Proceedings of the PhD Symposium at iFM’17 on Formal Methods: Algorithms, Tools and Applications (PhD-iFM’17)

Virtually timed ambients is a calculus of nested virtualization, which models timing and resource consumption for hierarchically structured virtual machines. This structure may change dynamically to support load-balancing, migration, and scaling. This paper introduces resource-awareness for virtually timed ambients, which enables processes to actively query the system about the resources necessary for a task and to reconfigure accordingly. Technically we extend virtually timed ambients with context-expressions using modal logic operators, give a formal semantics for the extension, and define bisimulation for resource-aware virtually timed systems. The paper also provides a proof of concept implementation in Maude and a case study involving dynamic auto scaling. For further details, please refer to the full paper appearing in the iFM 2018 conference proceedings. Task Planning with OMT: an Application to Production Logistics Francesco Leofante, Erika Abraham, Armando Tacchella RWTH Aachen University, Germany University of Genova,Italy leofante@cs.rwth-aachen.de, abraham@informatik.rwth-aachen.de, armando.tacchella@unige.it Abstract. Task planning is a well-studied problem for which interesting applications exist in production logistics. Planning for such domains requires to take into account not only feasible plans, but also optimality targets, e.g., minimize time, costs or energy consumption. Although there exist several algorithms to compute optimal solutions with formal guarantees, heuristic approaches are typically preferred in practical applications, trading certified solutions for a reduced computational cost. Reverting this trend represents a standing challenges within the domain of task planning at large. In this paper we discuss our experience using Optimization Modulo Theories to synthesize optimal plans for multirobot teams handling production processes within the RoboCup Logistics League. Besides presenting our results, we discuss challenges and possible directions for future development of OMT planning. For further details, please refer to "Task Planning with OMT: an Application to Production Logistics" appearing in iFM 2018 Task planning is a well-studied problem for which interesting applications exist in production logistics. Planning for such domains requires to take into account not only feasible plans, but also optimality targets, e.g., minimize time, costs or energy consumption. Although there exist several algorithms to compute optimal solutions with formal guarantees, heuristic approaches are typically preferred in practical applications, trading certified solutions for a reduced computational cost. Reverting this trend represents a standing challenges within the domain of task planning at large. In this paper we discuss our experience using Optimization Modulo Theories to synthesize optimal plans for multirobot teams handling production processes within the RoboCup Logistics League. Besides presenting our results, we discuss challenges and possible directions for future development of OMT planning. For further details, please refer to "Task Planning with OMT: an Application to Production Logistics" appearing in iFM 2018 A Formal Framework to Unified Metamodel for Consistent Transformation Jagadeeswaran Thangaraj and Senthilkumaran Ulaganathan 1 Maynooth University, Maynooth, Ireland 2 VIT University, Vellore, TN, India Jagadeeswaran.Thangaraj@mu.ie