Following his interest in real world applications, Riccardo’s research agenda has focused on Scheduling and Execution Monitoring & Control. Given his background in Artificial Intelligence, his work has mainly centered on resource constrained project scheduling problem solving with Constraint Programming techniques. Riccardo’s PhD work has delved into the Reactive Scheduling problem, specifically through the assessment of the efficacy of different proactive and reactive execution strategies in the face of executional uncertainty. Great attention has been dedicated to the characterization & management of schedule robustness, as the scheduling strategies vary within the space. In this respect, Riccardo is currently studying the issue of reactive scheduling benchmarks synthesis, to the aim of providing techniques to objectively test different rescheduling procedures by means of reproducible experiments based on execution simulations of dynamically disturbed plans; A significant part of Riccardo’s research work is devoted to the study of algorithms and heuristics for pure scheduling problems. In particular, Riccardo is interested in stochastic search procedures and optimization metaheuristics for the resolution of notoriously difficult scheduling problems. Within the RoboCare Project (see Projects section), Riccardo’s work has centered on Sensor-based execution monitoring and on-line rescheduling of real world plans, to the aim of analyzing dynamically the consequences of the variation of the temporal information within the original plan. The internship spent at the European Space Agency and the long term collaboration with the Planning & Scheduling Team have helped Riccardo build up a considerable experience in space-related problems and applications. Currently, within the GOAC Project (Goal Oriented Autonomous Controller), Riccardo is tackling the problem of autonomous execution of plans on space exploration rovers. In particular, Riccardo’s goal is to study the properties (i.e., dispatchability and controllability) introduced to guarantee sufficient stability conditions for plans constrained on time, parameters and resources, under execution in environments characterized by uncertainty.