The Experts below are selected from a list of 533649 Experts worldwide ranked by ideXlab platform
Daniel R. Dolk - One of the best experts on this subject based on the ideXlab platform.
-
Model Integration and a theory of Models
Decision Support Systems, 1993Co-Authors: Daniel R. Dolk, Jeffrey E. KottemannAbstract:Model Integration extends the scope of Model management to include the dimension of manipulation as well. This invariably leads to comparisons with database theory. Model Integration is viewed from four perspectives: Organizational, definitional, procedural, and implementational. Strategic Modeling is discussed as the organizational motivation for Model Integration. Schema and process Integration are examined as the logical and manipulation counterparts of Model Integration corresponding to data definition and manipulation, respectively. A Model manipulation language based on structured Modeling and communicating structured Models is suggested which incorporates schema and process Integration. The use of object-oriented concepts for designing and implementing integrated Modeling environments is discussed. Model Integration is projected as the springboard for building a theory of Models equivalent in power to relational theory in the database community.
-
An introduction to Model Integration and integrated Modeling environments
Decision Support Systems, 1993Co-Authors: Daniel R. DolkAbstract:Abstract Integrated Modeling systems provide support for the definition, manipulation, and control of mathematical Models throughout the entire Modeling life cycle. Model Integration is a particularly crucial operation which requires thinking about “Modeling in the large”, and which extends the scope of Model management research to include manipulation as well as definition. Several aspects of Model Integration are identified and briefly described with respect to the problems they raise for constructing integrated Modeling environments. Relevant work in these areas is cited. A brief introduction to each of the papers in this special issue is provided within the context established.
-
Model Integration and Modeling Languages: A Process Perspective
Information Systems Research, 1992Co-Authors: Jeffrey E. Kottemann, Daniel R. DolkAbstract:Development of large-scale Models often involves-or, certainly could benefit from-linking existing Models. This process is termed Model Integration and involves two related aspects: 1 the coupling of Model representations, and 2 the coupling of the processes for evaluating, or executing, instances of these representations. Given this distinction, we overview Model Integration capabilities in existing executable Modeling languages, discuss current theoretical approaches to Model Integration, and identify the limiting assumptions implicitly made in both cases. In particular, current approaches assume away issues of dynamic variable correspondence and synchronization in composite Model execution. We then propose a process-oriented conceptualization and associated constructs that overcome these limiting assumptions. The constructs allow Model components to be used as building blocks for more elaborate composite Models in ways unforeseen when the components were originally developed. While we do not prove the sufficiency of the constructs over the set of all Model types and Integration configurations, we present several examples of Model Integration from various domains to demonstrate the utility of the approach.
-
Process-oriented Model Integration
[1988] Proceedings of the Twenty-First Annual Hawaii International Conference on System Sciences. Volume III: Decision Support and Knowledge Based Sys, 1Co-Authors: Jeffrey E. Kottemann, Daniel R. DolkAbstract:The authors outline a process-oriented approach to Model Integration based on familiar notions from discrete-event simulation and communicating sequential processes. They introduce features of a Model Integration command language (MICL) that incorporates message-passing, demons, suspend/resume mechanisms, and structured programming constructs to represent a Model Integration schema, or procedure. They present several examples of Model Integration from various domains or demonstrate how the MICL functions. The primary advantage of the MICL is that it allows Model components to be used as building blocks for more elaborate composite Models without necessitating modifications to the components themselves. Incorporating the MICL as part of a Model-management system's Model manipulation language provides a mechanism for multiparadigmatic Integration as well as for assimilating behaviorally complex components, such as those found in discrete event simulation, into the Model management framework. >
Pedro Zuidberg Dos Martires - One of the best experts on this subject based on the ideXlab platform.
-
monte carlo anti differentiation for approximate weighted Model Integration
arXiv: Artificial Intelligence, 2020Co-Authors: Pedro Zuidberg Dos Martires, Samuel KolbAbstract:Probabilistic inference in the hybrid domain, i.e. inference over discrete-continuous domains, requires tackling two well known #P-hard problems 1)~weighted Model counting (WMC) over discrete variables and 2)~Integration over continuous variables. For both of these problems inference techniques have been developed separately in order to manage their #P-hardness, such as knowledge compilation for WMC and Monte Carlo (MC) methods for (approximate) Integration in the continuous domain. Weighted Model Integration (WMI), the extension of WMC to the hybrid domain, has been proposed as a formalism to study probabilistic inference over discrete and continuous variables alike. Recently developed WMI solvers have focused on exploiting structure in WMI problems, for which they rely on symbolic Integration to find the primitive of an integrand, i.e. to perform anti-differentiation. To combine these advances with state-of-the-art Monte Carlo Integration techniques, we introduce \textit{Monte Carlo anti-differentiation} (MCAD), which computes MC approximations of anti-derivatives. In our empirical evaluation we substitute the exact symbolic Integration backend in an existing WMI solver with an MCAD backend. Our experiments show that that equipping existing WMI solvers with MCAD yields a fast yet reliable approximate inference scheme.
-
exact and approximate weighted Model Integration with probability density functions using knowledge compilation
National Conference on Artificial Intelligence, 2019Co-Authors: Pedro Zuidberg Dos Martires, Anton Dries, Luc De RaedtAbstract:Weighted Model counting has recently been extended to weighted Model Integration, which can be used to solve hybrid probabilistic reasoning problems. Such problems involve both discrete and continuous probability distributions. We show how standard knowledge compilation techniques (to SDDs and d-DNNFs) apply to weighted Model Integration, and use it in two novel solvers, one exact and one approximate solver. Furthermore, we extend the class of employable weight functions to actual probability density functions instead of mere polynomial weight functions.
-
AAAI - Exact and Approximate Weighted Model Integration with Probability Density Functions Using Knowledge Compilation
Proceedings of the AAAI Conference on Artificial Intelligence, 2019Co-Authors: Pedro Zuidberg Dos Martires, Anton Dries, Luc De RaedtAbstract:Weighted Model counting has recently been extended to weighted Model Integration, which can be used to solve hybrid probabilistic reasoning problems. Such problems involve both discrete and continuous probability distributions. We show how standard knowledge compilation techniques (to SDDs and d-DNNFs) apply to weighted Model Integration, and use it in two novel solvers, one exact and one approximate solver. Furthermore, we extend the class of employable weight functions to actual probability density functions instead of mere polynomial weight functions.
Jeffrey E. Kottemann - One of the best experts on this subject based on the ideXlab platform.
-
Model Integration and a theory of Models
Decision Support Systems, 1993Co-Authors: Daniel R. Dolk, Jeffrey E. KottemannAbstract:Model Integration extends the scope of Model management to include the dimension of manipulation as well. This invariably leads to comparisons with database theory. Model Integration is viewed from four perspectives: Organizational, definitional, procedural, and implementational. Strategic Modeling is discussed as the organizational motivation for Model Integration. Schema and process Integration are examined as the logical and manipulation counterparts of Model Integration corresponding to data definition and manipulation, respectively. A Model manipulation language based on structured Modeling and communicating structured Models is suggested which incorporates schema and process Integration. The use of object-oriented concepts for designing and implementing integrated Modeling environments is discussed. Model Integration is projected as the springboard for building a theory of Models equivalent in power to relational theory in the database community.
-
Model Integration and Modeling Languages: A Process Perspective
Information Systems Research, 1992Co-Authors: Jeffrey E. Kottemann, Daniel R. DolkAbstract:Development of large-scale Models often involves-or, certainly could benefit from-linking existing Models. This process is termed Model Integration and involves two related aspects: 1 the coupling of Model representations, and 2 the coupling of the processes for evaluating, or executing, instances of these representations. Given this distinction, we overview Model Integration capabilities in existing executable Modeling languages, discuss current theoretical approaches to Model Integration, and identify the limiting assumptions implicitly made in both cases. In particular, current approaches assume away issues of dynamic variable correspondence and synchronization in composite Model execution. We then propose a process-oriented conceptualization and associated constructs that overcome these limiting assumptions. The constructs allow Model components to be used as building blocks for more elaborate composite Models in ways unforeseen when the components were originally developed. While we do not prove the sufficiency of the constructs over the set of all Model types and Integration configurations, we present several examples of Model Integration from various domains to demonstrate the utility of the approach.
-
Process-oriented Model Integration
[1988] Proceedings of the Twenty-First Annual Hawaii International Conference on System Sciences. Volume III: Decision Support and Knowledge Based Sys, 1Co-Authors: Jeffrey E. Kottemann, Daniel R. DolkAbstract:The authors outline a process-oriented approach to Model Integration based on familiar notions from discrete-event simulation and communicating sequential processes. They introduce features of a Model Integration command language (MICL) that incorporates message-passing, demons, suspend/resume mechanisms, and structured programming constructs to represent a Model Integration schema, or procedure. They present several examples of Model Integration from various domains or demonstrate how the MICL functions. The primary advantage of the MICL is that it allows Model components to be used as building blocks for more elaborate composite Models without necessitating modifications to the components themselves. Incorporating the MICL as part of a Model-management system's Model manipulation language provides a mechanism for multiparadigmatic Integration as well as for assimilating behaviorally complex components, such as those found in discrete event simulation, into the Model management framework. >
Luc De Raedt - One of the best experts on this subject based on the ideXlab platform.
-
exact and approximate weighted Model Integration with probability density functions using knowledge compilation
National Conference on Artificial Intelligence, 2019Co-Authors: Pedro Zuidberg Dos Martires, Anton Dries, Luc De RaedtAbstract:Weighted Model counting has recently been extended to weighted Model Integration, which can be used to solve hybrid probabilistic reasoning problems. Such problems involve both discrete and continuous probability distributions. We show how standard knowledge compilation techniques (to SDDs and d-DNNFs) apply to weighted Model Integration, and use it in two novel solvers, one exact and one approximate solver. Furthermore, we extend the class of employable weight functions to actual probability density functions instead of mere polynomial weight functions.
-
AAAI - Exact and Approximate Weighted Model Integration with Probability Density Functions Using Knowledge Compilation
Proceedings of the AAAI Conference on Artificial Intelligence, 2019Co-Authors: Pedro Zuidberg Dos Martires, Anton Dries, Luc De RaedtAbstract:Weighted Model counting has recently been extended to weighted Model Integration, which can be used to solve hybrid probabilistic reasoning problems. Such problems involve both discrete and continuous probability distributions. We show how standard knowledge compilation techniques (to SDDs and d-DNNFs) apply to weighted Model Integration, and use it in two novel solvers, one exact and one approximate solver. Furthermore, we extend the class of employable weight functions to actual probability density functions instead of mere polynomial weight functions.
Andrea Passerini - One of the best experts on this subject based on the ideXlab platform.
-
Learning Weighted Model Integration Distributions
Proceedings of the AAAI Conference on Artificial Intelligence, 2020Co-Authors: Paolo Morettin, Samuel Kolb, Stefano Teso, Andrea PasseriniAbstract:Weighted Model Integration (WMI) is a framework for probabilistic inference over distributions with discrete and continuous variables and structured supports. Despite the growing popularity of WMI, existing density estimators ignore the problem of learning a structured support, and thus fail to handle unfeasible configurations and piecewise-linear relations between continuous variables. We propose lariat, a novel method to tackle this challenging problem. In a first step, our approach induces an SMT(ℒℛA) formula representing the support of the structured distribution. Next, it combines the latter with a density learned using a state-of-the-art estimation method. The overall Model automatically accounts for the discontinuous nature of the underlying structured distribution. Our experimental results with synthetic and real-world data highlight the promise of the approach.
-
Advanced SMT techniques for weighted Model Integration
Artificial Intelligence, 2019Co-Authors: Paolo Morettin, Andrea Passerini, Roberto SebastianiAbstract:Abstract Weighted Model Integration (WMI) is a recent formalism generalizing weighted Model counting (WMC) to run probabilistic inference over hybrid domains, characterized by both discrete and continuous variables and relationships between them. WMI is computationally very demanding as it requires to explicitly enumerate all possible truth assignments to be integrated over. Component caching strategies which proved extremely effective for WMC are difficult to apply in this formalism because of the tight coupling induced by the arithmetic constraints. In this paper we present a novel formulation of WMI, which allows to exploit the power of SMT-based predicate abstraction techniques in designing efficient inference procedures. A novel algorithm combines a strong reduction in the number of Models to be integrated over with their efficient enumeration. Experimental results on synthetic and real-world data show drastic computational improvements over the original WMI formulation as well as existing alternatives for hybrid inference.
-
IJCAI - Probabilistic inference in hybrid domains by weighted Model Integration
2015Co-Authors: Vaishak Belle, Andrea Passerini, Guy Van Den BroeckAbstract:Weighted Model counting (WMC) on a propositional knowledge base is an effective and general approach to probabilistic inference in a variety of formalisms, including Bayesian and Markov Networks. However, an inherent limitation of WMC is that it only admits the inference of discrete probability distributions. In this paper, we introduce a strict generalization of WMC called weighted Model Integration that is based on annotating Boolean and arithmetic constraints, and combinations thereof. This methodology is shown to capture discrete, continuous and hybrid Markov networks. We then consider the task of parameter learning for a fragment of the language. An empirical evaluation demonstrates the applicability and promise of the proposal.
