Abstract I develop novel intelligent approximation algorithms for solving modern problems of CyberPhysical Systems (CPS), such as control and verifi- cation, by combining advanced statistical methods. it is important for the control algorithms underlying the class of multi-agent CPS to be resilient to various kinds of attacks. I designed a very general adaptive receding-horizon synthesis approach to planning and control that can be applied to controllable stochastic dynamical systems. Apart from being fast and efficient, it provides statistical guarantees of convergence. The optimization technique based on the best features of Model Predictive Control and Particle Swarm Optimization proves to be robust in finding a winning strategy in the stochastic non-cooperative games against a malicious attacker. The technique can further benefit probabilistic model checkers and real-world CPS