Constraint Reasoning for Differential Models ( Frontiers in Artificial Intelligence and Applications )

Publication series : Frontiers in Artificial Intelligence and Applications

Author: Cruz J.  

Publisher: Ios Press‎

Publication year: 2005

E-ISBN: 9781607501213

P-ISBN(Paperback): 9781586035327

Subject: TP Automation Technology , Computer Technology

Keyword: 自动化技术、计算机技术

Language: ENG

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Description

Comparing the major features of biophysical inadequacy was related with the representation of differential equations. System dynamics is often modeled with the expressive power of the existing interval constraints framework. It is clear that the most important model was through differential equations but there was no way of expressing a differential equation as a constraint and integrate it within the constraints framework. Consequently, the goal of this work is focused on the integration of ordinary differential equations within the interval constraints framework, which for this purpose is extended with the new formalism of Constraint Satisfaction Differential Problems. Such framework allows the specification of ordinary differential equations, together with related information, by means of constraints, and provides efficient propagation techniques for pruning the domains of their variables. This enabled the integration of all such information in a single constraint whose variables may subsequently be used in other constraints of the model. The specific method used for pruning its variable domains can then be combined with the pruning methods associated with the other constraints in an overall propagation algorithm for reducing the bounds of all model variables.

Chapter

Solving a Constraint Satisfaction Problem

Pruning

Branching

Stopping

Constraint Satisfaction Problems With Continuous Domains

Intervals Representing Unidimensional Continuous Domains

Interval Operations and Basic Functions

Interval Approximations

Boxes Representing Multidimensional Continuous Domains

Solving Continuous Constraint Satisfaction Problems

Summary

Interval Analysis

Interval Arithmetic

Extended Interval Arithmetic

Interval Functions

Interval Extensions

Interval Methods

Univariate Interval Newton Method

Multivariate Interval Newton Method

Summary

Constraint Propagation

The Propagation Algorithm

Associating Narrowing Functions to Constraints

Constraint Decomposition Method

Constraint Newton Method

Complementary Approaches

Summary

Partial Consistencies

Local Consistency

Higher Order Consistency

Summary

Global Hull-Consistency

The Higher Order Consistency Approach

The (n+1)B-consistency Algorithm

Backtrack Search Approaches

The BS0 Algorithm

The BS1 Algorithm

The BS2 Algorithm

The BS3 Algorithm

Ordered Search Approaches

The OS1 Algorithm

The OS3 Algorithm

The Tree Structured Approach

The Data Structures

The Actions

The TSA Algorithm

Summary

Local Search

The Line Search Approach

Obtaining a Multidimensional Vector - the Newton-Raphson Method

Obtaining a New Point

Alternative Local Search Approaches

Integration of Local Search with Global Hull-Consistency Algorithms

Summary

Experimental Results

A simple example

The Census Problem

Protein Structure

Local Search

Summary

Interval Constraints for Differential Equations

Ordinary Differential Equations

Numerical Approaches

Taylor Series Methods

Errors and Step Control

Interval Approaches

Interval Taylor Series Methods

Validation and Enclosure of Solutions Between two Discrete Points

Computation of a Tight Enclosure of Solutions at a Discrete Point

Constraint Approaches

Older's Constraint Approach

Hickey's Constraint Approach

Jansen, Deville and Van Hentenryck's Constraint Approach

Summary

Constraint Satisfaction Differential Problems

CSDPs are CSPs

Value Restrictions

Maximum and Minimum Restrictions

Time and Area Restrictions

First and Last Value Restrictions

First and Last Maximum and Minimum Restrictions

Integration of a CSDP within an Extended CCSP

Canonical Solutions for Extended CCSPs

Local Search for Extended CCSPs

Modelling with Extended CCSPs

Modelling Parametric ODEs

Representing Interval Valued Properties

Combining ODE Solution Components

Summary

Solving a CSDP

The ODE Trajectory

Narrowing Functions for Enforcing the ODE Restrictions

Value Narrowing Functions

Maximum and Minimum Narrowing Functions

Time and Area Narrowing Functions

First and Last Value Narrowing Functions

First and Last Maximum and Minimum Narrowing Functions

Narrowing Functions for the Uncertainty of the ODE Trajectory

Propagate Narrowing Function

Link Narrowing Function

Improve Narrowing Functions

The Constraint Propagation Algorithm for CSDPs

Summary

Biomedical Decision Support with ODEs

A Differential Model for Diagnosing Diabetes

Representing the Model and its Constraints with an Extended CCSP

Using the Extended CCSP for Diagnosing Diabetes

A Differential Model for Drug Design

Representing the Model and its Constraints with an Extended CCSP

Using the Extended CCSP for Parameter Tuning

The SIR Model of Epidemics

Using the Extended CCSP for Predicting the Epidemic Behaviour

Summary

Conclusions and Future Work

Interval Constraints for Differential Equations

Global Hull-consistency

Local Search for Interval Constraint Reasoning

Prototype Implementation: Applications to Biophysical Modelling

Conclusions

References

Appendix A: Interval Analysis Theorems

Appendix B: Constraint Propagation Theorems

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