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R. St-Aubin, J. Friedman, and Alan K. Mackworth. A Formal Mathematical Framework for Modeling Probabilistic Hybrid Systems. In Proceedings of the Ninth International Symposium on AI and Mathematics, AI-Math-06, Ft. Lauderdale, FL, January 2006.
The development of autonomous agents, such as mobile robots and software agents, has generated considerable research in recent years. Robotic systems, which are usually built from a mixture of continuous (analog) and discrete (digital) components, are often referred to as hybrid dynamical systems. Traditional approaches to real-time hybrid systems usually dene behaviors purely in terms of determinism or sometimes non-determinism. However, this is insufcient as real-time dynamical systems very often exhibit uncertain behaviour. To address this issue, we develop a semantic model, Probabilistic Constraint Nets (PCN), for probabilistic hybrid systems. PCN captures the most general structure of dynamic systems, allowing systems with discrete and continuous time/variables, synchronous as well as asynchronous event structures and uncertain dynamics to be modeled in a unitary framework. Based on a formal mathematical paradigm uniting abstract algebra, topology and measure theory, PCN provides a rigorous formal programming semantics for the design of hybrid real-time embedded systems exhibiting uncertainty.
@InProceedings{AI-Math06,
author = {R. St-Aubin and J. Friedman and Alan K. Mackworth},
title = {A Formal Mathematical Framework for Modeling Probabilistic Hybrid Systems},
year = {2006},
month = {January},
booktitle = {Proceedings of the Ninth International Symposium on AI and Mathematics, AI-Math-06},
address = {Ft. Lauderdale, FL},
abstract = {The development of autonomous agents, such as
mobile robots and software agents, has generated
considerable research in recent years. Robotic
systems, which are usually built from a mixture
of continuous (analog) and discrete (digital)
components, are often referred to as hybrid
dynamical systems. Traditional approaches to
real-time hybrid systems usually dene behaviors
purely in terms of determinism or sometimes
non-determinism. However, this is insufcient
as real-time dynamical systems very often exhibit
uncertain behaviour. To address this issue,
we develop a semantic model, Probabilistic Constraint
Nets (PCN), for probabilistic hybrid systems.
PCN captures the most general structure of
dynamic systems, allowing systems with discrete
and continuous time/variables, synchronous as
well as asynchronous event structures and uncertain
dynamics to be modeled in a unitary framework.
Based on a formal mathematical paradigm
uniting abstract algebra, topology and measure
theory, PCN provides a rigorous formal programming
semantics for the design of hybrid real-time
embedded systems exhibiting uncertainty.},
bib2html_pubtype ={Refereed Conference Proceeding},
bib2html_rescat ={},
}
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