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On compositional modeling
 THE KNOWLEDGE ENGINEERING REVIEW
, 2001
"... Many solutions to AI problems require the task to be represented in one of a multitude of rigorous mathematical formalisms. The construction of such mathematical models forms a difficult problem which is often left to the user of the problemsolver. This void between problemsolvers and their proble ..."
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Cited by 31 (14 self)
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Many solutions to AI problems require the task to be represented in one of a multitude of rigorous mathematical formalisms. The construction of such mathematical models forms a difficult problem which is often left to the user of the problemsolver. This void between problemsolvers and their problems is studied by the eclectic field of automated modelling. Within this field, compositional modelling, a knowledgebased methodology for systemmodelling, has established itself as a leading approach. In general, a compositional modeller organises knowledge in a structure of composable fragments that relate to particular system components or processes. Its embedded inference mechanism chooses the appropriate fragments with respect to a given problem, instantiates and assembles them into a consistent system model. Many different types of compositional modeller exist, however, with significant differences in their knowledge representation and approach to inference. This paper examines compositional modelling. It presents a general framework for building and analysing compositional modellers. Based on this framework, a number of influential compositional modellers are examined and compared. The paper also identifies the strengths and weaknesses of compositional modelling and discusses some typical applications.
A WebBased Compositional Modeling System for Sharing of Physical Knowledge
 Proceedings of the 15th International Joint Conference on Artificial Intelligence, AAAI
, 1997
"... 1 This paper describes a compositional modeling system called CDME (Collaborative Device Modeling Environment) for constructing domain theories of physical systems, composing models of devices, and simulating their behavior. We have implemented the system with the goal of encouraging sharing as wel ..."
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Cited by 31 (2 self)
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1 This paper describes a compositional modeling system called CDME (Collaborative Device Modeling Environment) for constructing domain theories of physical systems, composing models of devices, and simulating their behavior. We have implemented the system with the goal of encouraging sharing as well as the collaborative construction of knowledge bases describing physical domains. To maximize the chance of sharing and reuse of knowledge, CDME is implemented as a collection of network services on the World Wide Web. Knowledge is represented at three distinct levels: the physical, ontological, and logical. We describe the levels of representation, and how the system enables knowledge sharing at each level. 1 Introduction Compositional modeling [1] is an effective method for automatically formulating a behavior model of a complex physical system. In compositional modeling, a system is provided with a knowledge base about the physical world, including knowledge of the behavior of a varie...
Automated Model Selection for Simulation Based on Relevance Reasoning
 Artificial Intelligence
, 1997
"... 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 [7], a system is provided with a library of composable pieces of knowledge about the physical wor ..."
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Cited by 26 (5 self)
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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 [7], 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 in [21, 16]. In this paper, we p...
Compositional model repositories via dynamic constraint satisfaction with orderofmagnitude preferences
 Journal of Artificial Intelligence Research
"... The predominant knowledgebased approach to automated model construction, compositional modelling, employs a set of models of particular functional components. Its inference mechanism takes a scenario describing the constituent interacting components of a system and translates it into a useful mathe ..."
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Cited by 12 (3 self)
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The predominant knowledgebased approach to automated model construction, compositional modelling, employs a set of models of particular functional components. Its inference mechanism takes a scenario describing the constituent interacting components of a system and translates it into a useful mathematical model. This paper presents a novel compositional modelling approach aimed at building model repositories. It furthers the field in two respects. Firstly, it expands the application domain of compositional modelling to systems that can not be easily described in terms of interacting functional components, such as ecological systems. Secondly, it enables the incorporation of user preferences into the model selection process. These features are achieved by casting the compositional modelling problem as an activitybased dynamic preference constraint satisfaction problem, where the dynamic constraints describe the restrictions imposed over the composition of partial models and the preferences correspond to those of the user of the automated modeller. In addition, the preference levels are represented through the use of symbolic values that differ in orders of magnitude. 1.
Generalized physical networks for automated model building
 In Proceedings of the 16th International Joint Conference on Artificial Intelligence
, 1999
"... We present a new knowledge representation and reasoning framework for modeling nonlinear dynamical systems. The goals of this framework are to smoothly incorporate varying levels of domain knowledge and to tailor the reasoning methods and hence the search space — accordingly. Our solution explo ..."
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Cited by 8 (3 self)
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We present a new knowledge representation and reasoning framework for modeling nonlinear dynamical systems. The goals of this framework are to smoothly incorporate varying levels of domain knowledge and to tailor the reasoning methods and hence the search space — accordingly. Our solution exploits generalized physical networks (GPN), a rnetalevel representation of idealized twoterminal elements, together with a hierarchy of qualitative and quantitative analysis tools, to produce a dynamic modeling domain whose complexity naturally adapts to the amount of available information about the target system. 1
Causality Enabled Compositional Modelling of Bayesian Networks
, 2004
"... Probabilistic abduction extends conventional symbolic abductive reasoning with Bayesian inference methods. This allows for the uncertainty underlying implications to be expressed with probabilities as well as assumptions, thus complementing the symbolic approach in situations where the use of a comp ..."
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Cited by 5 (2 self)
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Probabilistic abduction extends conventional symbolic abductive reasoning with Bayesian inference methods. This allows for the uncertainty underlying implications to be expressed with probabilities as well as assumptions, thus complementing the symbolic approach in situations where the use of a complete list of assumptions underlying inferences is not practical. However, probabilistic abduction has been of little use in first principlebased applications, such as abductive diagnosis, largely because no methods are available to automate the construction of probabilistic models, such as Bayesian networks (BNs). This paper addresses this issue by proposing a compositional modelling method for BNs.
Real world applications of qualitative reasoning: Introduction to the special issue
, 1997
"... this article for more information about the field. ..."
Intelligent simulation tools for mining large scientific data sets
 NEW GENERATION COMPUTING
, 1999
"... This paper describes problems, challenges, and opportunities for intelligent simulation of physical systems. Prototype intelligent simulation tools have been constructed for interpreting massive data sets from physical fields and for designing engineering systems. We identify the characteristics of ..."
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Cited by 2 (1 self)
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This paper describes problems, challenges, and opportunities for intelligent simulation of physical systems. Prototype intelligent simulation tools have been constructed for interpreting massive data sets from physical fields and for designing engineering systems. We identify the characteristics of intelligent simulation and describe several concrete application examples. These applications, which include weather data interpretation, distributed control optimization, and spatiotemporal diffusionreaction pattern analysis, demonstrate that intelligent simulation tools are indispensable for the rapid prototyping of application programs in many challenging scientific and engineering domains.
INTERACTIVE SEMIQUALITATIVE SIMULATION FOR VIRTUAL ENVIRONMENTS
, 2001
"... 3D virtual environments are being used in an increasing array of applications ranging from training systems, to video games and social communities. The steady growth of computing power and rendering capabilities makes them even more compelling as eachday passes. These visually richworlds are inhabi ..."
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Cited by 1 (0 self)
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3D virtual environments are being used in an increasing array of applications ranging from training systems, to video games and social communities. The steady growth of computing power and rendering capabilities makes them even more compelling as eachday passes. These visually richworlds are inhabited byintelligent agents and contain physicsbased simulated artifacts ranging from a light switch to a complete aircraft. However, a whole category of applications, such as maintenance simulations and virtual construction sets, based on the ability to alter onthe#y the structure of these devices has yet to be developed. The main reason is that for performance purposes physicsbased models must be precompiled at the time the application is developed, and thus they cannot be altered at run time. We present the development of an interactive semiqualitative simulation framework to support assemblycentric virtual environments. The framework's purpose is to simulate the physicsbased behavior of complex devices and their interaction with virtual humans. In particular, we use automated model building methods to enable users and participating agents to alter the structure of a device while its behavior is kept physically consistent without suspending the simulation. For example disconnecting a pipe in a virtual hydraulic system may cause the #uid it carries to spill in the environment. The framework v consists of a simulation engine and its objectoriented modeling language. The language captures the hybrid behavior of complex devices with hierarchical finite state machines. It encodes their continuous operation modes as di#erential algebraic equation systems. It ...