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The developing academic field of machine ethics seeks to make artificial agents safer as they become more pervasive throughout society. In contrast to computer ethics, machine ethics is concerned with the behavior of machines toward human users and other machines. This study aims to use an action-based ethical theory founded on the combinational aspects of deontological and teleological theories of ethics in the construction of an artificial moral agent (AMA).

The decision results derived by the AMA are acquired via fuzzy logic interpretation of the relative values of the steady-state simulations of the corresponding rule-based fuzzy cognitive map (RBFCM).

Through the use of RBFCMs, the following paper illustrates the possibility of incorporating ethical components into machines, where latent semantic analysis (LSA) and RBFCMs can be used to model dynamic and complex situations, and to provide abilities in acquiring causal knowledge.

This approach is especially appropriate for data-poor and uncertain situations common in ethics. Nonetheless, to ensure that a machine with an ethical component can function autonomously in the world, research in artificial intelligence will need to further investigate the representation and determination of ethical principles, the incorporation of these ethical principles into a system’s decision procedure, ethical decision-making with incomplete and uncertain knowledge, the explanation for decisions made using ethical principles and the evaluation of systems that act based upon ethical principles.

To date, the conducted research has contributed to a theoretical foundation for machine ethics through exploration of the rationale and the feasibility of adding an ethical dimension to machines. Further, the constructed AMA illustrates the possibility of utilizing an action-based ethical theory that provides guidance in ethical decision-making according to the precepts of its respective duties. The use of LSA illustrates their powerful capabilities in understanding text and their potential application as information retrieval systems in AMAs. The use of cognitive maps provides an approach and a decision procedure for resolving conflicts between different duties.

This paper suggests that cognitive maps could be used in AMAs as tools for meta-analysis, where comparisons regarding multiple ethical principles and duties can be examined and considered. With cognitive mapping, complex and abstract variables that cannot easily be measured but are important to decision-making can be modeled. This approach is especially appropriate for data-poor and uncertain situations common in ethics.

The purpose of this paper is to apply a fuzzy LinPreRa cognitive map to evaluate the interaction and importance of factors affecting the development of strategic alliance partnerships between the outlying island duty-free shops and existing collaboration firms in duty-free shops. Meanwhile, the key factors should be considered in establishing strategic alliance partnerships while analyzing and comparing the perspectives of owners for outlying island duty-free shop and partner vendors of collaborating firms along with differences of influencing key factors on partnership quality.

This study incorporates a fuzzy linguistic preference relation analytical network process (fuzzy LinPreRa ANP) in the fuzzy cognitive map (FCM) method to formulate a fuzzy LinPreRa cognitive map to evaluate the interactions and importance of key factors and the conditions of interactive impacts during the establishment of strategic alliance partnerships. The authors use the outlying island duty-free shops in Taiwan as the empirical subject to illustrate how the fuzzy LinPreRa cognitive map is applied. In-depth, interviews and questionnaire surveys are conducted to collect and evaluate respondents concerning key factors affecting strategic alliance partnerships establishment.

The following three findings based on the results of empirical analysis: first, the administrative behavioral patterns of managers for strategic alliance partnerships encompass shared values and goal coherence, while the associative statements are located on the first layer of fuzzy LinPreRa cognitive map core associations, which illustrates that businesses attach great importance to conceptual ideas. Second, integrity and reputations of both parties are the governing mechanism of strategic alliance partnerships, influencing mutual reputation. Third, the relationship of strategic alliance partnerships refers to the profit opportunities of both parties and their ability to respond to the market, including future development, regional indicators, marketing capabilities, brand multiplicity and customer retention. However, it can be inferred that such associative factors are located in the outer layer or belong to noncore associations, which means that both parties’ abilities to respond to market reactions are weakened.

This study provides valuable relationship managerial strategies to maintain long-term partnerships for outlying island duty-free shops and their alliance collaborating firms including strengthened relationships of both parties’ managers to achieve common values and consistent objectives; improved beneficial value of both parties in strategic alliance partnerships; continued close communications to enhance the quality of strategic alliance partnerships; and establishment of personnel training mechanisms and strict formulation of management rules for strategic alliance partnerships.

The main valuable contributions are included the fuzzy LinPreRa cognitive map by combining two different decision methods including FCM and fuzzy LinPreRa ANP is proposed to help decision makers to improve the evaluation quality and calculation efficiency for critical elements’ interaction and importance; the fuzzy LinPreRa cognitive map can clarify considering significant factors when maintaining strategic alliance partnerships and further provide valuable relationship managerial strategies to maintain long-term relationships for duty-free shop owners and their alliance collaborating firms.

Conventional rule‐based systems are insufficient for description of complex dynamic systems requiring nontrivial decision procedures. Fuzzy cognitive maps seem to be convenient to overcome these limitations. However, they lack ability of self‐learning and therefore some adaptation approaches are needed. The purpose of this paper is both to show the use of fuzzy cognitive maps for such systems and to present migration algorithms as convenient adaptation means.

Some problems of a complex dynamic system description by knowledge‐based means are discussed. Fuzzy cognitive maps are presented as a possible way to solve these problems followed by description of migration algorithms as their adaptation means. Their use is clarified on an example of the so‐called parking problem based on path planning using a graph search algorithm and a traffic simulation system.

After series of simulations the reality of the proposed system and selected methods with their modifications was proved. It has shown the robustness of the presented solution under circumstances of uncertainty, too.

The paper points to stability investigation of the proposed approach introducing uncertainties into the traffic simulation system to take into account, e.g. unexpected events. Further, a possibility of developing a linguistic information retrieval system is mentioned.

The proposed approach can find various implementations not only in planning tasks but also in robotic navigation and multi‐agent applications in general. In addition, it suggests possibilities of knowledge‐based systems, directly using human‐like approaches, to areas of decision making under uncertainties and contradictories.

An new modification of migration algorithms for adaptation of parameters for fuzzy cognitive maps is introduced and compared to other known self‐learning methods. Further, the concept of a traffic simulation system for path planning is presented.

The purpose of this paper is to explore a very new approach of creating scenarios with fuzzy cognitive maps (FCM); specifically to create scenarios for wind energy deployment.

In this paper a small exercise is conducted to illustrate the usefulness of FCM‐based scenarios.

Scenario‐based planning is often used in technology management and scenarios are generated to cater for uncertainty and facilitate development of responsive and robust strategic plans. Scenario analysis helps to identify different alternatives of the future state of technologies, needs, policies and environment. Scenario planning helps to overcome thinking limitations by presenting multiple futures. FCMs are based on causal cognitive maps and combine the benefits of both qualitative and quantitative analysis. Domain knowledge of multiple experts can be integrated into an FCM to present collective knowledge base.

This is a conceptual paper exploring use of fuzzy cognitive maps to create scenarios for wind energy deployment. The paper provides scenarios and a way to integrate them into technology roadmaps.

This paper aims to propose a new genetically evolved fuzzy cognitive mapping approach as a decision-making framework for analyzing the relationships between the drivers and strategies for green computing adoption.

A focus group discussion among stakeholders in the Philippines is used to establish the relationships between the drivers and strategies of green computing adoption.

The proposed approach significantly reduces the time complexity for developing the fuzzy cognitive maps and provides a basis for comprehensively clustering drivers and strategies that share similar characteristics.

This paper’s results provide insights into how the drivers and strategies of green computing adoption facilitate the intention of adopting stakeholders. Moreover, it provides a framework for analyzing structural relationships that exist between factors in a compliant manner.

To the best of the author’s knowledge, the paper is the first to analyze the drivers and strategies of green computing under a complex systems’ perspective. Moreover, this is the first study to offer lenses in a Philippine scenario.

This paper aims to explore the critical factors which affect the quality of preschool education service so that targeted and effective measures to improve service quality can be put forward.

Evidential theory is applied to aggregate experts’ knowledge, and a fuzzy cognitive map (FCM) model of preschool education service quality is established to further carry out a simulation for inference, thus figuring out the critical factors to improve service quality.

The simulation results show that the main body of supervision and environment of governments and policies are two critical factors affecting the quality of preschool education service. More emphasis should be put on these two aspects, and corresponding measures can be put forward so as to ensure the quality of preschool education service.

This paper proposes a new model based on FCM and evidential theory to study the factors affecting preschool education service quality.

The purpose of this paper is to propose an intelligent service recommendation model. The paper formulates the service adaptation process by using artificial intelligence techniques like Bayesian Network, fuzzy logic and rule based reasoning.

The authors formulate the service adaptation process by using artificial intelligence techniques like Bayesian Network, fuzzy logic and rule based reasoning. Bayesian Network is used to classify the incoming call (high priority call, low priority call and unknown calls), fuzzy linguistic variables and membership degrees to define the context situations, the rules for adopting the policies of implementing a service, fitness degree computation and service recommendation. In addition to this the paper proposes maximum to minimum priority based context attributes matching algorithm for rule selection based on fitness degree of rules. The context aware mobile is tested for library and class room scenario to exemplify the proposed service recommendation engine and demonstrate its effectiveness.

First, it was found that there was reduction in application searching time in different contexts. For example, if user enters into the library, the proposed mobile will be adapted to the library situation automatically by configuring its desktop and internal settings to facilitate the library services like book search, web link, silent mode and friends search. Second, the design of the recommendation engine, utilizing contextual parameters like Location (class room, college campus, house, etc.) Personal (age, name), Temporal (time, date), Physical (fall, normal), and schedule agendas, was found to be of importance.

Exploitation of hybrid fuzzy system, Bayesian Networks and the utility theory (usage history and context history) for modeling and implementation.

The purpose of the paper is to present the common use of the Delphi method, the analytical hierarchy process (AHP) and dynamic fuzzy cognitive maps for the development of a future scenario in correlation with climate warming.

A combination of the Delphi method, the analytical hierarchy process (AHP) and dynamic fuzzy cognitive maps was employed to construct a future scenario as a learning device.

Based on the data, the future is not going to be so bad but also not very attractive even if it is assumed that human consciousness about global warming issues will remain high. After a few years or so it will be possible to stop negative climate processes but not possible to fix the problem in the sense that the situation we have now can be substantially improved.

This paper is of interest for foresight practitioners and policy makers who want to employ a hybrid approach to scenario development.

The paper is the first to try to use a combination of different methods for scenario development related to climate change.

The aim of this study is to present a model for service supply chain performance evaluation (SSCPE) based on fuzzy cognitive mapping (FCM).

Despite the unique nature of services, which distinguishes them from goods, the performance evaluation of service supply chains (SSCs) has still not received adequate attention from researchers or practitioners. With the help of in-depth interviews conducted with insurance industry experts in Iran, the performance measures of SSCs are defined, and the relevance between the performance measures, which are based upon the knowledge of experts in this field, are presented as a fuzzy cognitive map.

By blocking the aggregate map of experts, a model for SSCPE is developed that discusses performance measures and metrics of strategic, tactical and operational SSC processes, such as service-providing management, service-enabling management, market management, cash-flow management, customer-relationship management, supplier-relationship management, knowledge- and information-flow management and risk management. Indexes of FCM models indicate that the supply chain service quality and strategy blocks have the most importance compared with others.

The paper develops an original SSCPE model that can be used as a basis for SSC improvement and develops an excellence model for SSC.

Because container terminals (CTs) are broker organizations, their interest has complex, various internal and external factors, the purpose of this study is to scrutiny and structure the factors affecting the interests of the CT.

In terms of purpose, this study is a developmental study that, are identified the factors related to the interests of the CT through studying the library and interviewing experts and then the degree of influence of each factor on each other by using the interview with experts of CT in Bushehr province are determined. Then, the fuzzy DEMATEL method is used to calculate the feedback loop of the indicators, and then the matrix obtained Fcmapper software and the fuzzy cognitive map (FCM) is drawn.

According to the FCM analysis, three important factors are centrality identified in terms of attracting capital, quality of service and efficiency. In addition, there are three factors, attracting capital, operator performance and law have high outdegree.

This research has identified 34 effective indicators of CT interests and evaluated their relationship with FCM. In the available researches, all these indicators measurement has not been evaluated together. Furthermore, fuzzy DEMATEL has been used to evaluate self-loops. Another contribution of this research is the introduction of a strategic route as a roadmap for CT managers in Bushehr province to decide on the interests of a CT.