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Symbolic grounding is a bridge between high-level planning and actual robot sensing, and actuation. Uncertainties raised by the unstructured environment make a bottleneck for integrating traditional artificial intelligence with service robotics. This paper presents a fuzzy logic based approach to formalise the grounding problems into a fuzzy optimization problem, which is robust to uncertainties. Novel techniques are applied to establish the objective function, to model fuzzy constraints and to perform fuzzy optimisation. The outcome is tested with a service robot fetch and carry task, where the fuzzy optimisation approach helps the robot to determine the most comfortable position (location and orientation) for grasping objects. Experimental results show that the proposed approach improves the robustness of the task implementation in unstructured environments.

Symbolic grounding in unstructured environments remains an important challenge in robotics [7]. Homecare robots are often required to be instructed by their human users intuitively, which means the robots are expected to take highlevel commands and execute corresponding tasks in a domestic environment. High-level commands are represented with symbolic terms such as “near” and “close” and, on the other hand, robots are controlled based on trajectories. The robots need to translate the symbolic terms to trajectories. In addition, domestic environment is unstructured where the same objects can be placed in different places over the time. This increases the difficulties in symbolic grounding. This paper presents a fuzzy logic based approach to symbolic grounding. In this approach, grounded concepts are modelled as fuzzy sets and the existing knowledge is used to deduce grounded values given real-time sensory inputs. Experiments results show that this approach works well in unstructured environment.

Internet of Things (IoT) has given rise to the fourth industrial revolution (Industrie 4.0), and it brings great benefits by connecting people, processes and data. However, cybersecurity has become a critical challenge in the IoT enabled cyber physical systems, from connected supply chain, Big Data produced by huge amount of IoT devices, to industry control systems. Evolutionary computation combining with other computational intelligence will play an important role for cybersecurity, such as artificial immune mechanism for IoT security architecture, data mining/fusion in IoT enabled cyber physical systems, and data driven cybersecurity. This paper provides an overview of security challenges in IoT enabled cyber-physical systems and what evolutionary computation and other computational intelligence technology could contribute for the challenges. The overview could provide clues and guidance for research in IoT security with computational intelligence.

The massive increase of spam is posing a very serious threat to email and SMS, which have become an important means of communication. Not only do spams annoy users, but they also become a security threat. Machine learning techniques have been widely used for spam detection. Email spams can be detected through detecting senders’ behaviour, the contents of an email, subject and source address, etc, while SMS spam detection usually is based on the tokens or features of messages due to short content. However, a comprehensive analysis of email/SMS content may provide cures for users to aware of email/SMS spams. We cannot completely depend on automatic tools to identify all spams. In this paper, we propose an analysis approach based on information entropy and incremental learning to see how various features affect the performance of an RBF-based SVM spam detector, so that to increase our awareness of a spam by sensing the features of a spam. The experiments were carried out on the spambase and SMSSpemCollection databases in UCI machine learning repository. The results show that some features have significant impacts on spam detection, of which users should be aware, and there exists a feature space that achieves Pareto efficiency in True Positive Rate and True Negative Rate.