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The propagation of cyber-attacks targeting modern industrial cyber-physical systems (ICPSs) is considered a sophisticated and persistent cross-layer penetration process, posing significant cyber-to-physical (C2P) risks to critical industrial infrastructures. However, existing defensive solutions for ICPSs lack the global defensive decision-making capacity against such persistent and stealthy cross-layer threats. This paper proposes a Bayesian-Stochastic hybrid game-theoretic approach that can generate optimal defensive strategies throughout the cross-layer penetration lifecycle, even without full information about attackers. Specifically, we first present a unified quantification framework to guarantee consistent utility function configurations in a cyber-physical layer integrated system. Then, we propose a state transition-based stochastic game model to characterize the dynamic evolution process of cross-layer penetration. The attack-defense interaction in each state is modeled as an incomplete-information Bayesian game to capture the inherent uncertainties of attacker information. Furthermore, a multi-agent Bayesian Q-learning (MABQL) algorithm is developed to learn the optimal defensive strategies despite incomplete information. The proposed approach is implemented and assessed based on a real-world ICPS testbed and the numerical results validate its feasibility and effectiveness for making optimal defensive decisions. [ABSTRACT FROM AUTHOR]

In decision-making systems, conflict management is an important concept that represents the degree of dissimilarity between bodies of evidence, ultimately enhancing decision-making performance. Jousselme's distance, as the most commonly employed one so far, is used to measure the distance between basic belief assignments (BBAs) in Dempster-Shafer (D-S) evidence theory. However, the Jousselme's distance has limitations, which can also be demonstrated in other methods theoretically. To address this issue, a BBA refinement method and a novel multi-granularity distance are proposed in this paper. Moreover, the methods are verified to be effective in the problems that Jousselme's distance cannot satisfy. Additionally, a hypothetical physical model is employed to verify the practicability of the proposed methods with multiple granularity. Furthermore, based on the proposed multiple granularity distance, a novel decision-making algorithm is designed. The results validate that the proposed decision-making method is beneficially applicable to classification scenarios and different real-world data. [ABSTRACT FROM AUTHOR]

[Display omitted] • First, in the proposed method, to identify the strong positive, positive, neutral, negative, and strong negative sentiment orientations of aspects in tweets, we consider not only clear phrases but also fuzzy sentiment phrases. This is a valuable attempt at increasing performance for analyzing sentiment of aspects. • Second, the main strength of the proposed method is the simultaneous consideration of user satisfaction, dissatisfaction, and hesitation when evaluating aspects of a given topic. • Then, they are used to build a fuzzy decision tree. The consideration of the fuzzy sentiment phrases and the dissatisfaction, hesitation of users that help the proposed method overcomes the limitations of the previous studies. • More importantly, this method has developed and enriched theories and techniques for decision-making based on sentiment in Tweets. Social networks are a very popular channel for people to communicate with, to find, to reference other users before making decisions, especially those concerning purchase. How can users' opinions within social networks be used in making decisions cost-effective and reliable? In this paper, we propose an approach for supporting decision-making based on measuring the user satisfaction level by analyzing the sentiment of aspects and mining the fuzzy decision trees. Our proposal has been proved to overcome some of the disadvantages of previous methods. Specifically, we consider the fuzzy sentiments of users for aspects and the effects of user satisfaction, dissatisfaction, and hesitation for decision-making. The proposed method comprises four main stages. The first stage identifies a topic, which the user is interested. In the second stage, aspects of the topic and their sentiments within tweets are extracted. At the third stage, the user satisfaction level is calculated according to each kind of sentiment identified in the second step. Finally, a decision matrix is constructed, and the fuzzy decision tree is built to generate a set of rules for supporting users in decision-making. The experiments using tweets show that the proposed method achieves promising results regarding the accuracy and gained information. [ABSTRACT FROM AUTHOR]

This study introduces an extension of the Characteristic Objects Method (COMET) by incorporating the Expected Solution Point (ESP) approach to enhance individual decision-making. By integrating the ESP methodology, we aim to simplify decision-making, particularly when facing a vast decision matrix concerning multiple criteria. We demonstrate the efficacy of the proposed ESP-COMET method by applying it with one or two ESPs, thereby examining its impact on ranking alternatives according to personal customer preferences. In order to provide a practical example, we use a dataset of used cars to show how the proposed method facilitates the selection of the most suitable vehicle based on individual needs. The main goal of this paper is to show how the ESP-COMET method can help to adapt decisions to individual needs and expectations. We also introduce a novel and efficient approach for determining characteristic values in personalized decision-making, which plays a crucial role in ensuring the higher accuracy of the ESP-COMET method. Additionally, we compare the proposed approach with such methods as Stable Preference Ordering Toward the Ideal Solution (SPOTIS) and Reference Ideal Method (RIM). This study addresses the research gap by introducing an easy way to define customer preferences within COMET, enabling responsible decision-making. [ABSTRACT FROM AUTHOR]

This paper focuses on the problem of constructing aggregation functions which support the concept of partial agreement when only one or more sub-group(s) of decision makers and criteria, not necessarily all of them, is (are) involved to take the final action. The proposed method is started with computing all different α -combinations from an n -element set of inputs, according to the binomial coefficient, and then completed by combining their aggregations. This approach, so-called combination operator-based aggregation function, guarantees to consider various agreement scenarios at different consensus levels in contrary with the traditional composition-based construction methods that act as a full agreement. The attention is then given to the weighted case specially the conditions of weighting vectors for ensuring the well-defined property of the proposed method. As an application, the combination operator-based aggregation function is generalized over the Cartesian product of unit intervals [0,1] to deal with multi-polar information. The proposed aggregating methodology is then used to reach consensus in a group decision-making problem. [ABSTRACT FROM AUTHOR]

In recent years, much research has been conducted on path replanning in a dynamic environment, as it is considered to be a more complex problem than path planning in a static environment. However, with respect to determining when to replan a path, most research on dynamic path replanning typically generates a new path if obstacles collide with the current path, or with reference to each time unit interval. As these approaches do not focus on effective path regeneration, they result in redundant and unnecessary path regeneration. In this context, this paper proposes an intelligent decision-making mechanism for path regeneration to determine whether a new path should be generated when obstacles move over time. Based on the identification of certain important obstacles, we let these obstacles propagate influence, and then obtain path values that represent the distance between the obstacles and the path. Then, the necessity of path regeneration is determined according to the changes in path values in a dynamic environment. Using this approach, it is possible to both enhance paths in terms of distance and resolve any infeasibilities due to moving obstacles. In the experiment, we test the proposed methodology to verify that appropriate decisions are made, and compare it with other approaches that regenerate a path at each time interval or on collision. Our results show that the paths are regenerated only if it is necessary to improve the current path or to resolve infeasibilities, indicating that effective and intelligent decisions for path regeneration can be obtained using the proposed decision mechanism. [ABSTRACT FROM AUTHOR]

Abstract: Today’s business world is highly competitive and unpredictable, so effective decision-making is of primary importance. However, it is difficult to make effective decisions when sufficient information is not available, and decision-making in such situations involves a high risk of error. Conventional statistics based approaches to such problems are not effective, because in such situations decision-making is usually in the hands of a small panel of experts. However, the expert opinions can be represented by probability distribution functions. Thus, such a problem reduces to the aggregation of a set of probability distribution functions to an aggregated or consensus distribution. In this paper, we propose a new approach to address this problem. The novelties of the proposed approach include: (1) the problem is formulated as an optimization problem and (2) the overlapping area between an individual expert’s distribution and an aggregated distribution is taken to measure the expertise level of that expert and subsequently to determine the weight of the expert. The proposed approach in this paper is illustrated by an example reported in literature handled with the Delphi method, which also shows the effectiveness of our approach. [Copyright &y& Elsevier]

Abstract: As a matter of fact, humans continuously delegate and distribute cognitive functions to the environment to lessen their limits. They build models, representations, and other various mediating structures that are considered to aid thought. In doing these, humans are engaged in a process of cognitive niche construction. In this sense, I argue that a cognitive niche emerges from a network of continuous interplays between individuals and the environment, in which people alter and modify the environment by mimetically externalizing fleeting thoughts, private ideas, etc. into external supports. Through mimetic activities, humans create external semiotic anchors that are the result of a process in which concepts, ideas, and thoughts are projected onto external structures. Once concepts and thoughts are externalized and projected, new chances and ways of inferring come up from the blend. For cognitive niche construction may also contribute to make available a great portion of chances – in terms of information and knowledge – that otherwise would remain simply unexpressed or unreachable. The central part of the paper will illustrate that “abduction” – or reasoning to explanatory hypotheses – is also central to understanding some features of the problem of action and decision-making. Abduction prompts action and plays a key role in decision-making, like Peirce teaches: the neurological perspective depicted in this paper also increases knowledge about the distinction between thought and motor action, seeing both aspects as fruit of brain activity. We can say that thought possesses an essential “motor” component reflected in brain action but not in actual movement. On the basis of this analysis, I can further illustrate some problems related to the role of abducting chances in decision-making, both in deliberate and in unconscious cases. [Copyright &y& Elsevier]

Abstract: We develop a new approach that uses the ordered weighted averaging (OWA) operator in the selection of financial products. In doing so, we introduce the ordered weighted averaging distance (OWAD) operator and the ordered weighted averaging adequacy coefficient (OWAAC) operator. These aggregation operators are very useful for decision-making problems because they establish a comparison between an ideal alternative and available options in order to find the optimal choice. The objective of this new model is to manipulate the attitudinal character of previous methods based on distance measures, so that the decision maker can select financial products according to his or her degree of optimism, which is also known as the orness measure. The main advantage of using the OWA operator is that we can generate a parameterized family of aggregation operators between the maximum and the minimum. Thus, the analysis developed in the decision process by the decision maker is much more complete, because he or she is able to select the particular case in accordance with his or her interests in the aggregation process. The paper ends with an illustrative example that shows results obtained by using different types of aggregation operators in the selection of financial products. [Copyright &y& Elsevier]

Abstract: One of the tasks of decision-making support systems is to develop methods that help the designer select a solution among a set of actions, e.g. by constructing a function expressing his/her preferences over a set of potential solutions. In this paper, a new method to solve multiobjective optimization (MOO) problems is developed in which the user’s information about his/her preferences is taken into account within the search process. Preference functions are built that reflect the decision-maker’s (DM) interests and use meaningful parameters for each objective. The preference functions convert these objective preferences into numbers. Next, a single objective is automatically built and no weight selection is performed. Problems found due to the multimodality nature of a generated single cost index are managed with Genetic Algorithms (GAs). Three examples are given to illustrate the effectiveness of the method. [Copyright &y& Elsevier]

Abstract: A soft risk map of natural disaster is aimed at the visualization of risk levels of natural disasters defined by the fuzzy probabilities. In this paper, we discuss three soft risk maps and show their applications. A soft risk map of flood is applied to help choose zones for investment in projects whose profits depend on water resources, cost and flood risk. A soft risk map of drought can help an investor to choose an appropriate city to invest in a flower shop whose profits depend on drought risk and the number of customers. With a soft risk map of earthquake, a licensee designing a nuclear power station can reasonably adjust the earthquake-resistant parameters to avoid underestimating earthquake. [Copyright &y& Elsevier]