
Artiicial Intelligence
K. Åström, P. Eykhoff (1971)
System identification-A surveyAutomatica, 7
K. Yip (1991)
Understanding Complex Dynamics by Visual and Symbolic ReasoningArtif. Intell., 51
O. Raiman (1986)
Order of Magnitude ReasoningArtif. Intell., 51
(1986)
ACM SIGPLAN Notices
Kenneth Forbus, J. Kleer (1993)
Building Problem Solvers
F. Morrison (1991)
The Art of Modeling Dynamic Systems
(1994)
Automatic Construction of Accurate Models of Physical Systems ; CU-CS-721-94 Computer Science Technical Reports. Paper 687
B. Williams (1991)
A Theory of Interactions: Unifying Qualitative and Quantitative Algebraic ReasoningArtif. Intell., 51
S. Addanki, R. Cremonini, J. Penberthy (1991)
Graphs of ModelsArtif. Intell., 51
J. Kleer, Alan Mackworth, R. Reiter (1990)
Characterizing Diagnoses
(1993)
The future of tin-ic series. 1mm Time SeriesPrediction: Forecasting the Future and Understandingthe Past.Saumta Fe, NM
Seshashayee Murthy (1988)
Qualitative Reasoning at Multiple Resolutions
P. Struss (1988)
Mathematical aspects of qualitative reasoningArtif. Intell. Eng., 3
B. Kuipers (1993)
Reasoning with Qualitative ModelsArtif. Intell., 59
H. Priesmeyer (1992)
The art of modeling dynamic systems: Forecasting for chaos, randomness, and determinism: F. Morrison, 1991, (Wiley-Interscience, John Wiley & Sons, Inc., New York), pp. 387, ISBN 0-471-52004-7, $54.95International Journal of Forecasting, 8
B. Kuipers (1994)
Qualitative reasoning: Modeling and simulation with incomplete knowledgeAutom., 25
J. Backus, F. Bauer, Julien Green, C. Katz, J. McCarthy, A. Perlis, H. Rutishauser, K. Samelson, B. Vauquois, J. Wegstein, A. Wijngaarden, M. Woodger, P. Naur (1963)
Revised report on the algorithm language ALGOL 60Commun. ACM, 6
S. Addanki, R. Cremonini, J. Penberthy (1989)
Reasoning About Assumptions in Graphs of Models
J. Kleer, J. Brown (1984)
A Qualitative Physics Based on ConfluencesArtif. Intell., 24
L. Sterling, E. Shapiro, Randy Garrett (1987)
The Art of PrologIEEE Expert, 2
This paper describes an implemented computer program called PRET that automates the process of system identification: given hypotheses, observations, and specifications, it constructs an ordinary differential equation model of a target system with no other inputs or intervention from its user. The core of the program is a set of traditional system identification (SID) methods. A layer of artificial intelligence (AI) techniques built around this core automates the high-level stages of the identification process that are normally performed by a human expert. The AI layer accomplishes this by selecting and applying appropriate methods from the SID library and performing qualitative, symbolic, algebraic, and geometric reasoning on the user's inputs. For each supported domain (e.g., mechanics), the program uses a few powerful encoded rules (e.g., σF=0) to combine hypotheses into models. A custom logic engine checks models against observations, using a set of encoded domain-independent mathematical rules to infer facts about both, modulo the resolution inherent in the specifications, and then searching for contradictions. The design of the next generation of this program is also described in this paper. In it, discrepancies between sets of facts will be used to guide the removal of unnecessary terms from a model. Power-series techniques will be exploited to synthesize new terms from scratch if the user's hypotheses are inadequate, and sensors and actuators will allow the tool to take aninput-output approach to modeling real physical systems.
Annals of Mathematics and Artificial Intelligence – Springer Journals
Published: Dec 8, 2005
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