Biblio

The classical SWOT methodology and many of the tools based on it used so far are very static, used for one stable project and lacking dynamics [1]. This paper proposes the idea of combining several SWOT analyses enriched with computing with words (CWW) paradigm into a single network. In this network, individual analysis of the situation is treated as the node. The whole structure is based on fuzzy cognitive maps (FCM) that have forward and backward chaining, so it is called fuzzy SWOT maps. Fuzzy SWOT maps methodology newly introduces the dynamics that projects are interacting, what exists in a real dynamic environment. The whole fuzzy SWOT maps network structure has explainable artificial intelligence (XAI) traits because each node in this network is a "white box"-all the reasoning chain can be tracked and checked why a particular decision has been made, which increases explainability by being able to check the rules to determine why a particular decision was made or why and how one project affects another. To confirm the vitality of the approach, a case with three interacting projects has been analyzed with a developed prototypical software tool and results are delivered.

Network security is an important issue in today's world with existence of network systems that communicate data and information about all aspects of our life, work and business. Network security is an important issue with connected networks and data communication between organisations of that specialized in different areas. Network security engineers spend a considerable amount of time to investigate network for security breaches and to enhance the security of their networks and data communications on their networks. They use Attack Graphs (AGs) which are graphical representation of networks to assist them in analysing large networks. With increase size of networks and their complexity, the use of attack graphs alone does not provide the necessary risk analysis and assessment facilities. There is a need for automated intelligent systems such as multiagent systems to assist in analysing, assessing and testing networks. Network systems changes with the increase in the size of organisation and connectivity of network of organisations based on the business needs or organisational or governmental rules and regulations. In this paper a multi-agent system is developed assist in analysing interconnected network to identify security risks. The multi-agent system is capable of security network analysis to identify paths using an attack graph of the network under consideration to protect network systems, as the networks grow and change, against possible attacks. The multiagent system uses a model developed by Mohammadian [3] for converting AGs to Fuzzy Cognitive Maps (FCMs) to identify attack paths from attack graphs and perform security risk analysis. In this paper a novel decision-making approach using FCMs is employed.

With the introduction of computer methods, the amount of material and processing accuracy of policy text analysis have been greatly improved. In this paper, Text mining(TM) and latent semantic analysis(LSA) were used to collect policy documents and extract policy elements from them. Fuzzy association rule mining(FARM) technique and partial association test (PA) were used to discover the causal relationships and impact degrees between elements, and a fuzzy cognitive map (FCM) was developed to deduct the evolution of elements through a soft computing method. This non-interventionist approach avoids the validity defects caused by the subjective bias of researchers and provides policy makers with more objective policy suggestions from a neutral perspective. To illustrate the accuracy of this method, this study experimented by taking the state-owned capital layout adjustment related policies as an example, and proved that this method can effectively analyze policy text.

Today's more reliable communication technology, together with the availability of higher computational power, have paved the way for introduction of more advanced automation systems based on distributed intelligence and multi-agent technology. However, abundance of data, while making these systems more powerful, can at the same time act as their biggest vulnerability. In a web of interconnected devices and components functioning within an automation framework, potential impact of malfunction in a single device, either through internal failure or external damage/intrusion, may lead to detrimental side-effects spread across the whole underlying system. The potentially large number of devices, along with their inherent interrelations and interdependencies, may hinder the ability of human operators to interpret events, identify their scope of impact and take remedial actions if necessary. Through utilization of the concepts of graph-theoretic fuzzy cognitive maps (FCM) and expert systems, this paper puts forth a solution that is able to reveal weak links and vulnerabilities of an automation system, should it become exposed to partial internal failure or external damage. A case study has been performed on the IEEE 34-bus test distribution system to show the efficiency of the proposed scheme.