The purpose of this project is to extend the MCAI paradigm to reasoning about the behavior of models of complex physical systems, such as those found in biological and embedded-control areas. In particular, we have identified four problems - pancreatic-cancer modeling, atrial-fibrillation detection, distributed automotive control, and aerospace control software - which will be used as technology drivers and test-beds for the results obtained in the course of the project. For each of these problems, a successful outcome is likely to have significant societal, economic, and scientific benefits.
While the application of MCAI technologies to embedded control systems does not seem to pose conceptual hurdles, for biological systems it might be difficult to see how MCAI could be applied. The emerging field of Systems Biology combines experimental methods with computational modeling to elicit mechanistic models that seek to describe the actual molecular or cellular mechanisms that drive biological processes. In this sense, mechanistic models are equivalent to the kinds of models that have been the target of Model Checking and Abstract Interpretation in the past. We believe that MCAI 2.0 will play a critical and transformative role in the analysis of mechanistic models for biological systems in the future.