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The grade of Fellow is conferred by the IEEE Board of Directors upon an IEEE member with an extraordinary record of accomplishments in any of the IEEE fields of interest. The Fellow grade recognizes extraordinary individual technical or educational accomplishments, in line with the IEEE Certificate of Incorporation that states that the purposes and objects of the Corporation are “to engage exclusively in scientific and educational activities.” An effort to review the Fellow elevation process was started by the IEEE Board of Directors back in 2020, culminating today in a proposal that drastically changes many aspects of the elevation process. Several concerning issues with this proposal were identified by multiple stakeholders, such as the 2021 Fellow Strategic Planning Subcommittee (FSPS) and several Societies and Technical Councils who expressed their strong objections to the proposal. This article provides an in-depth analysis of the proposal and concludes that the proposed changes derive from a misunderstanding of the current Fellow processes and are based on a misidentification of the root causes of the perceived shortcomings in the current practice. Furthermore, the proposed changes lack an evidence-based rationale for justifying making such drastic changes while the available evidence points to their ineffectiveness at addressing the stated goals. The objective of this article is to help IEEE members and decision makers better understand what is at stake and make informed decisions on a difficult and complex topic. [ABSTRACT FROM AUTHOR]

Human social relationships imply conforming to the norms, behaviors, and cultural values of the society, but also socialization of emotions, to learn how to interpret and show them. In multiagent systems, much progress has been made in the analysis and interpretation of both emotions and norms. Nonetheless, the relationship between emotions and norms has hardly been considered and most normative agents do not consider emotions, or vice-versa. In this article, we provide an overview of relevant aspects within the area of normative agents and emotional agents. First we focus on the concept of norm, the different types of norms, its life cycle and a review of multiagent normative systems. Second, we present the most relevant theories of emotions, the life cycle of an agent’s emotions, and how emotions have been included through computational models in multiagent systems. Next, we present an analysis of proposals that integrate emotions and norms in multiagent systems. From this analysis, four relationships are detected between norms and emotions, which we analyze in detail and discuss how these relationships have been tackled in the reviewed proposals. Finally, we present a proposal for an abstract architecture of a Normative Emotional Agent that covers these four norm-emotion relationships. [ABSTRACT FROM AUTHOR]

The article offers information on the upcoming papers of the journal which includes one on Decision Model for Information Technology Management, the other on Teaching-Learning-Based Optimization Algorithm and the third on Organizational Capital Decision Model for Knowledge- Intensive Organizations.

It has been widely suggested that a key goal of visualization systems is to assist decision making, but is this true? We conduct a critical investigation on whether the activity of decision making is indeed central to the visualization domain. By approaching decision making as a user task, we explore the degree to which decision tasks are evident in visualization research and user studies. Our analysis suggests that decision tasks are not commonly found in current visualization task taxonomies and that the visualization field has yet to leverage guidance from decision theory domains on how to study such tasks. We further found that the majority of visualizations addressing decision making were not evaluated based on their ability to assist decision tasks. Finally, to help expand the impact of visual analytics in organizational as well as casual decision making activities, we initiate a research agenda on how decision making assistance could be elevated throughout visualization research. [ABSTRACT FROM AUTHOR]

This column surveys selected papers in computational sustainability from the 30th AAAI Conference on Artificial Intelligence. [ABSTRACT FROM PUBLISHER]

This paper investigates a futuristic spectrum sharing paradigm for heterogeneous wireless networks with imperfect channels. In the heterogeneous networks, multiple wireless networks adopt different medium access control (MAC) protocols to share a common wireless spectrum and each network is unaware of the MACs of others. This paper aims to design a distributed deep reinforcement learning (DRL) based MAC protocol for a particular network, and the objective of this network is to achieve a global $\alpha$ α -fairness objective. In the conventional DRL framework, feedback/reward given to the agent is always correctly received, so that the agent can optimize its strategy based on the received reward. In our wireless application where the channels are noisy, the feedback/reward (i.e., the ACK packet) may be lost due to channel noise and interference. Without correct feedback, the agent (i.e., the network user) may fail to find a good solution. Moreover, in the distributed protocol, each agent makes decisions on its own. It is a challenge to guarantee that the multiple agents will make coherent decisions and work together to achieve the same objective, particularly in the face of imperfect feedback channels. To tackle the challenge, we put forth (i) a feedback recovery mechanism to recover missing feedback information, and (ii) a two-stage action selection mechanism to aid coherent decision making to reduce transmission collisions among the agents. Extensive simulation results demonstrate the effectiveness of these two mechanisms. Last but not least, we believe that the feedback recovery mechanism and the two-stage action selection mechanism can also be used in general distributed multi-agent reinforcement learning problems in which feedback information on rewards can be corrupted. [ABSTRACT FROM AUTHOR]

The papers in this special section provide a series of high quality papers which significantly contribute to new theories, applications, and algorithms about uncertain multicriteria decision making. It not only reports recent significant developments but also highlights potential, growing research directions, and future trends, which will benefit researchers in studying theories, applications, and algorithms about uncertain multicriteria/ multiobjective/multiattribute decision making. [ABSTRACT FROM AUTHOR]

The antagonistic behavior in the crowd usually exacerbates the seriousness of the situation in sudden riots, where the antagonistic emotional contagion and behavioral decision making play very important roles. However, the complex mechanism of antagonistic emotion influencing decision making, especially in the environment of sudden confrontation, has not yet been explored very clearly. In this paper, we propose an Emotional contagion-aware Deep reinforcement learning model for Antagonistic Crowd Simulation (ACSED). First, we build a group emotional contagion module based on the improved Susceptible Infected Susceptible (SIS) infection disease model, and estimate the emotional state of the group at each time step during the simulation. Then, the tendency of crowd antagonistic action is estimated based on Deep Q Network (DQN), where the agent learns the action autonomously, and leverages the mean field theory to quickly calculate the influence of other surrounding individuals on the central one. Finally, the rationality of the predicted actions by DQN is further analyzed in combination with group emotion, and the final action of the agent is determined. The proposed method in this paper is verified through several experiments with different settings. We can conclude antagonistic emotions play a critical role in the decision making of the crowd through influencing the individual behavior in the riot scenario, where individual behaviors are primarily driven by emotions and goals, rather than common rules. The experiment results also prove that the antagonistic emotion has a vital impact on the group combat, and positive emotional states are more conducive to combat. Moreover, by comparing the simulation results with real scenes, the feasibility of our method is further confirmed, which can provide good reference to formulate battle plans and improve the win rate of righteous groups in a variety of situations. [ABSTRACT FROM AUTHOR]

Practitioners of multi-objective optimization currently lack open tools that provide decision support through knowledge discovery. There exist many software platforms for multi-objective optimization, but they often fall short of implementing methods for rigorous post-optimality analysis and knowledge discovery from the generated solutions. This paper presents Mimer, a multi-criteria decision support tool for solution exploration, preference elicitation, knowledge discovery, and knowledge visualization. Mimer is openly available as a web-based tool and uses state-of-the-art web-technologies based on WebAssembly to perform heavy computations on the client-side. Its features include multiple linked visualizations and input methods that enable the decision maker to interact with the solutions, knowledge discovery through interactive data mining and graph-based knowledge visualization. It also includes a complete Python programming interface for advanced data manipulation tasks that may be too specific for the graphical interface. Mimer is evaluated through a user study in which the participants are asked to perform representative tasks simulating practical analysis and decision making. The participants also complete a questionnaire about their experience and the features available in Mimer. The survey indicates that participants find Mimer useful for decision support. The participants also offered suggestions for enhancing some features and implementing new features to extend the capabilities of the tool. [ABSTRACT FROM AUTHOR]

Systems that are based on recursive Bayesian updates for classification limit the cost of evidence collection through certain stopping/termination criteria and accordingly enforce decision making. Conventionally, two termination criteria based on pre-defined thresholds over (i) the maximum of the state posterior distribution; and (ii) the state posterior uncertainty are commonly used. In this paper, we propose a geometric interpretation over the state posterior progression and accordingly we provide a point-by-point analysis over the disadvantages of using such conventional termination criteria. For example, through the proposed geometric interpretation we show that confidence thresholds defined over maximum of the state posteriors suffer from stiffness that results in unnecessary evidence collection whereas uncertainty based thresholding methods are fragile to number of categories and terminate prematurely if some state candidates are already discovered to be unfavorable. Moreover, both types of termination methods neglect the evolution of posterior updates. We then propose a new stopping/termination criterion with a geometrical insight to overcome the limitations of these conventional methods and provide a comparison in terms of decision accuracy and speed. We validate our claims using simulations and using real experimental data obtained through a brain computer interfaced typing system. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to develop an integrated multiobjective and multiperiod mathematical model for sustainable supplier selection and order allocation of an automotive manufacturer. In spite of the vast quantity of research on this area, integrated consideration of risk and inflation in sustainable supplier selection and order allocation problem is less investigated. To fill this gap, this paper aims to develop a multiobjective sustainable supplier selection and order allocation model considering risk and inflation. Particularly, six objective functions of total cost, economic score, environmental score, social score, inflation rate, and risk level along with related constraints are optimized in the model. The developed model is solved using weighted sum approach and augmented ε-constraint method. In addition, a sensitivity analysis has been conducted. The results provide the best sustainable suppliers and the optimum quantity of products to be purchase from each supplier in each period. [ABSTRACT FROM AUTHOR]

The implementation of autonomous driving is inseparable from developing intelligent driving decision-making models, which are facing high scene complexity, poor decision-making coupling, and the inability to guarantee decision-making safety. This paper starts with the priority and logic of lane change and car-following decision-making, considering driving efficiency, safety, and comfort, then constructs a double-layer decision-making model. This paper uses two deep reinforcement learning algorithms for the upper and lower layers to process large-scale mixed state space and ensure the composite action output of lane-changing decisions and car-following decisions. In the upper layer model, we use the D3QN algorithm to distinguish the potential value of the environment and the value of selecting lane-changing actions when making lane-changing decisions. Different from the traditional mechanisms that only use negative rewards, the lane changing benefit function and dangerous action shielding mechanism are used to eliminate collisions. DDPG algorithm is adopted in the lower layer model to process car-following decisions and output continuous vehicle speed control. Besides, coupled training is taken for the two algorithms to improve the coordination of the double-layer model. This paper selected mixed standard driving cycle conditions to build a highly complex training environment and used NGSIM data to reconstruct scenes to test our model. Simulations in SUMO are presented that the double-layer model can increase the driving speed of the original data by 23.99%, which has higher effectiveness than other models. [ABSTRACT FROM AUTHOR]

Actual condition-based maintenance for traction power supply equipment (TPSE) is almost based on completely observable equipment state. However, it is unpractical to accurately reveal the equipment state due to the inescapably uncertainty of state assessment. In order to optimize the maintenance of TPSE, a maintenance model based on partially observable Markov decision process is proposed in this paper. Firstly, the degradation process of the TPSE is described by a four-state Markov process, and the state residence time and its transition probability of the equipment are obtained by equaling fault times in the statistical period. Then, the imperfect maintenance is considered in this paper. And the failure risk of the TPSE after maintenance is quantified for optimizing both the economic cost and the reliability of maintenance strategy. Finally, the practical fault record data of 27.5 kV vacuum circuit breakers for a traction power supply system (TPSS) are used to verify the proposed model. The results show that the maintenance model can provide guidance on decision-making for the maintenance under uncertainty, and the determination of maintenance schemes to optimize both TPSE reliability and operational cost. [ABSTRACT FROM AUTHOR]

Extreme events can cause severe power system damage. Resilience-driven operation of networked microgrids (MGs) has been heavily studied in literature. There is, though, little research considering the influence of resilience on decision making for planning. In this paper, a three-level model is suggested to solve the optimal sizing problem of networked MGs considering both resilience and cost. In the first level, a meta-heuristic technique based on an adaptive genetic algorithm (AGA) is utilized to tackle the normal sizing problem, while a time-coupled AC OPF is utilized to capture stability properties for accurate decision-making. The second and third levels are combined as a defender-attacker-defender model. In the former, the suggested AGA is utilized to generate attacking plans capturing load profile uncertainty and contingencies for load shedding maximization, while a multi-objective optimization problem is suggested for the latter to obtain a trade-off between cost and resilience. Simulations considering meshed networks and load distinction into critical and non-critical are developed to demonstrate algorithm effectiveness on capturing resilience at the planning stage and optimally sizing multiple parameters. The results indicate that higher resilience levels lead to higher investment cost, while sizing networked MGs leads to decreased investment in comparison with standalone MGs sizing. [ABSTRACT FROM AUTHOR]

Nowadays, the increased amount of users’ devices produce huge volumes of data that should be efficiently managed by modern applications. Streams are adopted to deliver data that, usually, are stored into a number of partitions. Splitting the data offers a lot of advantages as applications can process them in parallel, thus, they increase the speed of processing. Progressive analytics are also adopted to deliver partial responses, during processing, thus, saving time in the execution of applications. Data exploration and analytics queries are very significant for future applications. Usually, such queries demand for an ordered set of objects as a response and require intelligent predictive schemes to deliver the responses on top of the partial results retrieved by the distributed data partitions. A finite set of query processors are adopted to produce these partial results. Processors are placed in front of each partition and report progressive analytics to a central entity. In this paper, we envision the query controller (QC) as the central entity that collects progressive analytics and return the final response to users/applications. The QC receives partial ordered sets of objects and aggregates them to derive the final outcome. We focus on a QC that applies time-optimized techniques and aggregation operators to deliver every response, i.e., ordered sets, over streams of partial ordered subsets. We perform a comprehensive performance assessment with synthetic data and report on the performance of the QC. Our experimental evaluation reveals the pros and cons of the proposed model and a comparison assessment places this paper in the respective literature. [ABSTRACT FROM AUTHOR]

In group decision making, it is inevitable that the individual decision maker’s subjectivity is involved, which causes difficulty in reaching a group decision. One of the difficulties is to aggregate a small set of expert opinions with the individual subjectivity or uncertainty modeled with probability theory. This difficult problem is called probability distribution function aggregation (DFA). This paper presents a simple and efficient approach to the DFA problem. The main idea of the proposed approach is that the DFA problem is modeled as a nonlinear function of a set of probability distribution functions, and then a linear feedback iteration scheme is proposed to solve the nonlinear function, leading to a group judgment or decision. Illustration of this new approach is given by a well-known DFA example which was solved with the Delphi method. The DFA problem is a part of the group decision problem. Therefore, the proposed algorithm is also useful to the decision making problem in general. Another contribution of the this paper is the proposed notation of systematically representing the imprecise group decision problem with the classification of imprecise information into three classes, namely incomplete information, vague information, and uncertain information. The particular DFA problem dealt with in this paper is then characterized with this general notation. [ABSTRACT FROM AUTHOR]

This paper analyzes the literature to identify ideas which may be applied to decentralized realtime railway traffic management. This system represents a new way for dealing with railway traffic perturbations in absence of a central decision maker. Specifically, we are interested in identifying techniques that may constitute suitable automatic mechanisms for the emergence of an effective system behavior. In this literature review, we discuss the possibility of exploiting the existing research works on other transport modes. The analysis of these works makes it clear that real-time railway traffic management is very peculiar. Hence, we consider different approaches: hierarchical self-organization, task allocation, reinforcement learning, consensus, auction and coopetition techniques. Some promising possibilities emerge, which we analyze proposing ideas for modeling decentralized real-time railway traffic management. [ABSTRACT FROM AUTHOR]

This paper presents an approach that generates the overall trajectory of mobile manipulators for a complex mission consisting of several sub-tasks. Parametric dynamic movement primitives (PDMPs) can quickly generalize the online motion of robot manipulation by learning multiple demonstrations in offline. However, regarding complex missions consisting of multiple sub-tasks, a large number of demonstrations are required for full generalization, which is impractical. In this paper, we propose a framework that reduces the number of demonstrations for a complex mission. In the proposed method, complex demonstrations are segmented into multiple unit motions representing sub-tasks, and one PDMP is formed per each segment, resulting in multiple PDMPs. The phase decision process determines which sub-task and associated PDMPs to be executed online, allowing multiple PDMPs to be autonomously configured within an integrated framework. In order to generalize the execution time and regional goal in each phase, the Gaussian process regression (GPR) is applied. Simulation results from two different scenarios confirm that the proposed framework not only effectively reduces the number of demonstrations but also improves generalization performance. The actual experiments also demonstrate that the mobile manipulators effectively perform complex missions through the proposed framework. Note to Practitioners—This paper presents an approach of learning from demonstration (LfD) to generalize complex movements of robots. Parametric dynamic movement primitives (PDMPs) compute styles of movements from multiple demonstrations. However, the complexity of the PDMP increases as the mission involves more sub-tasks. In this paper, we resolve this issue by segmenting the complex mission into multiple sub-tasks and configuring multiple PDMPs. This work effectively reduces the number of required demonstrations for PDMPs, moderates the complexity of the algorithm. Also, the proposed approach allows flexible sub-task sequencing. It enables the mission in an unlearned sequence or a new combination of sub-tasks. The proposed approach is validated in both simulation and experimental results. Our approach is applicable for complex missions whose sub-tasks are clearly identified [ABSTRACT FROM AUTHOR]

The safety and efficiency of the merging zone is particularly important for traffic networks. Although autonomous vehicle improves the safety and efficiency from vehicle view, traffic controlling in merging zone mostly focus on improving efficiency from roadside view. Lacking of detailed driving recommendation, it ignores the safety of merging zone where commercial vehicle pose a high collision risk in real traffic. This paper proposes a roadside decision-making methodology to simultaneously improve the safety and efficiency of merging zone. We have built two modules, namely assessment and decision-making. Assessment module takes advantage of Bayesian inference to evaluate dynamic collision risk. Decision-making module based on deep reinforcement learning recommends the actions to commercial vehicles by roadside unit. A series of typical simulation tests show that our method increases the TTC of commercial vehicles by an average of 62.7%. In the free flow, the overall travel time of vehicles in the merging zone is reduced by 11.68%. Most notably, when congestion occurred, the average jam length is reduced by 59.68% on the premise of safety. Moreover, the average accuracy of the roadside decision-making method on the evaluation metrics of TTC, travel time, and jam length are 93.73%, 91.65%, and 94.45%, respectively. The experimental results show that the roadside decision-making methodology simultaneously improves safety and efficiency, and it dynamically adapts free and congested traffic flow. [ABSTRACT FROM AUTHOR]

In this paper, we propose a human-robot interaction (HRI) framework to reduce the vulnerability of an unmanned ground vehicle (UGV) platoon under cyber attacks. An observer-based autonomous resilient control strategy is first designed to mitigate the effects of vehicle-to-vehicle (V2V) cyber attacks. Rigorous proof of the safety conditions of the platoon under V2V cyber attacks is derived. Next, to facilitate human supervision in emergencies and uncertainties, a decision-making aid system is developed, which consists of an anomaly reporting system (ARS), a trust-based information management system (TIMS), and a graphical user interface (GUI). ARS is designed to detect the abnormality of the neighbor UGVs based on the model-based residual generation and analysis. TIMS is developed to rule out information with low trustworthiness, which is fulfilled by a Bayesian-based information pre-processor followed by an enhanced median filter with trustworthiness regulation. It is rigorously proved that TIMS can recover the tempered information under the mild cyber attack scenario autonomously. To deal with unexpected severe vehicle-to-cloud (V2C) cyber attacks, human observations can also be leveraged to bolster the resilience of the TIMS. Representative simulation and humans-in-the-loop experiment (fulfilled by the designed GUI) demonstrate that the proposed framework can effectively guide human operators when working with UGV platoons under cyber attacks. On average, compared to a multi-screen surveillance benchmark approach, the proposed framework can achieve 78.4% decrements of platoon vulnerability and 39.0% decrements in human workload. [ABSTRACT FROM AUTHOR]

Age of Information (AoI) has become an important metric to evaluate the freshness of information, and studies of minimizing AoI in wireless networks have drawn extensive attention. In mobile edge networks, changes in critical levels for distinct information is important for users’ decision making, especially when merely partial observations are available. However, existing research has not yet addressed this issue, which is the subject of this paper. To address this issue, we first establish a system model, in which the information freshness is quantified by changes in its critical levels. We formulate Age-of-Critical-Information (AoCI) minimization as an optimization problem, with the purpose of minimizing the average relative AoCI of mobile clients to help them make timely decisions. Then, we propose an information-aware heuristic algorithm that can reach optimal performance with full obsevations in an offline manner. For online scheduling, an imitation learning-based scheduling approach is designed to choose update preferences for mobile clients under partial observations, where policies obtained by the above heuristic algorithm are utilized for expert policies. Finally, we demonstrate the superiority of our designed algorithm from both theoretical and experimental perspectives. [ABSTRACT FROM AUTHOR]

Power transformer is a critical asset in electrical transmission and distribution networks that need to be carefully monitored during its entire operational life. Considering the fact that a significant number of worldwide in-service power transformers have approached the end of their expected operational life, utilities have adopted various transformer condition-based maintenance techniques to avoid any potential catastrophic failure to the equipment. The extent of transformer insulation system ageing can be quantified through measuring several diagnostic indicators such as interfacial tension number of the insulating oil which has a strong correlation with the number of transformer operating years. Moisture and furanic compounds generated due to paper insulation degradation are indicators for solid insulation ageing. This paper introduces a new adaptive neuro fuzzy logic model to estimate the life of mineral oil-filled power transformers based on the values of insulating oil interfacial tension number, furan content in oil and the moisture content within the cellulose insulation. Also, an integrated asset management decision model is proposed. Results of the proposed model are validated against practical data collected from utility and industry mineral oil-filled power transformers of different ratings, designs, operating conditions and lifespans. [ABSTRACT FROM AUTHOR]

In this paper, we present the cooperative driving system developed by the Chalmers car team for the grand cooperative driving challenge 2016. This paper gives an overview of the system architecture and describes in detail the communication, signal processing, and decision-making sub-systems. Experimental results demonstrate the system’s performance and operation according to the rules and requirements of the competition. [ABSTRACT FROM PUBLISHER]

A good quality stability model is a key factor for accurate power system operations.Inaccurate parameters of the stability models affect the decision making which paves the way for serious consequences. Thus, it is necessary to calibrate the stability model parameters in a regular manner. There are several calibration methods in the literature which are based on simultaneous estimation of the parameters and states. However, not all of the model parameters are well estimable simultaneously. Simultaneous estimation of parameters with high collinearity may result in biased calibration results. In this paper, the trajectory sensitivity method is used to detect the sensitive parameters and construct the sensitivity matrix. Then, parameters with high linear dependency are identified using the sensitivity matrix. It is shown that, despite the high sensitivity of a parameter, its estimability degrades as the collinearity with other parameters increase. In this paper an identifiability analysis that detects the collinearity among the sensitive parameters is proposed. The proposed method is validated using WSCC 9-Bus System. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new state representation method, called encoding sum and concatenation (ESC), to describe the environment observation for decision-making in autonomous driving. Unlike existing state representation methods, ESC is applicable to the situation where the number of surrounding vehicles is variable and eliminates the need for manually pre-designed sorting rules, leading to higher representation ability and generality. The proposed ESC method introduces a feature neural network (NN) to encode the real-valued feature of each surrounding vehicle into an encoding vector, and then adds these vectors up to obtain the representation vector of the set of surrounding vehicles. Then, a fixed-dimensional and permutation-invariance state representation can be obtained by concatenating the set representation with other variables, such as indicators of the ego vehicle and road. By introducing the sum-of-power mapping, this paper has further proved that the injectivity of the ESC state representation can be guaranteed if the output dimension of the feature NN is greater than the number of variables of all surrounding vehicles. This means that the ESC representation can be used to describe the environment and taken as the inputs of learning-based policy functions. Experiments demonstrate that compared with the fixed-permutation representation method, the policy learning accuracy based on ESC representation is improved by 62.2%. [ABSTRACT FROM AUTHOR]

Decision support is a vital function in electronic commerce (e-commerce). The purpose of this paper is to construct a review-based decision support model for items comparison in e-commerce. The proposed model uses probability multivalued neutrosophic linguistic numbers (PMVNLNs) to characterize online reviews. It overcomes the limitation of existing models by considering neutral information and hesitancy in text reviews. The fuzzy characterization of reviews (i.e., PMVNLN) can reflect similarities and differences in positive (negative) information. In addition, the model considers consumers’ bounded rational behaviors by combining the regret theory with an outranking method. We empirically compare the proposed model with models in http://PConline.com and four existing models with data from http://PConline.com. The performance of these models in terms of accuracy is measured by the total relative difference metric. Results indicate the good performance of the proposed model. Our model is a promising option for e-commerce to provide consumers with good decision support service. [ABSTRACT FROM AUTHOR]

While formal and rigorous procedures are adopted to ensure the reliability of measurement results, the decision-making activities based on the information returned by measurement are often poorly structured and based on vague assumptions and reasoning, thus negatively impacting the reliability of the achieved conclusion. To overcome this problem, a structured multicriteria decision-making approach based on the analytic hierarchy process was recently proposed in the literature to properly support decision-making activities by exploiting the information provided by measurement results related to heterogeneous properties and its application was demonstrated in a case study. Using the same case study, this paper analyses the sensitivity of the obtained conclusion to various uncertainty sources that are embedded in that approach. In particular, the impact of uncertainties on both measurement and pairwise comparisons of the criteria is analyzed and their combined effect is evaluated by considering a worst case situation. Moreover, the effect of the formula adopted to aggregate the information returned by the measurement of heterogeneous properties on the final decision is considered. The performed analysis contributes to support a disciplined and methodologically sound implementation of decision-making activities, significantly improving both the repeatability and reproducibility of the whole evaluation process, as well as the confidence in the achieved conclusions. [ABSTRACT FROM AUTHOR]

With growing environmental and sustainability-related concerns, recovery optimization of mechanical products has been gaining increased exposure. It facilitates environmental sustainability through the improvement in the life-cycle material efficiency and reduction in environmental impact with disassembly sequence planning, component reuse, and material recycling. Traditional product recovery separates end-of-life (EOL) products into components and selects EOL options of components. However, there are many practical cases in which the recovery of a set of subassemblies and components leads to better net revenue than that of a complete set of single components. This paper proposes to model and optimize hybrid disassembly and EOL operations of product recovery to maximize the recovery profit and minimize the environmental impact. Flexible process planning of hybrid disassembly determines a disassembly level by identifying the reusability of subassemblies and disassembly sequences mixed with subassemblies and components. Optimal EOL decisions for each subassembly and component are investigated such that the economic and environmental objectives can be achieved. Finally, a case study is described to illustrate the proposed method and the influence on decision variables of the tradeoff between the recovery profit and environmental impact is discussed. Note to Practitioners—This paper deals with the process planning and EOL decision-making problem of product recovery. Based on hybrid disassembly, this paper proposes a flexible process planning and EOL decision-making method for product recovery. Flexible process planning of hybrid disassembly determines a disassembly level by identifying the reusability of subassemblies and disassembly sequences mixed with subassemblies and components. Optimal EOL decisions for each subassembly and component are investigated such that the economic and environmental objectives can be achieved. The goal of this paper is to model and optimize hybrid disassembly and EOL operations of product recovery to maximize the recovery profit and minimize the environmental impact. The results demonstrate that the proposed method can leads to small environmental impact and low cost. Subassemblies and components with high reliability and expensive price are suggested to be destined for reuse. Minimizing transportation distances is more effective to reduce product recovery cost. Such results can help decision makers to perform better judgments when a disassembly process of an EOL product is executed. [ABSTRACT FROM AUTHOR]

Recommender systems are popular for personalization in online communities. Users, items, and other affiliated information such as tags, item genres, and user friends of an online community form a heterogenous information network. User profiling is the foundation of personalized recommender systems. It provides the basis to discover knowledge about an individual user’s interests to items. Typically, users are profiled with their direct explicit or implicit ratings, which ignored the inter-connections among users, items, and other entity nodes of the information network. This paper proposes a deep reinforcement user profiling approach for recommender systems. The user profiling process is framed as a sequential decision making problem which can be solved with a Reinforcement Learning (RL) agent. The RL agent interacts with the external heterogenous information network environment and learns a decision making policy network to decide whether there is an interest or preference path between a user and an unobserved item. To effectively train the RL agent, this paper proposes a multi-iteration training process to combine both expert and data-specific knowledge to profile users, generate meta-paths, and make recommendations. The effectiveness of the proposed approaches is demonstrated in experiments conducted on three datasets. [ABSTRACT FROM AUTHOR]

The famous Hirsch index has been introduced just ca. ten years ago. Despite that, it is already widely used in many decision-making tasks, like in evaluation of individual scientists, research grant allocation, or even production planning. It is known that the $h$-index is related to the discrete Sugeno integral and the Ky Fan metric introduced in the 1940s. The aim of this paper is to propose a few modifications of this index as well as other fuzzy integrals—also on bounded chains—that lead to better discrimination of some types of data that are to be aggregated. All of the suggested compensation methods try to retain the simplicity of the original measure. [ABSTRACT FROM PUBLISHER]

Role negotiation is a pivotal step in the role-based collaboration (RBC) process. There are many factors to be considered when decision makers set up criteria to evaluate group performance and agents’ abilities. In this paper, we investigate the applications of three traditional multicriteria decision making methods into role negotiation of RBC, i.e., simple additive weighting, multiplication exponent weighting, and weighted distance. The goal is to acquire ideal and satisfactory methods to set up the evaluation criteria for the role negotiation task. The main contributions of this paper include: 1) a method of generating the matrix of agent qualifications, with a discrete, usually, limited, and finite set of prespecified attributes and values; 2) the upper and lower thresholds of a role’s ability requirements, which are critical parameters for a workable team and its performance; and 3) the potential influences of specialist agents and generalist agents on the group performance. The proposed methods are verified by simulation experiments. The simulation results show that the proposed methods are reasonable, feasible, and practical. [ABSTRACT FROM AUTHOR]

It is an important topic to organize a team efficiently and keep it in a good state. In most cases, administrators try to avoid critical members. With this requirement, administrators prefer that their team members are able to be good at many things and expert in one (GMEO). However, that too many people are “good at many things” is definitely a waste. Role-based collaboration and its environments—classes, agents, roles, groups, and objects (E-CARGO) model are a good means to provide modeling and solutions to such a challenging problem. This paper formalizes the problem of GMEO into GMEO-1 (the fundamental form of GMEO) with the support of E-CARGO, clarifies two different forms of GMEO-1 and provides two highly practical solutions. The proposed solutions are verified by comparing with initial solutions using a linear programming solver, i.e., the IBM ILOG CPLEX Optimization Platform. The contributions of this paper are a thorough investigation of GMEO-1 that has no exact solutions even for a small group (e.g., ten people) and is normally managed by highly qualified administrators. The proposed solutions provide digital results with algorithms that form a solid foundation for decision making in dealing with similar issues. [ABSTRACT FROM AUTHOR]

There is an on-going need for faster, more accurate, and easier to implement threat identification systems for concealed electronics, to thwart terrorism and espionage attempts. Common electronics are used in the design of improvised explosive devices (IEDs), targeting military, and civilian populations, while concealed recording devices steal proprietary/confidential data, compromising both government and industrial resources. This paper discusses a novel, nonintrusive, repeatable, reliable, expandable, and simple-to-implement detection and identification (D&I) system for identifying threats by using unintended radiated emissions (URE). This paper differs from current URE proposals, by avoiding energy radiation. The proposed process also addresses big data problems involved in capturing and building databases of URE characteristics. Issue interpretation is utilized with URE data to distinguish between threat/nonthreat devices using multiple criteria decision analysis and other decision-making techniques to determine model and mode of hidden devices. System optimization, verified by testing, is used to improve the speed and accuracy of the identification algorithm. The system is developed with data from 130 devices and validated with 33 separate devices, which are representative of IED and espionage threats. Due to slow progress in the URE D&I field, the properties and potential of a more effective D&I system, compared to current methods, will be of interest to the explosive ordnance disposal, security services, electronic systems manufacturing, automated inventory, mobile application development communities, and potentially others as well. For a field that is not well documented, this paper provides a clear, concise, and detailed model for future research and applications of URE D&I. [ABSTRACT FROM AUTHOR]

Security is an indispensable part of smart grid (SG). Key establishment protocols play a vital role in fulfilling the security requirements in SG. In recent years, many key establishment schemes have been proposed for SG. However, only three of them offer the strong smart meter anonymity. In 2015, Tsai and Lo presented an anonymous key establishment scheme to be employed in SG. Recently, Odelu et al. have demonstrated that Tsai and Lo's scheme suffers from the ephemeral secret leakage attack. Furthermore, another scheme has been proposed recently by He et al. that has improved the efficiency of Tsai and Lo's scheme. However, besides its merits, we indicate that He et al.'s scheme suffers from both known session-specific temporary information attack and private key leakage. Additionally, these three anonymous schemes have the key escrow problem since the trusted anchor is aware of entities’ private key. Hence, to cover the existing issues, in this paper we propose a key establishment scheme that not only is free from the security challenges of the previous anonymous schemes and key escrow problem, but also is efficient in terms of both computational and communication costs. Finally, yet importantly, we present the performance analysis of our proposed scheme on a state-of-the-art advanced RISC machine (ARM) chip, namely NXP LPC1788, as a suitable hardware for the smart meters. [ABSTRACT FROM AUTHOR]

The articles in this special section focus on situation awareness in intelligent human-computer interaction for critical decision making (HCI). HCI is recognized as an ctive field that focuses on the various interactions of human with machines. The HCI has been widely applied in multiple domains, such as artificial intelligence, computer vision, image and multimedia analysis, and cognitive and behavioral sciences. The objective of the HCI is to make the computer smart via receiving enough knowledge about the environment where it is deployed and reduce the human intervention aspect toward decision making. This enables development of high-end computers that are context aware and smart in making decisions with reference to the context. Situation awareness of an intelligent HCI will decide the success and application of the solution across the real world environment. The aim of this special issue is to provide a platform on the topic of situation awareness in intelligent HCI for time critical decision making. [ABSTRACT FROM AUTHOR]

We address the problem of merging probabilistic knowledge in a centralized (nonflat) multiple participant system. Each participant independently reports a known distribution of a common quantity of interest to an extra participant, called the deliberator. The deliberator’s task is the optimal design of a compromise distribution, which seeks to minimize the expected loss imposed on each of the participants. We formulate this task as one of Bayesian decision-making, and solve it using fully probabilistic design. Solutions are currently available for discrete distributions only. In this paper, we provide two novel contributions. First, the deliberator’s unknown distribution is parametric on a continuous support. Second, the scheme adopts a hierarchical model for the deliberator, optimizing the deliberator’s parameter prior. This allows quantification of the deliberator’s uncertainty in their model choice. We formulate conditions under which a conjugate design is achieved, and provide an example where the design is analytically tractable. Extensive simulation-based evidence is reported, pointing to the significantly enhanced performance of our framework over logarithmic pools. [ABSTRACT FROM PUBLISHER]

In this paper, a novel multi-agent decision making architecture based on dual’s dual problem formulation is designed for economic operation. The benefits of such architecture include its limited information exchange among autonomous entities and the ease of implementation. Existing ac optimal power flow (OPF) software packages can be easily incorporated. The proposed decision making is based on dual’s dual problem formulation, which is the first time seen in the literature to tackle an OPF problem. Subgradient-based iterative solving procedure results in a multi-agent decision making architecture. Convergence property of the algorithm is analyzed. The application scope of the proposed method is also discussed to identify challenges for meshed networks. Case studies are given to demonstrate the feasibility of the proposed method in providing approximate solutions to IEEE 14-bus system ac OPF. Implementing the proposed decision making architecture in real world is also demonstrated in a dynamic simulation platform. [ABSTRACT FROM PUBLISHER]

This paper describes the results obtained for the affective engagement of students with systems thinking (ST). In prior work, the authors have developed and validated a questionnaire instrument for measuring affective engagement of undergraduate engineering students with ST. This paper presents results obtained when the questionnaire was used with undergraduate students. Two surveys with different versions of the questionnaire, one using positive grammar questions only and the other using a mix of positive and negative constructs, were used to measure the students’ engagement with ST and its relationship with gender, age, and work experience. Each questionnaire version was applied to a different sample, the first, 186 participants, completed the positive grammar version, and, the second group of 163 completed the mixed version. The results show that participants in both studies valued ST in each of the three dimensions of the ST construct. Statistical tests confirmed no significant gender differences in either study. Student engagement with the practical dimension of ST was shown to vary, with statistical significance, with groups of age, years of work experience, and country of the university. [ABSTRACT FROM AUTHOR]

This paper explores the barriers which hamper the utilization and the routinization of product lifecycle management (PLM) practices in manufacturing organizations. The paper aims to confirm the barriers and their rank in order to improve the PLM institutionalization status in firms. Core barriers have been explored in an automotive firm by applying multicriteria decision-making techniques. Further, the significance of these barriers were corroborated with the executives in the broader PLM domain through a t-test. This study confirmes the “lack of advanced IT infrastructure,” “lack of integration and interoperability,” and “training on the PLM systems” as the top three barriers to PLM utilization and routinization in manufacturing firms in India. [ABSTRACT FROM AUTHOR]

In the context of project management, the attention given to project portfolio management has increased in recent years. The use of mathematical programming for portfolio management is also on the rise, because it integrates the project interactions with the multiple objectives of portfolio management into a single model. Among the possible objectives, recent studies have paid special attention to the emerging objective of future preparedness, which has not yet been incorporated into the existing mathematical models. This paper presents a mixed-integer nonlinear optimization model for portfolio selection that considers four main performance measures for project management, namely, value maximization, strategic alignment, balance, and future preparedness. Given the importance of the last measure, the purpose of this paper is to provide a more complete model that provides the marginal contribution and the best combination of projects according to the needs of the company. The model is tested using real data from two companies, one in Brazil and one in Canada, and the results obtained are coherent with their respective practices. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC), as an emerging and prospective mobile computing paradigm, allows a content provider to serve its users by allocating their mobile devices to nearby edge servers to lower the latency in the delivery of its content to those mobile services. From the content provider's perspective, a cost-effective mobile device allocation (MDA) aims to allocate maximum mobile devices to minimum edge servers. However, the allocation of excessive mobile devices to an edge server may result in severe communication interference and consequently, impact mobile devices’ data rates. Sometimes, not all the mobile devices can be allocated to edge servers and thus have to retrieve content from the remote cloud through base stations with high latency. The connection between these mobile devices and base stations also incur communication interference. In this paper, we formally model this Interference-aware mobile edge device allocation (I-MEDA) problem, and propose a game-theoretic based approach named I-MEDAGame to formulate the I-MEDA problem as an I-MEDA game. In the I-MEDA game, allocation decisions are made for individual mobile devices in parallel to alleviate the need for centralized optimization. Our theoretical analysis of I-MEDAGame shows that it admits at least one Nash equilibrium. To solve the I-MEDA problem, I-MEDAGame employs a novel decentralized algorithm to find the Nash equilibrium of the IMEDA game. The performance of I-MEDAGame is theoretically analyzed and experimentally evaluated. The results show that I-MEDAGame can solve the I-MEDA problem effectively and efficiently, outperforming four representative approaches significantly. [ABSTRACT FROM AUTHOR]

Information about several papers discussed at the International Symposium on Ethics in Engineering, Science and Technology (ETHICS 2014) held on May 23-24, 2014 and organized by the Institute of Electrical and Electronics Engineers (IEEE), is presented. Topics of the papers include technologies that interact with the brain, the technologies being used as moral proxies for medical decision making and Internet vulnerabilities. Another paper talks about the teaching of engineering ethics.

This paper proposes a noncooperative game-based planning method for microgrid (MG) considering the interconnection between developing and developed privately-owned clustered MGs on an island. A noncooperative game with imperfect information is designed to regulate the decision-making in planning and operating strategies for rational MG owners. The optimization models for both developing and developed MGs are presented. Different levels of maturity of MG development are modeled by diverse objective functions and constraints, while the data privacy of privately-owned MGs is preserved through imperfect information and prior beliefs. To tackle the seasonality of renewable energy and load on an island, hydrogen storage is considered in the developing MG on the island. Diagonalization and realization are incorporated to develop an effective solving methodology in searching the mixed strategy equilibrium for each MG. The effectiveness of the proposed method is verified using comprehensive case studies. [ABSTRACT FROM AUTHOR]

In sequential decision-making, imitation learning (IL) trains a policy efficiently by mimicking expert demonstrations. Various imitation methods were proposed and empirically evaluated, meanwhile, their theoretical understandings need further studies, among which the compounding error in long-horizon decisions is a major issue. In this paper, we first analyze the value gap between the expert policy and imitated policies by two imitation methods, behavioral cloning (BC) and generative adversarial imitation. The results support that generative adversarial imitation can reduce the compounding error compared to BC. Furthermore, we establish the lower bounds of IL under two settings, suggesting the significance of environment interactions in IL. By considering the environment transition model as a dual agent, IL can also be used to learn the environment model. Therefore, based on the bounds of imitating policies, we further analyze the performance of imitating environments. The results show that environment models can be more effectively imitated by generative adversarial imitation than BC. Particularly, we obtain a policy evaluation error that is linear with the effective planning horizon w.r.t. the model bias, suggesting a novel application of adversarial imitation for model-based reinforcement learning (MBRL). We hope these results could inspire future advances in IL and MBRL. [ABSTRACT FROM AUTHOR]

The IEEE Transactions on Automation Science and Engineering (T-ASE) wishes to thank the 1649 reviewers over the past year who have performed an essential role in maintaining the quality of this publication. T-ASE strives for the 90–90 standard, i.e., 90% of articles should be reviewed within 90 days of submission. The review process starts when the Editor-in-Chief assigns the paper to an Editor, who assigns it to an Associate Editor (AE). The AE then obtains confirmation from three to five reviewers, carefully studies the results, and makes a recommendation to the Editor, who then makes a decision that is reported back to the lead author. This review process is very demanding, especially under time constraints. We have summarized the process under “Guidelines for T-ASE Editors and AEs for Handling Papers” on our website. I am pleased to report that submissions have steadily increased (over 1300 in 2021) and our impact factor is now 6.636, a 30.55% increase over the past year. As we strive to increase the quality and impact of the journal, we rely on the hard work of our reviewers and members of the Editorial Board. To those dedicated volunteers, and to anyone who may have been inadvertently omitted from the following list, we extend our sincere appreciation. [ABSTRACT FROM AUTHOR]

The Industry 4.0 (I4.0) is a paradigm settled down by the introduction of the Internet of things (IoT) into the production and manufacturing environment. I4.0 promotes the connection of physical items such as sensors, devices, and enterprise assets, to each other and to the Internet. The information that flows through these items is vital because it serves to make relevant decisions. One of the main features of I4.0 is its adaptability to the human needs, this means that the items included in the I4.0 network are heterogeneous and they are large in number. The majority of I4.0 papers, which are focused on testing, describe a specific system or part of the I4.0 network. We have not found any paper that undertakes the testing of multiple connected IoT devices that will receive, process, and make decisions according to the complex and real data that travel through the network. In this article, we present IoT-TEG (Test Event Generator) 4.0, which is based on the test event generator system IoT-TEG. IoT-TEG 4.0 provides two new main contributions: the generation of test cases, which can include all the different types of data that the connected I4.0 devices under study can manage, and real-time testing. Additionally, its validation using real IoT programs is included and the results show that IoT-TEG 4.0 allows us to conduct tests that mimic real IoT system behaviors. [ABSTRACT FROM AUTHOR]

The optimal allocation of protective devices and switches in distribution systems concerns the analysis of conflicting attributes, such as capital investment, SAIDI, ENS, ECOST, MAIFI, among others. Regarding this multiattribute context, this paper proposes a multicriteria strategy to support decision-making. From this strategy, an assertive decision can be made under different scenarios when defining an appropriate solution to a real-world problem. A multiobjective optimization approach is applied to approximate an adequate set of potential solutions for allocating protective devices and switches. The capital investment and the SAIDI index are assumed as objective functions of the problem, and the well-known NSGA-II method is employed as the optimizer. Experiments are performed using the RBTS - Bus 4; the obtained trade-off solutions are evaluated in the previously stated attributes, and assisted decisions are made to illustrate the effect of considering different scenarios of preferences from the decision unit. The results highlight both the advantages of a multiobjective approach for allocating protective devices and the usefulness of a multicriteria strategy to aid assertive decision-making under different scenarios. [ABSTRACT FROM AUTHOR]