Games, Time, and Probabilities: Models and Algorithms for System Design and Analysis
published: April 16, 2011, recorded: April 2011, views: 3906
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Digital technology, from medical implants to drive-by-wire systems, is increasingly deployed in safety-critical situations. This calls for systematic design and verification methodologies that can cope with three major sources of system complexity: concurrency, real time, and uncertainty. We advocate a two-step process: formal modeling followed by algorithmic analysis (or, "model building" followed by "model checking"). We model the components of a concurrent system as potential collaborators or adversaries in a multi-player game with temporal objectives, such as system safety. The real-time aspects of hardware and software are modeled by hybrid dynamical systems that combine both discrete state transitions and continuous state evolutions. Uncertainty in the environment is naturally modeled by stochastic behavior. As a result, we obtain three orthogonal extensions of basic state-transition graph models of systems --game graphs, timed graphs, and Markov decision processes-- and all combinations thereof.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !