zbMATH — the first resource for mathematics

Automated modeling of complex systems to answer prediction questions. (English) Zbl 1017.93500
Summary: A question about the behavior of a complex, physical system can be answered by simulating the system – the challenge is building a model of the system that is appropriate for answering the question. If the model omits relevant aspects of the system, the predicted behavior may be wrong. If, on the other hand, the model includes many aspects that are irrelevant to the question, it may be difficult to simulate and explain. The leading approach to automated modeling, “compositional modeling”, constructs a simplest adequate model for a question from building blocks (“model fragments”) that are designed by knowledge engineers. This paper presents a new compositional modeling algorithm that constructs models from simpler building blocks – the individual influences among system variables – and addresses important modeling issues that previous programs left to the knowledge engineer. In the most rigorous test of a modeling algorithm to date, we implemented our algorithm, applied it to a large knowledge base for plant physiology, and asked a domain expert to evaluate the models it produced.

93A30 Mathematical modelling of systems (MSC2010)
68T35 Theory of languages and software systems (knowledge-based systems, expert systems, etc.) for artificial intelligence
PDF BibTeX Cite
Full Text: DOI
[1] Addanki, S.; Cremonini, R.; Penberthy, J.S., Graphs of models, Artificial intelligence, 51, 145-177, (1991)
[2] Allen, T.F.H.; Starr, T.B., ()
[3] Amsterdam, J., Automated qualitative modeling of dynamic physical systems, ()
[4] Catino, C.A., Automated modeling of chemical plants with application to hazard and operability studies, ()
[5] Clancy, D.J.; Kuipers, B.J., Behavior abstraction for tractable simulation, (), 57-64
[6] Collins, J.W.; Forbus, K.D., Reasoning about fluids via molecular collections, (), 590-595
[7] Cormen, T.H.; Leiserson, C.E.; Rivest, R.L., ()
[8] Davis, R., Diagnostic reasoning based on structure and behavior, Artificial intelligence, 24, 347-410, (1984)
[9] Ellman, T.; Keane, J.; Schwabacher, M., Intelligent model selection for hillclimbing search in computer-aided design, (), 594-599
[10] Falkenhainer, B., Modeling without amnesia: making experience-sanctioned approximations, (), 44-55
[11] Falkenhainer, B., Ideal physical systems, (), 600-605
[12] Falkenhainer, B.; Forbus, K.D., Compositional modeling: finding the right model for the job, Artificial intelligence, 51, 95-143, (1991)
[13] Farquhar, A., Automated modeling of physical systems in the presence of incomplete knowledge, ()
[14] Farquhar, A., A qualitative physics compiler, (), 1168-1174
[15] Forbus, K.D., Qualitative process theory, Artificial intelligence, 24, 85-168, (1984)
[16] Forbus, K.D., Qualitative physics: past, present and future, (), 11-39
[17] Forbus, K.D.; Falkenhainer, B., Self-explanatory simulations: an integration of qualitative and quantitative knowledge, (), 380-387
[18] Forrester, J.W., ()
[19] Gold, H.J., ()
[20] Guyton, A.C., ()
[21] Iwasaki, Y., Reasoning with multiple abstraction models, (), 67-82
[22] Iwasaki, Y.; Bhandari, I., Formal basis for commonsense abstraction of dynamic systems, (), 307-312
[23] Iwasaki, Y.; Levy, A.Y., Automated model selection for simulation, (), 1183-1190
[24] Iwasaki, Y.; Simon, H.A., Causality and model abstraction, Artificial intelligence, 67, 1, 143-194, (1994) · Zbl 0942.68711
[25] Kline, S.J., ()
[26] Kokotovic, P.V.; O’Malley, R.E.; Sannuti, P., Singular perturbations and order reduction in control theory—an overview, Automatica, 12, 123-132, (1976) · Zbl 0323.93020
[27] Kuipers, B.J., Qualitative simulation, Artificial intelligence, 29, 289-338, (1986) · Zbl 0624.68098
[28] Kuipers, B.J., Abstraction by time scale in qualitative simulation, (), 621-625
[29] Kuipers, B.J., Qualitative reasoning: modeling and simulation with incomplete knowledge, (1994), MIT Press Cambridge, MA
[30] Lapp, S.A.; Powers, G.J., Computer-aided synthesis of fault trees, IEEE trans. reliability, (April 1977)
[31] Lester, J., Generating natural language explanations from large-scale knowledge bases, ()
[32] J. Lester and B. Porter, Developing and empirically evaluating robust explanation generators: the knight experiments, Computational Linguistics, to appear.
[33] Lester, J.; Porter, B., Scaling up explanation generation: large-scale knowledge bases and empirical studies, (), 416-423
[34] Levy, A.Y., Irrelevance reasoning in knowledge based systems, ()
[35] Lin, C.C.; Segal, L.A., Mathematics applied to deterministic problems in the natural sciences, (1974), Macmillan New York · Zbl 0286.00003
[36] Ling, S.-k.R., Using a domain theory to guide automated modeling of complex physical phenomena, (), 1766-1772 · Zbl 0233.60042
[37] Mittal, S.; Falkenhainer, B., Dynamic constraint satisfaction problems, (), 25-32
[38] Nayak, P.P., Causal approximations, Artificial intelligence, 70, 277-334, (1994) · Zbl 0938.68845
[39] Nayak, P.P.; Joskowicz, L., Efficient compositional modeling for generating causal explanations, Artificial intelligence, 83, 193-227, (1996)
[40] O’Neill, R.V.; DeAngelis, D.L.; Waide, J.B.; Allen, T.F.H., ()
[41] Pearl, J., Heuristics: intelligent search strategies for computer problem solving, (1984), Addison-Wesley Reading, MA
[42] Porter, B.; Lester, J.; Murray, K.; Pittman, K.; Souther, A.; Acker, L.; Jones, T., AI research in the context of a multifunctional knowledge base: the botany knowledge base project, ()
[43] Puccia, C.J.; Levins, R., ()
[44] Rickel, J., Automated modeling of complex systems to answer prediction questions, () · Zbl 1017.93500
[45] Rickel, J.; Porter, B., Automated modeling for answering prediction questions: selecting the time scale and system boundary, (), 1191-1198
[46] Roberts, N.; Andersen, D.; Deal, R.; Garet, M.; Shaffer, W., ()
[47] ()
[48] Saksena, V.R.; O’Reilly, J.; Kokotovic, P.V., Singular perturbations and time-scale methods in control theory: survey 1976-1983, Automatica, 20, 3, 273-293, (1984) · Zbl 0532.93002
[49] (), Chapter 3
[50] Shirley, M.; Falkenhainer, B., Explicit reasoning about accuracy for approximating physical systems, (), 153-162
[51] Simon, H.A.; Ando, A., Aggregation of variables in dynamic systems, Econometrica, 29, 111-138, (1961) · Zbl 0121.15103
[52] Weld, D.S., Reasoning about model accuracy, Artificial intelligence, 56, 255-300, (1992) · Zbl 0787.68090
[53] ()
[54] Williams, B.C., Critical abstraction: generating simplest models for causal explanation, (), 77-92
[55] Williams, B.C.; Raiman, O., Decompositional modeling through caricatural reasoning, (), 1199-1204
[56] Yip, K.M.-k., Model simplification by asymptotic order of magnitude reasoning, (), 634-641
[57] Yip, K.M.-k., Model simplification by asymptotic order of magnitude reasoning, Artificial intelligence, 80, 309-348, (1996)
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.