2 research outputs found
Productivity equation and the m distributions of information processing in workflows
This research investigates an equation of productivity for workflows
regarding its robustness towards the definition of workflows as probabilistic
distributions. The equation was formulated across its derivations through a
theoretical framework about information theory, probabilities and complex
adaptive systems. By defining the productivity equation for organism-object
interactions, workflows mathematical derivations can be predicted and monitored
without strict empirical methods and allows workflow flexibility for
organism-object environments.Comment: 6 pages, 0 figure
A Formal Mathematical Framework for Modeling Probabilistic Hybrid Systems.
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.