# zbMATH — the first resource for mathematics

Automated model selection for simulation based on relevance reasoning. (English) Zbl 0903.68215
Summary: Constructing an appropriate model is a crucial step in performing the reasoning required to successfully answer a query about the behavior of a physical situation. In the compositional modeling approach of Falkenhainer and Forbus, a system is provided with a library of composable pieces of knowledge about the physical world called model fragments. The model construction problem involves selecting appropriate model fragments to describe the situation. Model construction can be considered either for static analysis of a single state or for simulation of dynamic behavior over a sequence of states. The latter is significantly more difficult than the former since one must select model fragments without knowing exactly what will happen in the future states. The model construction problem in general can advantageously be formulated as a problem of reasoning about relevance of knowledge that is available to the system using a general framework for reasoning about relevance described by Levy (1993) and Levy and Sagiv (1993). In this paper, we present a model formulation procedure based on that framework for selecting model fragments efficiently for the case of simulation. For such an algorithm to be useful, the generated model must be adequate for answering the given query and, at the same time, as simple as possible. We define formally the concepts of adequacy and simplicity and show that the algorithm in fact generates an adequate and simplest model.

##### MSC:
 68U20 Simulation (MSC2010)
Full Text:
##### References:
 [1] Addanki, S.; Cremonini, R.; Penberthy, J., Reasoning about assumptions in graphs of models, (), 443-446 · Zbl 0719.68066 [2] Bobrow, D.; Falkenhainer, B.; Farquhar, A.; Fikes, R.; Forbus, K.; Gruber, T.; Iwasaki, Y.; Kuipers, B., A compositional modeling language, (), 12-21 [3] also: AAAI Tech. Rept. WS-96-01. [4] Bobrow, D.; Falkenhainer, B.; Farquhar, A.; Fikes, R.; Forbus, K.; Gruber, T.; Iwasaki, Y.; Kuipers, B., Cml: a compositional modeling language, () [5] Carnap, R., () [6] Crawford, J.; Farquhar, A.; Kuipers, B., A compiler from physical models into qualitative differential equations, (), 365-372 [7] Denn, M.M., () [8] Falkenhainer, B.; Forbus, K.D., Compositional modeling: finding the right model for the job, Artificial intelligence, 51, 95-143, (1991) [9] Forbus, K.D., Qualitative process theory, Artificial intelligence, 24, 178-219, (1984) [10] Gärdenfors, P., On the logic of relevance, Synthese, 37, 351-367, (1978) · Zbl 0395.03005 [11] Genesereth, M.R.; Fikes, R.E., Knowledge interchange format, version 3.0 reference manual, () [12] Iwasaki, Y.; Low, C.M., Model generation and simulation of device behavior with continuous and discrete change, Intelligent systems engineering, 1, 2, 115-145, (1993) [13] Iwasaki, Y.; Simon, H.A., Causality in device behavior, Artificial intelligence, 29, 3-32, (1986) [14] Iwasaki, Y.; Farquhar, A.; Fikes, R.; Rice, J., A web-based compositional modeling system for sharing of physical knowledge, () [15] Iwasaki, Y.; Low, C.M., Device modeling environment: an integrated model-formulation and simulation environment for continuous and discrete phenomena, (), 141-146 [16] Keynes, J.M., () [17] Levy, A.Y., Irrelevance reasoning in knowledge base systems, () [18] Levy, A.Y., Creating abstractions using relevance reasoning, (), 588-594 [19] Levy, A.Y.; Fikes, R.E.; Sagiv, S., Speeding up inferences using relevance reasoning: a formalism and algorithms, Artificial intelligence, 97, 83-136, (1997) · Zbl 0904.68163 [20] Levy, A.Y.; Rajaraman, A.; Ordille, J.J., Query answering algorithms for information agents, (), 40-47 [21] Levy, A.Y.; Sagiv, Y., Constraints and redundancy in Datalog, (), 67-80 [22] Levy, A.Y.; Sagiv, Y., Exploiting irrelevance reasoning to guide problem solving, (), 138-144 [23] Macaulay, D., () [24] Nayak, P.P., Automated model selection, () [25] Nayak, P.P., Causal approximations, Artificial intelligence, 70, 277-334, (1994) · Zbl 0938.68845 [26] Nayak, P.P.; Joskowicz, L., Efficient compositional modeling for generating causal explanations, Artificial intelligence, 83, 193-227, (1996) [27] Rickel, J.; Porter, B., Automated modeling for answering prediction questions: exploiting interaction paths, (), 82-95 [28] Rickel, J.; Porter, B., Automated modeling for answering prediction questions: selecting the time scale and system boundary, (), 1191-1198 [29] Rickel, J.; Porter, B., Automated modeling of complex systems for answering prediction questions, Artificial intelligence, 93, 201-260, (1997) · Zbl 1017.93500 [30] Simon, H.A.; Rescher, N., Cause and counterfactual, Philos. sci., 33, 323-340, (1966) [31] Subramanian, D.; Genesereth, M.R., The relevance of irrelevance, (), 416-422 [32] Subramanian, D., A theory of justified reformulations, () · Zbl 0792.68169 [33] Weld, D.S., Approximation reformulation, (), 407-412 [34] Williams, B.C., Capturing how things work: constructing critical abstractions of local interactions, (), 163-174 [35] Williams, B.C., Interaction-based invention: designing novel devices from first principles, (), 349-356
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.