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Reasoning About Sensor Data for Automated System Identification
 In Advances in Intelligent Data
, 1998
"... The computer program pret automatically constructs mathematical models of physical systems. A critical part of this task is automating the processing of sensor data. pret's intelligent data analyzer uses geometric reasoning to infer qualitative information from quantitative data; if critical va ..."
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Cited by 12 (7 self)
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The computer program pret automatically constructs mathematical models of physical systems. A critical part of this task is automating the processing of sensor data. pret's intelligent data analyzer uses geometric reasoning to infer qualitative information from quantitative data; if critical variables are either unknown or cannot be measured, it uses delaycoordinate embedding to reconstruct the internal dynamics from the external sensor measurements. Successful modeling results for two sensorequipped systems, a driven pendulum and a radiocontrolled car, demonstrate the effectiveness of these techniques.
Multimodal Reasoning for Automatic Model Construction
 In Proceedings of AAAI98
, 1998
"... This paper describes a program called Pret that automates system identication, the process of nding a dynamical model of a blackbox system. Pret performs both structural identication and parameter estimation by integrating several reasoning modes: qualitative reasoning, qualitative simulation, ..."
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Cited by 4 (2 self)
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This paper describes a program called Pret that automates system identication, the process of nding a dynamical model of a blackbox system. Pret performs both structural identication and parameter estimation by integrating several reasoning modes: qualitative reasoning, qualitative simulation, numerical simulation, geometric reasoning, constraint reasoning, resolution, reasoning with abstraction levels, declarative metalevel control, and a simple form of truth maintenance. Unlike other modeling programs that map structural or functional descriptions to model fragments, Pret combines hypotheses about the mathematics involved into candidate models that are intelligently tested against observations about the target system. We give two examples of system identication tasks that this automated modeling tool has successfully performed. The rst, a simple linear system, was chosen because it facilitates a brief and clear presentation of Pret's features and reasoning t...
Arguing About Radioisotope Dating
, 2007
"... We present a prototype of the AICronus system, an argumentation system that automates a challenging reasoning process used by experts in cosmogenic isotope dating. The architecture of the system is described and preliminary results are discussed. ..."
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We present a prototype of the AICronus system, an argumentation system that automates a challenging reasoning process used by experts in cosmogenic isotope dating. The architecture of the system is described and preliminary results are discussed.
Reasoning about inputoutput modeling of dynamical systems
 In Proceedings of the Third International Symposium on Intelligent Data Analysis (IDA99
, 1999
"... Abstract. The goal of inputoutput modeling is to apply a test input to a system, analyze the results, and learn something useful from the causeeffect pair. Any automated modeling tool that takes this approach must be able to reason effectively about sensors and actuators and their interactions with ..."
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Abstract. The goal of inputoutput modeling is to apply a test input to a system, analyze the results, and learn something useful from the causeeffect pair. Any automated modeling tool that takes this approach must be able to reason effectively about sensors and actuators and their interactions with the target system. Distilling qualitative information from sensor data is fairly easy, but a variety of difficult controltheoretic issues — controllability, reachability, and utility — arise during the planning and execution of experiments. This paper describes some representations and reasoning tactics, collectively termed qualitative bifurcation analysis, that make it possible to automate this task. 1 InputOutput Modeling System identification (SID) is the process of inferring an internal ordinary differential equation (ODE) model from external observations of a system. The computer program pret[5] automates the SID process, using a combination of artificial intelligence and system identification techniques to construct ODE models of lumpedparameter continuoustime nonlinear dynamic systems. As dimodeling specification ODE model domain math PRET data excitation sensors actuators target system Fig. 1. pret uses sensors and actuators to interact with target systems in an inputoutput approach to dynamical system modeling. agrammed in Fig. 1, pret uses domain knowledge to combine model fragments into ODEs, then employs actuators and sensors to learn more about the target system, and finally tests the ODEs against the actuator/sensor data using a body of mathematical knowledge encoded in firstorder logic[20].
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"... We present a prototype of AICronus, an argumentation system that automates a challenging reasoning process used by experts in cosmogenic isotope dating. The architecture of the system is described and preliminary results are discussed. 1. ..."
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We present a prototype of AICronus, an argumentation system that automates a challenging reasoning process used by experts in cosmogenic isotope dating. The architecture of the system is described and preliminary results are discussed. 1.
Artificial Intelligence 133 (2001) 139–188 Reasoning about nonlinear system identification
, 2001
"... System identification is the process of deducing a mathematical model of the internal dynamics of a system from observations of its outputs. The computer program PRET automates this process by building a layer of artificial intelligence (AI) techniques around a set of traditional formal engineering ..."
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System identification is the process of deducing a mathematical model of the internal dynamics of a system from observations of its outputs. The computer program PRET automates this process by building a layer of artificial intelligence (AI) techniques around a set of traditional formal engineering methods. PRET takes a generateandtest approach, using a small, powerful metadomain theory that tailors the space of candidate models to the problem at hand. It then tests these models against the known behavior of the target system using a large set of moregeneral mathematical rules. The complex interplay of heterogeneous reasoning modes that is involved in this process is orchestrated by a special firstorder logic system that uses static abstraction levels, dynamic declarative meta control, and a simple form of truth maintenance in order to test models quickly and cheaply. Unlike other modeling tools—most of which use libraries to model small, wellposed problems in limited domains and rely on their users to supply detailed descriptions of the target system—PRET works with nonlinear systems in multiple domains and interacts directly with the real world via sensors and actuators. This approach has met with success in a variety of simulated and real applications, ranging