Higher-level Knowledge, Rational and Social Levels Constraints of the Common Model of the Mind

We present the input to the discussion about the computational framework known as Common Model of Cognition (CMC) from the working group dealing with the knowledge/rational/social levels. In particular, we present a list of the higher level constraints that should be addressed within such a general framework

Network Security Intelligence Centres for Information Security Incident Management

Programme: 6598 - Ph.D. on the Basis of Prior Published Works in Cyber SecurityIntensive IT development has led to qualitative changes in our living, which are driving current information security (IS) trends and require sophisticated structures and adequate approached to manage IS for different businesses. The wide range of threats is constantly growing in modern intranets; they have become not only numerous and diverse but more disruptive. In such circumstances, organizations realize that IS incidents’ timely detection and prevention in the future (what is more important) are not only possible but imperative. Any delay and only reactive actions to IS incidents put their assets under risk. A properly designed IS incident management system (ISIMS), operating as an integral part of the whole organization’s governance system, reduces IS incidents’ number and limits damage caused by them. To maximally automate IS incident management (ISIM) within one organization and to deepen its knowledge of IS level, this research proposes to unite together all advantages of a Security Intelligence Centre (SIC) and a Network Operations Centre (NOC) with their unique and joint toolkits and techniques in a unified Network SIC (NSIC). For this purpose the glossary of the research area was introduced, the taxonomy of IS threats, vulnerabilities, network attacks, and incidents was determined. Further, IS monitoring as one of the ISIM processes was described, the Security Information and Event Management (SIEM) systems’ role in it and their evolution were shown. The transition from Security Operations Centres (SOCs) to SICs was followed up. At least, modern network environment’s requirements for new protection solutions were formulated and it was proven that the NSIC proposed as a combination of a SIC and a NOC fully meets them. The NSIC’s zone security infrastructure with corresponding IS controls is proposed. Its implementation description at the Moscow Engineering Physics Institute concludes the research at this stage. In addition, some proposals for the training of highly qualified personnel for NSICs were formulated. The creation of an innovative NSIC concept, its interpretation, construction and initial implementation through original research presented are its main results. They contribute substantially to the modern networks’ security, as they extend the forefront of the SOCs and SICc used nowadays and generate significant new knowledge and understanding of network security requirements and solutions

Population-coding and Dynamic-neurons improved Spiking Actor Network for Reinforcement Learning

With the Deep Neural Networks (DNNs) as a powerful function approximator, Deep Reinforcement Learning (DRL) has been excellently demonstrated on robotic control tasks. Compared to DNNs with vanilla artificial neurons, the biologically plausible Spiking Neural Network (SNN) contains a diverse population of spiking neurons, making it naturally powerful on state representation with spatial and temporal information. Based on a hybrid learning framework, where a spike actor-network infers actions from states and a deep critic network evaluates the actor, we propose a Population-coding and Dynamic-neurons improved Spiking Actor Network (PDSAN) for efficient state representation from two different scales: input coding and neuronal coding. For input coding, we apply population coding with dynamically receptive fields to directly encode each input state component. For neuronal coding, we propose different types of dynamic-neurons (containing 1st-order and 2nd-order neuronal dynamics) to describe much more complex neuronal dynamics. Finally, the PDSAN is trained in conjunction with deep critic networks using the Twin Delayed Deep Deterministic policy gradient algorithm (TD3-PDSAN). Extensive experimental results show that our TD3-PDSAN model achieves better performance than state-of-the-art models on four OpenAI gym benchmark tasks. It is an important attempt to improve RL with SNN towards the effective computation satisfying biological plausibility.Comment: 27 pages, 11 figures, accepted by Journal of Neural Network

Planning while Believing to Know

Over the last few years, the concept of Artificial Intelligence (AI) has become essential in our daily life and in several working scenarios. Among the various branches of AI, automated planning and the study of multi-agent systems are central research fields. This thesis focuses on a combination of these two areas: that is, a specialized kind of planning known as Multi-agent Epistemic Planning. This field of research is concentrated on all those scenarios where agents, reasoning in the space of knowledge/beliefs, try to find a plan to reach a desirable state from a starting one. This requires agents able to reason about her/his and others’ knowledge/beliefs and, therefore, capable of performing epistemic reasoning. Being aware of the information flows and the others’ states of mind is, in fact, a key aspect in several planning situations. That is why developing autonomous agents, that can reason considering the perspectives of their peers, is paramount to model a variety of real-world domains. The objective of our work is to formalize an environment where a complete characterization of the agents’ knowledge/beliefs interactions and updates are possible. In particular, we achieved such a goal by defining a new action-based language for Multi-agent Epistemic Planning and implementing epistemic planners based on it. These solvers, flexible enough to reason about various domains and different nuances of knowledge/belief update, can provide a solid base for further research on epistemic reasoning or real-base applications. This dissertation also proposes the design of a more general epistemic planning architecture. This architecture, following famous cognitive theories, tries to emulate some characteristics of the human decision-making process. In particular, we envisioned a system composed of several solving processes, each one with its own trade-off between efficiency and correctness, which are arbitrated by a meta-cognitive module