Research on process flexibility has traditionally explored alternative ways of considering flexibility during the design of a business process. The focus typically has been on ways...
We describe a novel integration of Planning with Probabilistic State Estimation and Execution resulting in a unified representational and computational framework based on declarat...
Conor McGann, Frederic Py, Kanna Rajan, John Ryan,...
We present a new algorithm, GM-Sarsa(0), for finding approximate solutions to multiple-goal reinforcement learning problems that are modeled as composite Markov decision processe...
Difficult real-time AI problems require a means for expressing multi-dimensional and dynamic goal criteria and a principled model for satisficing to best meet the criteria. In the...
Abstract. We define a probabilistic contract framework for the construction of component-based embedded systems, based on the theory of Interactive Markov Chains. A contract specif...