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R. St-Aubin, J. Friedman, and Alan K. Mackworth. A Formal Mathematical Framework for Modeling Probabilistic Hybrid Systems. Annals of Mathematics and Artificial Intelligence, 37(3-4):397–425, 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 define behaviors purely in terms of determinism or sometimes non-determinism. However, this is insufficient as realtime dynamical systems very often exhibit uncertain behavior. 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 exploiting abstract algebra, topology and measure theory, PCN provides a rigorous formal programming semantics for the design of hybrid real-time embedded systems exhibiting uncertainty.
@Article{AMAI06,
author = {R. St-Aubin and J. Friedman and Alan K. Mackworth},
title = {A Formal Mathematical Framework for Modeling Probabilistic Hybrid Systems},
year = {2006},
journal = {Annals of Mathematics and Artificial Intelligence},
volume = {37},
number = {3-4},
pages = {397--425},
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 define behaviors purely in terms of
determinism or sometimes non-determinism. However, this is insufficient as realtime
dynamical systems very often exhibit uncertain behavior. 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 exploiting 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 Journal},
bib2html_rescat ={},
}
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