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12
Automated Modeling for Answering Prediction Questions: Selecting the Time Scale and System Boundary
 IN PROCEEDINGS OF THE TWELFTH NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE
, 1994
"... The ability to answer prediction questions is crucial to reasoning about physical systems. A prediction question poses a hypothetical scenario and asks for the resulting behavior of variables of interest. Prediction questions can be answered by simulating a model of the scenario. An appropriate syst ..."
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Cited by 60 (6 self)
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The ability to answer prediction questions is crucial to reasoning about physical systems. A prediction question poses a hypothetical scenario and asks for the resulting behavior of variables of interest. Prediction questions can be answered by simulating a model of the scenario. An appropriate system boundary, which separates aspects of the scenario that must be modeled from those that can be ignored, is critical to achieving a simple yet adequate model. This paper presents an efficient algorithm for system boundary selection, it shows the important role played by the model's time scale, and it provides a separate algorithm for selecting this time scale. Both algorithms have been implemented in a compositional modeling program called tripel and evaluated in the plant physiology domain.
Automated Modeling of Complex Systems to Answer Prediction Questions
 ARTIFICIAL INTELLIGENCE
, 1995
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Reasoning with Multipl e Abstraction Models
 In Faltings, B . and Struss, P . eds . : Recent Advances in Qualitative Physics, Cambridge , Mass
, 1992
"... The problem of complexity has kept qualitative physics techniques from being applied to large realworld systems. Use of a hierarchy of abstract models is crucial for managing complexity. Several researchers have proposed ways to use an abstraction hierarchy of models to control the complexity of qu ..."
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Cited by 15 (1 self)
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The problem of complexity has kept qualitative physics techniques from being applied to large realworld systems. Use of a hierarchy of abstract models is crucial for managing complexity. Several researchers have proposed ways to use an abstraction hierarchy of models to control the complexity of qualitative simulation [Falkenhainer & Forbus 88, Kuipers 87]. All the approaches proposed require models at predefined abstraction levels. Furthermore, the precise relations between different models are not explicitly defined, which makes it difficult to relate the conclusions drawn from different models to generate one coherent description of the behavior of the system as a whole. In this paper, we describe a scheme for generating models at abstraction levels appropriate for a given problem without requiring predefined set of abstract models. We also propose means for integrating behaviors produced from different abstraction models into one coherent description. 1
A comprehensive methodology for building hybrid models of physical systems
, 1999
"... This paper describes a comprehensive and systematic framework for building mixed continuous/discrete, i.e., hybrid physical system models. Hybrid models are a natural representation for embedded systems (physical systems with digital controllers) and for complex physical systems whose behavior is si ..."
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Cited by 13 (3 self)
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This paper describes a comprehensive and systematic framework for building mixed continuous/discrete, i.e., hybrid physical system models. Hybrid models are a natural representation for embedded systems (physical systems with digital controllers) and for complex physical systems whose behavior is simplified by introducing discrete transitions to replace fast nonlinear dynamics. In this paper we focus on two classes of abstraction mechanisms, viz., time scale and parameter abstractions, discuss their impact on building hybrid models, and then derive the transition semantics required to ensure that the derived models are consistent with physical system principles. The transition semantics are incorporated into a formal model representation language, which is used to derive a computational architecture for hybrid systems based on hybrid automata. This architecture forms the basis for a variety of hybrid simulation, analysis, and verification algorithms. A complex example of a colliding rod system demonstrates the application of our modeling framework. The divergence of time and behavior analysis principles are applied to ensure that physical principles are not violated in the definition of the discrete transition model. The overall goal is to use this framework as a basis for developing systematic compositional modeling and analysis schemes for hybrid modeling of
HigherOrder Derivative Constraints in Qualitative Simulation
 Artificial Intelligence
, 1991
"... Qualitative simulation is a useful method for predicting the possible qualitatively distinct behaviors of an incompletely known mechanism described by a system of qualitative differential equations (QDEs). Under some circumstances, sparse information about the derivatives of variables can lead to in ..."
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Cited by 7 (3 self)
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Qualitative simulation is a useful method for predicting the possible qualitatively distinct behaviors of an incompletely known mechanism described by a system of qualitative differential equations (QDEs). Under some circumstances, sparse information about the derivatives of variables can lead to intractable branching (or "chatter") representing uninteresting or even spurious distinctions among qualitative behaviors. The problem of chatter stands in the way of real applications such as qualitative simulation of models in the design or diagnosis of engineered systems. One solution to this problem is to exploit information about higherorder derivatives of the variables. We demonstrate automatic methods for identification of chattering variables, algebraic derivation of expressions for secondorder derivatives, and evaluation and application of the sign of second and thirdorder derivatives of variables, resulting in tractable simulation of important qualitative models. Caution is requir...
The Thought Experiment Approach to Qualitative Physics
 Proc. IJCAI89
, 1989
"... This paper discusses the application of the thought experiment methodology to qualitative reasoning. Problem solving using this technique involves simplification of the original problem, solution of the simplified problem, and generalization of the results obtained. Our emphasis in this work is to d ..."
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Cited by 2 (2 self)
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This paper discusses the application of the thought experiment methodology to qualitative reasoning. Problem solving using this technique involves simplification of the original problem, solution of the simplified problem, and generalization of the results obtained. Our emphasis in this work is to demonstrate the effectiveness of this approach in addressing complexity and grain size issues that affect qualitative simulation. The thought experiment methodology is presented formally, the implementation of a problem solver called TEPS is briefly discussed, and the methodology is compared with related techniques such as approximation, aggregation, and exaggeration. 1
Qualitative Analysis of Causal Graphs With Equilibrium TypeTransition
 IN THE PROC. OF IJCAI'97 (NAGOYA
, 1997
"... In this paper, we present a method to qualitatively compute the global characteristics of causal graphs by the analysis of the underlying dynamical systems, rather than traditional qualitative simulations which suffer from intractability and difficulty in understanding their simulation results ..."
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Cited by 2 (2 self)
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In this paper, we present a method to qualitatively compute the global characteristics of causal graphs by the analysis of the underlying dynamical systems, rather than traditional qualitative simulations which suffer from intractability and difficulty in understanding their simulation results. The key idea is to translate a given causal graph into an autonomous dynamical system and to analyze equilibrium points in the system. The method requires no numerical information and it has the advantage of computing the conditions under which a certain equilibrium type holds and when equilibrium typetransitions occur. The
TEPS: The Thought Experiment Approach to Qualitative Physics Problem solving, to appear in
 Recent Advances in Qualitative Physics, B. Faltings and
, 1992
"... This paper discusses the application of the thought experiment methodology to qualitative reasoning. Problem solving using this technique involves simplification of the original problem, solution of the simplifie d problem, and generalization of the results obtained. Our emphasis in this work is to ..."
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Cited by 1 (1 self)
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This paper discusses the application of the thought experiment methodology to qualitative reasoning. Problem solving using this technique involves simplification of the original problem, solution of the simplifie d problem, and generalization of the results obtained. Our emphasis in this work is to demonstrate th e effectiveness of this approach in addressing complexity and grain size issues that affect qualitative simulation. The thought experiment methodology is presented formally, the implementation of a problem solve r called TEPS is briefly discussed, and the methodology is compared with related techniques such as approximation, aggregation, and exaggeration. 1
On the Relationship between Model Abstraction and Causality: Variance of Causal Ordering under Abstraction Operations
 Stanford University
, 1990
"... ion and Causality: Variance of Causal Ordering under Abstraction Operations Yumi Iwasaki Knowledge Systems Laboratory Stanford University 701 Welch Rd., Palo Alto, CA 94304, USA Appeared in the Proceedings of the Pacific Rim International Conference on Artificial Intelligence, 1990. Abstract ..."
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ion and Causality: Variance of Causal Ordering under Abstraction Operations Yumi Iwasaki Knowledge Systems Laboratory Stanford University 701 Welch Rd., Palo Alto, CA 94304, USA Appeared in the Proceedings of the Pacific Rim International Conference on Artificial Intelligence, 1990. Abstract This paper examines the relationship between causal ordering and two temporal abstraction techniques, namely generation of mixed models from dynamic models and aggregation of nearly decomposable models. The paper defines two notions of causal equivalence between an original model and its abstraction or its elaboration. Then, it shows that when the constraint for a uniform temporal grain size for a model is enforced, both elaborating and abstracting a model can significantly alter the causal ordering. 2 Table of Contents 1 Introduction .....................................................................................................................1 2 Model Abstraction Techniques ......