Wang Yi

Wang Yi, Morteza Mohaqeqi, Susanne Graf

Inspired by the pioneering work of Gilles Kahn on concurrent systems, we propose to model timed systems as a network of software components (implemented as real-time processes or tasks), each of which is specified to compute a collection of functions according to given timing constraints. We present a fixed-point semantics for this model which shows that each system function of such a network computes for a given set of (timed) input streams, a deterministic (timed) output stream. As a desired feature, such a network model can be modified by integrating new components for adding new system functions without changing the existing ones. Additionally, existing components may be replaced also by new ones fulfilling given requirements. Thanks to the deterministic semantics, a model-based approach is enabled for not only building systems but also updating them after deployment, allowing for efficient analysis techniques such as model-in-the-loop simulation to verify the complete behaviour of the updated system.

Joohyung Lee, Yi Wang

LPMLN is a probabilistic extension of answer set programs with the weight scheme derived from that of Markov Logic. Previous work has shown how inference in LPMLN can be achieved. In this paper, we present the concept of weight learning in LPMLN and learning algorithms for LPMLN derived from those for Markov Logic. We also present a prototype implementation that uses answer set solvers for learning as well as some example domains that illustrate distinct features of LPMLN learning. Learning in LPMLN is in accordance with the stable model semantics, thereby it learns parameters for probabilistic extensions of knowledge-rich domains where answer set programming has shown to be useful but limited to the deterministic case, such as reachability analysis and reasoning about actions in dynamic domains. We also apply the method to learn the parameters for probabilistic abductive reasoning about actions.