Autonomous vehicles are complex distributed systems consisting of multiple software applications and computing nodes. Determining the assignment between these software applications and computing nodes is known as the application-placement problem. The input of this problem is a set of applications, their requirements, a set of computing nodes, and their provided resources. Due to the potentially large solution space of the problem, an optimization goal defines which solution is desired the most. However, the optimization goal used for the application-placement problem is not static but has to be adapted according to the current context the vehicle is experiencing. Therefore, an approach for a context-based determination of the optimization goal for a given instance of an application-placement problem is required. In this paper, we introduce C-po, an approach to address this issue. C - PO ensures that if the safety level of a system drops due to an occurring failure, the optimization goal for the successively executed application-placement determination aims to restore the safety level. Once the highest safety level is reached, C-po optimizes the application placement according to the current driving situation. Furthermore, we introduce two methods for dynamically determining the required level of safety.

Fictitious play is a popular game-theoretic model of learning in games. However, it has received little attention in practical applications to large problems. This paper introduces two variants of fictitious play that are implemented in behavioural strategies of an extensive-form game. The first variant is a full-width process that is realization equivalent to its normal-form counterpart and therefore inherits its convergence guarantees. However, its computational requirements are linear in time and space rather than exponential. The second variant, Fictitious Self-Play, is a machine learning framework that implements fictitious play in a sample-based fashion. Experiments in imperfect-information poker games compare our approaches and demonstrate their convergence to approximate Nash equilibria.