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A Formal Mathematical Framework for Modeling Probabilistic Hybrid Systems. (2005)
| Content Provider | CiteSeerX |
|---|---|
| Author | St-Aubin, Robert Mackworth, Alan K. |
| 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 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. 1 |
| File Format | |
| Publisher Date | 2005-01-01 |
| Access Restriction | Open |
| Subject Keyword | Formal Mathematical Framework Modeling Probabilistic Hybrid System Real-time Hybrid System Dynamic System Continuous Time Variable Mobile Robot Uncertain Dynamic Hybrid Dynamical System Rigorous Formal Programming Semantics Software Agent Unitary Framework Abstract Algebra Asynchronous Event Structure Exhibit Uncertain Behaviour Robotic System Measure Theory Real-time Dynamical System Probabilistic Hybrid System Semantic Model General Structure Probabilistic Constraint Net Formal Mathematical Paradigm Considerable Research |
| Content Type | Text |
