Reliability of complex Cyber-Physical Systems is necessary to guarantee availability and/or safety of the provided services. Diverse and complex fault tolerance policies are adopted to enhance reliability, that include a varied mix of redundancy and dynamic reconfiguration to address hardware reliability, as well as specific software reliability techniques like diversity or software rejuvenation. These complex policies call for flexible runtime health checks of system executions that go beyond conventional runtime monitoring of pre-programmed health conditions, also in order to minimize maintenance costs. Defining a suitable monitoring model in the application of this method in complex systems is still a challenge. In this paper we propose a novel approach, Reliability Based Monitoring (RBM), for a flexible runtime monitoring of reliability in complex systems, that exploits a hierarchical reliability model periodically applied to runtime diagnostics data: this allows to dynamically plan maintenance activities aimed at prevent failures. As a proof of concept, we show how to apply RBM to a 2oo3 software system implementing different fault-tolerant policies.

We propose a novel online learning algorithm, called SpCoSLAM 2.0, for spatial concepts and lexical acquisition with high accuracy and scalability. Previously, we proposed SpCoSLAM as an online learning algorithm based on unsupervised Bayesian probabilistic model that integrates multimodal place categorization, lexical acquisition, and SLAM. However, our previous algorithm had limited estimation accuracy owing to the influence of the early stages of learning, and increased computational complexity with added training data. Therefore, we introduce techniques such as fixed-lag rejuvenation to reduce the calculation time while maintaining an accuracy higher than that of the previous algorithm. The results show that, in terms of estimation accuracy, the proposed algorithm exceeds the previous algorithm and is comparable to batch learning. In addition, the calculation time of the proposed algorithm does not depend on the amount of training data and becomes constant for each step of the scalable algorithm. Our approach will contribute to the realization of long-term spatial language interactions between humans and robots.