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Artificially intelligent systems (AISs) are being created by software developing companies (SDCs) to influence clinical decision-making. Historically, clinicians have led healthcare decision-making, and the introduction of AISs makes SDCs novel actors in the clinical decision-making space. Although these AISs are intended to influence a clinician's decision-making, SDCs have been clear that clinicians are in fact the final decision-makers in clinical care, and that AISs can only inform their decisions. As such, the default position is that clinicians should hold responsibility for the outcomes of the use of AISs. This is not the case when an AIS has influenced a clinician's judgement and their subsequent decision. In this paper, we argue that this is an imbalanced and unjust position, and that careful thought needs to go into how personal moral responsibility for the use of AISs in clinical decision-making should be attributed. This paper employs and examines the difference between prospective and retrospective responsibility and considers foreseeability as key in determining how personal moral responsibility can be justly attributed. This leads us to the view that moral responsibility for the outcomes of using AISs in healthcare ought to be shared by the clinical users and SDCs., (© 2023 The Authors. Bioethics published by John Wiley & Sons Ltd.)

With the continuous growth of electricity demand, the safe and stable operation of distribution lines is crucial for power transportation. Unmanned aerial vehicle (UAV) inspection has been widely used for the maintenance and repair of distribution lines. Due to the limitations of computational power and endurance, it is difficult for UAVs to independently complete data processing. Combined with mobile edge computing (MEC), this paper proposes a computing offloading strategy based on multi‐agent reinforcement learning and double‐layer offloading mechanism, which can further utilize the computing power of non‐task devices and edge servers. Firstly, three‐layer system architecture, named MEC‐U‐NTDC (MEC‐UAV‐Non‐task Device Cloud), is built. Secondly, double‐layer offloading mechanism is designed to comprehensively utilize the computing power of edge servers and neighbouring non‐task devices. Finally, a multi‐agent algorithm DLMQMIX is proposed to minimize the total cost for UAV inspection. Simulation experiments show that the proposed algorithm can effectively solve the task offloading problem of UAV‐aided distribution line inspection, and compared with algorithms such as PSO, GA, and QMIX, it performs better in terms of average delay, system cost, and load balancing, achieving a smaller total system cost. [ABSTRACT FROM AUTHOR]

The quality of management decisions is one of the main issues facing higher education institutions today. Strategic decisions taken by the higher education institutions affect policies, schemes, and actions that the institutions are considering. Machine learning is an emergent artificial intelligence field that utilizes different algorithms, analyses data, and delivers a better understanding of the data contained in a specific context. Hence, in this paper, data‐driven decision‐making model has been proposed based on artificial intelligence in colleges and universities. Student data, graduation rate and curriculum design have been analysed for administrative decision‐making in college or university based on the machine learning method. With the availability of huge quantities and high‐quality input training data, machine‐learning progressions can attain precise outcomes and enable informed decision‐making. The experimental findings show that the suggested model improves the outcome ratio of 90.72%, the performance ratio of 97.62%, prediction ratio of 96.35%, decision‐making level of 95.51%, accuracy ratio of 95.61%, an efficiency ratio of 98.14%, graduation rate of 85.86%, data security rate (95.61%) and error rate 33.21% compared to other methods. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) based clinical decision support systems (CDSS) are becoming ever more widespread in healthcare and could play an important role in diagnostic and treatment processes. For this reason, AI‐based CDSS has an impact on the doctor–patient relationship, shaping their decisions with its suggestions. We may be on the verge of a paradigm shift, where the doctor–patient relationship is no longer a dual relationship, but a triad. This paper analyses the role of AI‐based CDSS for shared decision‐making to better comprehend its promises and associated ethical issues. Moreover, it investigates how certain AI implementations may instead foster the inappropriate paradigm of paternalism. Understanding how AI relates to doctors and influences doctor–patient communication is essential to promote more ethical medical practice. Both doctors' and patients' autonomy need to be considered in the light of AI. [ABSTRACT FROM AUTHOR]

To support artificial intelligence (AI)‐involved tasks offloaded from the mobile devices (MDs), it is necessary to equip the Unmanned Aerial Vehicle (UAV) with custom‐made co‐processor (CP) for handling AI workloads in multi‐UAV‐empowered Edge Intelligence. Existing CPU‐oriented task scheduling algorithm cannot apply to the CPU+CP heterogeneous architecture. In this backdrop, this paper first formulates the joint service function placement, collaborative task scheduling, UAV deployment, and MD position determination problem as a Mixed Integer Non‐Linear Programming problem. Then, an alternating optimization‐based algorithm is put forward to derive a sub‐optimal solution of the problem utilizing Differential Evolution and Greedy‐based Hungarian algorithms. A series of experiments are conducted to evaluate the performance of the proposal. Results show that authors' proposal can achieve an overall revenue that is roughly 50% higher than those of existing methods. [ABSTRACT FROM AUTHOR]

This paper proposes the application of association rule mining to improve quizzes and courses. First, the paper shows how to preprocess quiz data and how to create several data matrices for use in the process of knowledge discovery. Next, the proposed algorithm that uses grammar-guided genetic programming is described and compared with both classical and recent soft-computing association rule mining algorithms. Then, different objective and subjective rule evaluation measures are used to select the most interesting and useful rules. Experiments have been carried out by using real data of university students enrolled on an artificial intelligence practice Moodle's course on the CLIPS programming language. Some examples of these rules are shown, together with the feedback that they provide to instructors making decisions about how to improve quizzes and courses. Finally, starting with the information provided by the rules, the CLIPS quiz and course have been updated. These innovations have been evaluated by comparing the performance achieved by students before and after applying the changes using one control group and two different experimental groups. [ABSTRACT FROM AUTHOR]

Case-based reasoning (CBR) has been used in various problem-solving areas such as financial forecasting, credit analysis and medical diagnosis. However, conventional CBR has the limitation that it has no criterion for choosing the nearest cases based on the probabilistic similarity of cases. It uses a fixed number of neighbors without considering an optimal number for each target case, so it does not guarantee optimal similar neighbors for various target cases. This leads to the weakness of lowering predictability due to deviation from desired similar neighbors. In this paper we suggest a new case extraction technique called statistical case-based reasoning. The main idea involves a dynamic adaptation of the optimal number of neighbors by considering the distribution of distances between potential similar neighbors for each target case. In order to do this, our technique finds the optimal distance threshold and selects similar neighbors satisfying the distance threshold criterion. We apply this new method to five real-life medical data sets and compare the results with those of the statistical method, logistic regression; we also compare the results with the learning methods C5.0, CART, neural networks and conventional CBR. The results of this paper show that the proposed technique outperforms those of many other methods, it overcomes the limitation of conventional CBR, and it provides improved classification accuracy. [ABSTRACT FROM AUTHOR]

Multi‐agent reinforcement learning relies on reward signals to guide the policy networks of individual agents. However, in high‐dimensional continuous spaces, the non‐stationary environment can provide outdated experiences that hinder convergence, resulting in ineffective training performance for multi‐agent systems. To tackle this issue, a novel reinforcement learning scheme, Mutual Information Oriented Deep Skill Chaining (MioDSC), is proposed that generates an optimised cooperative policy by incorporating intrinsic rewards based on mutual information to improve exploration efficiency. These rewards encourage agents to diversify their learning process by engaging in actions that increase the mutual information between their actions and the environment state. In addition, MioDSC can generate cooperative policies using the options framework, allowing agents to learn and reuse complex action sequences and accelerating the convergence speed of multi‐agent learning. MioDSC was evaluated in the multi‐agent particle environment and the StarCraft multi‐agent challenge at varying difficulty levels. The experimental results demonstrate that MioDSC outperforms state‐of‐the‐art methods and is robust across various multi‐agent system tasks with high stability. [ABSTRACT FROM AUTHOR]

Mahjong, a complex game with hidden information and sparse rewards, poses significant challenges. Existing Mahjong AIs require substantial hardware resources and extensive datasets to enhance AI capabilities. The authors propose a transformer‐based Mahjong AI (Tjong) via hierarchical decision‐making. By utilising self‐attention mechanisms, Tjong effectively captures tile patterns and game dynamics, and it decouples the decision process into two distinct stages: action decision and tile decision. This design reduces decision complexity considerably. Additionally, a fan backward technique is proposed to address the sparse rewards by allocating reversed rewards for actions based on winning hands. Tjong consists of 15M parameters and is trained using approximately 0.5 M data over 7 days of supervised learning on a single server with 2 GPUs. The action decision achieved an accuracy of 94.63%, while the claim decision attained 98.55% and the discard decision reached 81.51%. In a tournament format, Tjong outperformed AIs (CNN, MLP, RNN, ResNet, VIT), achieving scores up to 230% higher than its opponents. Furthermore, after 3 days of reinforcement learning training, it ranked within the top 1% on the leaderboard on the Botzone platform. [ABSTRACT FROM AUTHOR]

This paper gives an introduction to a recently developed strategic decision support methodology, which makes it possible to represent rule-setting and rule-fulfilling decision-making processes in companies with their structural and behavioral differences. This new methodology also allows us to simulate evolutionary processes in company systems based on these two forms of decision-making. The new strategic decision support methodology combines the continuous feedback loop concept of system dynamics with discontinuous logical loops, which we call spiral loops. The spiral loop concept, which is based on new developments in evolutionary theory and in the field of artificial intelligence, is used to represent the rule-setting strategic decisions, which generate qualitative change and evolution. The continuous feedback loop concept is used to model the rule-fulfilling policy decisions of companies, which can generate quantitative changes in interaction processes. In order to demonstrate how loop-based strategic decision support systems work in principle we show the 'portfolio simulation model,' which helps us to explain and to design the evolution of multibusiness firms in duopoly markets. [ABSTRACT FROM AUTHOR]

Surgical robot systems (SRS) represent an innovative cross‐disciplinary research field using robotic technology to assist surgeons in operations. Current bottlenecks in SRS, such as the limited ability to process complex information and make surgical decisions, have not been effectively solved. Artificial intelligence (AI) is a valuable technique for simulating and extending human intelligence. AI offers a new direction and impetus for SRS by enhancing performance in areas such as perception, navigation, surgical planning, and control strategies. This review introduces the developmental history of AI‐aided SRS, summarizes the basic SRS architecture, and analyzes how AI can improve SRS performance. Classical cases of AI‐aided SRS, the impact of evidence in clinical settings, and associated ethical and legal considerations are explored. Finally, the challenges in AI‐aided SRS are discussed, including algorithm development, surgical data science, human–robot coordination, and trust building between humans and robots. [ABSTRACT FROM AUTHOR]

The process of translating objectives into actions is a difficult task. This difficulty is due to the wide range of possibilities and the lack of structured information. Managers must take into account relevant information and generate a range of options before a decision is reached. So far, little is available to guide managers in translating a set of objectives into actions. This paper presents a three-stage action-planning process to address this gap. The process, supported by a software tool, takes managers through the stages of model building, action generation, and action evaluation and selection. A case study illustrates the application of the process. The paper concludes by discussing the implication of this work for managers and academics. [ABSTRACT FROM AUTHOR]

Research has indicated the importance of matching Information Technology (IT) applications or manufacturing systems with the competitive strategy of a company. Selection of the right type of IT application is, however, a challenging task. When a company with a given dominant process structure emphasizes two or more competitive priorities, such as quality, product flexibility, etc., an unaided manager faces a complex decision problem in choosing from alternative IT applications available in the areas of product design through distribution. In this paper, we develop an Intelligent Decision Support System (IDSS) to assist managers with assessment of the relative importance of competitive priorities in their organization, evaluation of the fit between the competitive priorities and their dominant process structure, and identification of the IT applications that are consistent with both the competitive priorities and the process structure. The IDSS is comprised of an interactive user interface, a knowledge database, a decision model, and a Knowledge-Based System (KBS) that was developed using the 1st Class KBS shell. Validation of the system illustrates that its performance is as good as the human expert, and it has the potential to facilitate effective and swift decision making in the selection of appropriate IT applications that best match an organization's manufacturing strategy. The choice and use of the right type of IT application should provide a company with a competitive edge. [ABSTRACT FROM AUTHOR]

A considerable amount of research has been conducted over a long period of time into the effects of graphical and tabular representations on decision-making performance. To date, however, the literature appears to have arrived at few conclusions with regard to the performance of the two representations. This paper addresses these issues by presenting a theory, based on information processing theory, to explain under what circumstances one representation outperforms the other. The fundamental aspects of the theory are: (1) although graphical and tabular representations may contain the same information, they present that information in fundamentally different ways; graphical representations emphasize spatial information, while tables emphasize symbolic information; (2) tasks can be divided into two types, spatial and symbolic, based on the type of information that facilitates their solution; (3) performance on a task will he enhanced when there is a cognitive fit (match) between the information emphasized in the representation type and that required by the task type; that is, when graphs support spatial tasks and when tables support symbolic tasks; (4) the processes or strategies problem solvers use are the crucial elements of cognitive fit since they provide the link between representation and task; the processes identified here are perceptual and analytical; (5) so long as there is a complete fit of representation, processes, and task type, each representation will lead to both quicker and more accurate problem solving. The theory is validated by its success in explaining the results of published studies that examine the performance of graphical and tabular representations in decision making. [ABSTRACT FROM AUTHOR]

With the rapid development of artificial intelligence, adopting advanced deep reinforcement learning (DRL) methodologies to solve the optimisation problem in power systems has become more effective. This study proposes a novel energy control method associated with DRL to solve the economical optimisation problems in an integrated energy system with wind power and power-to-gas technology. To consider the randomness of wind power and the flexibility of upper-level energy prices, the economical optimisation problem is formulated as a finite Markov decision-making process. Cycling decay learning rate deep deterministic policy gradient (CDLR-DDPG) algorithm is proposed to obtain the optimal operation strategy. A comparison among different benchmark methods is provided to demonstrate the superiority of CDLR-DDPG algorithm in optimising an economical problem for the considered system. [ABSTRACT FROM AUTHOR]

To fully utilize the voltage regulation capacity and interaction characteristics of the Transmission and Distribution (T&D) system, a novel Modified Partially Observed Markov Decision Process (MPOMDP)‐based Reinforcement Learning (RL) scheme for Autonomous Voltage Control is proposed, which incorporates Demand Response (DR) and cooperation with the Transmission Network. The proposed scheme consists of two vital components: an MPOMDP block and a Modified Asynchronous Advantage Actor‐Critic‐based RL block. The MPOMDP block innovatively exploits the confidence interval of the observed state to make a better perception of the precise system state by introducing two new probability vectors. Then the MPOMDP block is fed into the underlying architecture of the RL block for asynchronously capturing features and optimal decision‐making, where the solving framework additionally brings in a public data buffer to realize boundary information sharing. Case studies are conducted on a modified T&D system considering N‐1 contingencies, with a training dataset from a district in Suzhou, China. Simulation results demonstrate that the proposed scheme can achieve significant voltage optimization while ensuring fast convergence speed. [ABSTRACT FROM AUTHOR]

The incentive mechanism of federated learning has been a hot topic, but little research has been done on the compensation of privacy loss. To this end, this study uses the Local SGD federal learning framework and gives a theoretical analysis under the use of differential privacy protection. Based on the analysis, a multi‐attribute reverse auction model is proposed to be used for user selection as well as payment calculation for participation in federal learning. The model uses a mixture of economic and non‐economic attributes in making choices for users and is transformed into an optimisation equation to solve the user choice problem. In addition, a post‐auction negotiation model that uses the Rubinstein bargaining model as well as optimisation equations to describe the negotiation process and theoretically demonstrate the improvement of social welfare is proposed. In the experimental part, the authors find that their algorithm improves both the model accuracy and the F1‐score values relative to the comparison algorithms to varying degrees. [ABSTRACT FROM AUTHOR]

The paper presents a novel machine learning algorithm used for training a compound classifier system that consists of a set of area classifiers. Area classifiers recognize objects derived from the respective competence area. Splitting feature space into areas and selecting area classifiers are two key processes of the algorithm; both take place simultaneously in the course of an optimization process aimed at maximizing the system performance. An evolutionary algorithm is used to find the optimal solution. A number of experiments have been carried out to evaluate system performance. The results prove that the proposed method outperforms each elementary classifier as well as simple voting. [ABSTRACT FROM AUTHOR]

Fuzzy logic has been used as a means of interpreting vague, incomplete and even contradictory information into a compromised rule base in artificial intelligence such as machine decision–making. Within this context, fuzzy logic can be applied in the field of expert systems to provide additional flexibilities in constructing a working rule base: different experts’ opinions can be incorporated into the same rule base, and each opinion can be modeled in a rather vague notion of human language. As some illustrative application examples, this paper describes how fuzzy logic can be used in expert systems. More precisely, it demonstrates the following applications: (i) a healthcare diagnostic system, (ii) an autofocus camera lens system and (iii) a financial decision system. For each application, basic rules are described, the calculation method is outlined and numerical simulation is provided. These applications demonstrate the suitability and performance of fuzzy logic in expert systems. [ABSTRACT FROM AUTHOR]

This paper presents a decision support system (DSS) for managing production/distribution planning in a continuous manufacturing environment. The vendor has multiple plants and distribution centers (DCs). The trading partners have widely varying independent demand patterns. The DSS is designed for use in a multiproduct environment with overlapping raw materials and processing requirements. The production and distribution lead times at plants may span multiple planning periods. The impact of any manual override of a suggested solution can also be evaluated. The DSS is based on a linear programming model with a rolling horizon and was originally designed for a large process industry. Results of a pilot implementation using actual data are also presented, which show potential for significant savings for the company. [ABSTRACT FROM AUTHOR]

Research has recently focused on the effort-reduction or minimization role of computerized decision aids, and how users may employ aids m manage their effort, which in mm affects their choice of decision strategies. In this paper, it is argued that consideration of effort reduction or minimization by itself is not sufficient for inducing changes in decision strategy. Instead, decision aid effects on effort must be considered jointly with the decision quality associated with the various decision strategies. This is true even if the decision aid has no effect on decision quality. We adapt and extend a theoretical framework that can be used to evaluate the joint effects of effort and quality on decision strategy choice. In addition, we reinterpret past research results in light of the framework and present new experimental evidence on the descriptive validity of the framework. [ABSTRACT FROM AUTHOR]

A global economy and increase in customer expectations in terms of cost and services have put a premium on effective supply chain reengineering. It is essential to perform risk-benefit analysis of reengineering alternatives before making a final decision. Simulation provides an effective pragmatic approach to detailed analysis and evaluation of supply chain design and management alternatives. However, the utility of this methodology is hampered by the time and effort required to develop models with sufficient fidelity to the actual supply chain of interest. In this paper, we describe a supply chain modeling framework designed to overcome this difficulty. Using our approach, supply chain models are composed from software components that represent types of supply chain agents (e.g., retailers, manufacturers, transporters), their constituent control elements (e.g., inventory policy), and their interaction protocols (e.g., message types). The underlying library of supply chain modeling components has been derived from analysis of several different supply chains. It provides a reusable base of domain-specific primitives that enables rapid development of customized decision support tools. [ABSTRACT FROM AUTHOR]

This paper proposes modelbase construction mechanisms in a decision support system (DSS) for representing and managing models of diverse management science/operations research modeling paradigms, using object-oriented database management systems (ODBMS) constructs. It focuses on the construction of a modelbase that maintains logical independence among the DSS components including modelbase, database, and solvers, but relieves the mismatching characteristics by facilitating intelligent and stabilized integration of them. As a conceptual framework to build such a modelbase, this research uses generic model concepts, and adopts structured modeling language (SML) as a paradigm-neutral model representation sublanguage. In the modelbase, three model abstraction layers including model type, model structure, and model instance are devised to facilitate the capture of multiple modeling paradigms and specific application models in different instantiation levels. The constructs and methods discussed are flexible enough to be applied to a wide variety of decision-making and problem-solving paradigms. A prototype system is developed under a commercial ODBMS called OBJECTSTORE with the C++ programming language, and diverse model manipulation commands are illustrated by an object-oriented structured query language (OSQL). [ABSTRACT FROM AUTHOR]

This paper describes a knowledge representation approach and reasoning implementation in a real-time knowledge-based control system (KBCS) for navigating ships in restricted waters. This shipboard piloting expert system (SPES) is being developed as an intelligent node in Sperry Marine's ExBridge integrated ship's bridge system (IBS) for Exxon Shipping Company tankers. The SPES is intended to provide decision support to ships' navigation officers while piloting large vessels in restricted waters, and to reduce the information overload under which they labor, by incorporating local, transit-specific, and ship handling knowledge, and by providing requisite decision support in a timely fashion. As such, the system provides decision support to (1) senior ships' pilots training junior pilots; (2) ships' masters training junior deck officers in the essentials of good piloting and ship handling; and (3) watch standing deck officers utilizing the system's on-line reminder and assist capabilities, or off-line simulation and contingency planning functionality. [ABSTRACT FROM AUTHOR]

As information systems make the transition from decision support to decision making, there will be a concomitant need for the programs to explain or justify their actions. Without such explanations, humans will not readily cede authority to a machine. This paper describes an automated decision-making program, VOTE, which generates natural language explanations for its decisions in both English and French. The program domain is Congressional roll call voting. VOTE simulates voting decisions of specific members of the United States House of Representatives on given bills. VOTE's natural language generation facility is also used by the underlying databases to interpret the knowledge representations. These underlying knowledge representations are described and applications of this decision making model to the general business domains of strategic planning, investment, and marketing are suggested. [ABSTRACT FROM AUTHOR]

Modelling the resource allocation decision process in a decentralized organization using mathematical programming decomposition approaches has proven intractable for all but the smallest of problems. In this paper three artificial intelligence (AI) techniques are integrated to model the resource allocation decision process, and the solution for a three-level decentralized organization is illustrated. The techniques are (1) the filtered beam search, which selects a list of potential projects at the subordinate level, (2) the world model, which describes the ordinate and superordinate's worlds and their decision making processes, and (3) the blackboard model, which allows for global, but selective, storage of information. Benefits of the new approach include: several different mechanisms of managerial control may be modelled, the true information flow in an organization is more closely mirrored, and problems of a more realistic size are now viable. There are three primary ways in which the new model could be useful to management: The desirability and effects of different organizational structures can be modeled, the efficacy of various coordination mechanisms along the organizational structure can be examined, and a subset of projects from a wider list of possibilities can be selected. Also, an example is given showing how the model may be extended to scenarios with asymmetric information and divergence of preferences, whereby subordinates have different objectives than their managers and where there is no way for managers to verify the truthfulness of their subordinates' responses. [ABSTRACT FROM AUTHOR]

One justification for eliciting and representing the judgment strategy of an expert in a rule-based expert system (RBES) is to facilitate knowledge transfer to individuals with less expertise. However, prior research suggests complexities and potential problems when using RBESs for training. In this paper, a conceptual framework of user learning from RBESs is presented. It is proposed that learning may be ineffective when the problem representation of the RBES is inconsistent with the user's mental representation of the task-domain knowledge. An experiment was conducted to examine the effects of consistency (inconsistency) between the problem-solving strategy of RBESs and individuals' memory structures. Groups of subjects whose memory structure either matched or did not match two RBESs' problem-solving strategies were examined using an internal control evaluation task. The results suggest that learning was facilitated only for groups with congruence between the RBES's problem-solving strategy and a subject's memory structure. [ABSTRACT FROM AUTHOR]

Discriminant analysis is relevant to business decision making in a variety of contexts, such as when one decides to make or buy a specified component, fund a venture project, or hire a particular person. Potential applications in artificial intelligence, particularly in the area of pattern recognition, have further underscored the importance of the field. A recent innovation in discriminant analysis is provided by special linear programming (LP) models, which offer attractive alternatives to classical statistical approaches. The scope of application in which discriminant analysis can be advantageously employed is broadened by the flexibility to tailor parameters in the LP approaches to reflect diverse goals and by the power to explore the sensitivity of these parameters. In spite of the promise of the LP formulations, however, limitations to their effectiveness have been uncovered in certain settings. A recent advance involving a normalization construct removes some of the limitations but entails solving the LP model twice (to allow for different signs of a normalization constant) and does not yield equivalent solutions for different rotations of the problem data. This paper introduces a new model and a new class of normalizations that remedy both remaining limitations, making it possible to take advantage of the modeling capabilities of the LP formulations without the attendant shortcomings encountered by earlier investigations. Our development shows by empirical testing and illustrative analysis that the quality of solutions from LP discriminant approaches is more favorable (relative to the classical model) than previously supposed. [ABSTRACT FROM AUTHOR]

Decision Support Systems (DSSs) originally were proposed as interactive problem-solving vehicles through which models and analytical techniques could be made available to decision makers. Model management represents a line of research within the DSS field that focuses on the design and implementation of software environments to support the use of DSSs for this purpose. This paper describes the tasks that must be supported within these environments and illustrates how existing prototype Model Management Systems (MMSs) implementations provide this support. The use of artificial intelligence techniques in such implementations are reviewed, and three scenarios are presented to show how future MMSs could be constructed using these techniques. [ABSTRACT FROM AUTHOR]

This paper discusses two principles that have become increasingly important in the design of knowledge-based systems: domain-specific knowledge used to support opportunistic reasoning and hierarchical organization structure used to control and coordinate problem solving activity. We propose a design framework that embodies these two principles and describe how this framework has been used to develop a knowledge-based job-shop scheduling system. This system, called OPIS 0, has undergone limited testing in an experimental environment modeled after an actual job shop. Its performance has been very good compared to ISIS and to the more traditional approach of constructing a schedule by dispatching jobs using the COVERT priority rule. The resulting design also shows potential for use in a decision support role. [ABSTRACT FROM AUTHOR]

Domain knowledge for various decision‐making activities of Software Engineering (SE) is rarely available in a structured or well‐formalised form. Owing to lack of the well‐informed knowledge, decision making for different kinds of predictions and estimations in SE domain is a challenge. Maintenance and elicitation of domain knowledge is an overwhelming task and causes the knowledge acquisition bottleneck. Most of the artificial intelligence techniques of prediction and estimation do not work in absence of complete and structured knowledge. Case‐based reasoning (CBR) is a lazy learning paradigm of artificial intelligence which takes care of this challenge and helps to reduce the knowledge availability bottleneck. This technique exploits the similar experience of past which may be available in unstructured form, and improves its learning curve with passage of time. In literature, CBR has been successfully applied in various areas of SE, but there is lack of single systematic panoramic picture which might have highlighted the potential research questions in this direction. In this study, the author has presented a comprehensive and panoramic systematic mapping study of various CBR applications in SE domain, and identified some promising future research directions. [ABSTRACT FROM AUTHOR]

The honey bee swarm carried out its best‐of‐N nest site computations by operating more than a dozen different information processing loops in parallel and by recruiting more resources to provide greater precision in loops evaluating the better quality sites. The positive feedback effects of recurrent recruitment by means of waggle dance signalling amplified the utilisation of the swarm's energy, memory and carrier resources. The relatively strong negative feedback effects of various attenuation mechanisms tended to reduce resource use and therefore counter‐balanced amplification for long enough for a meaningful nest site survey and quorum decision to be made. Some information processing mechanisms such as exploration tendency, waggle dance signalling, site non‐specific attenuation, noise reduction, independent site evaluation, energy efficient coding, mixed precision processing, self‐organising computation and quorum decision making were found to profoundly influence the efficiency of resource use. Significant insights were also gained into extended cognition, dark data processing, information quality and resource leakage. Finally, the energy cost of acquiring and processing sensory information was estimated. [ABSTRACT FROM AUTHOR]

An intelligent decision support information system is applied to develop an artificial intelligence (AI)‐driven solution to reduce patient waiting time in a hospital without an appointment system to improve healthcare service quality. The bottlenecks were identified, and then the healthcare service process was reengineered through the proposed shortest‐consultation time (SCT) model to increase the capacity for serving patients and the synchronization between capacity and patient arrival patterns. The study made several meaningful observations: (1) It is feasible to apply an AI‐driven solution to reduce patient waiting time in a hospital with no appointment system to improve healthcare service quality; (2) analytical models are helpful in identifying characteristics of patient flow problems; (3) the implementation of a few performance factors gives most of the improvements, and (4) theory of constraint (TOC) is a method that can be applied to improve patient service qualification. [ABSTRACT FROM AUTHOR]

This study explores the impact of artificial intelligence (AI) on user entrepreneurs, driving factors and user entrepreneurship process combined with its characteristics and development trends. Moreover, this study sorts out the logic of user entrepreneurship change in the AI era. This study achieves four findings. First, AI contributes to entrepreneurs by collecting large user data and intelligently analysing them to obtain optimal entrepreneurial judgements and decisions. Second, entrepreneurs may use AI systems to understand users' potential needs and get user demand information (e.g. more accurate and more major shortcomings of the products). Third, AI assists entrepreneurs in obtaining robust data of product users, including leading and ordinary users, for a wider audience. Fourth, AI replaces the original intergenerational product replacement model with intermittent and periodic characteristics for entrepreneurial activities and reforms the three‐stage user entrepreneurial process: product element deconstruction, product verification matching and innovative product commercialization. This study provides a feasible direction for the key research issues. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a new method of ranking trapezoidal intuitionistic fuzzy numbers has been introduced based on the concept of value and ambiguity at different levels of decision‐making. The concept of decision levels α for the membership function and β for the non‐membership function called as the flexibility parameters have been introduced in ranking these types of fuzzy numbers. If the flexibility parameters α is close to maximum membership degree of the membership function and β close to minimum membership degree of non‐membership function, then a high‐level decision is made. Likewise, if the flexibility parameters α is close to minimum membership degree of membership function and β close to maximum membership degree of non‐membership function, then a low‐level decision is made. Again, if the flexibility parameters α and β lie between minimum membership degree of the membership function and maximum membership degree of the non‐membership function, then an intermediate decision is made. This phenomenon of the proposed method is an attractive feature as it allows the decision‐maker to make a choice on the levels of decision. Further, the rationality validation of the proposed method has been checked by proving some of the Wang and Kerre's reasonable properties on ordering fuzzy quantities. [ABSTRACT FROM AUTHOR]

The goal of continual learning (CL) is to learn a sequence of tasks without suffering from the phenomenon of catastrophic forgetting. Previous work has shown that leveraging memory in the form of a replay buffer can reduce performance degradation on prior tasks. We hypothesize that forgetting can be further reduced when the model is encouraged to remember the evidence for previously made decisions. As a first step towards exploring this hypothesis, we propose a simple novel training paradigm, called Remembering for the Right Reasons (RRR), that additionally stores visual model explanations for each example in the buffer and ensures the model has "the right reasons" for its predictions by encouraging its explanations to remain consistent with those used to make decisions at training time. Without this constraint, there is a drift in explanations and increase in forgetting as conventional continual learning algorithms learn new tasks. We demonstrate how RRR can be easily added to any memory or regularization-based approach and results in reduced forgetting, and more importantly, improved model explanations. We have evaluated our approach in the standard and fewshot settings and observed a consistent improvement across various CL approaches using different architectures and techniques to generate model explanations and demonstrated our approach showing a promising connection between explainability and continual learning. Our code is available at https://github.com/SaynaEbrahimi/Remembering-for-the-Right-Reasons. [ABSTRACT FROM AUTHOR]

Neural network architectures are achieving superhuman performance on an expanding range of tasks. To effectively and safely deploy these systems, their decision-making must be understandable to a wide range of stakeholders. Methods to explain artificial intelligence (AI) have been proposed to answer this challenge, but a lack of theory impedes the development of systematic abstractions, which are necessary for cumulative knowledge gains. We propose Bayesian Teaching as a framework for unifying explainable AI (XAI) by integrating machine learning and human learning. Bayesian Teaching formalizes explanation as a communication act of an explainer to shift the beliefs of an explainee. This formalization decomposes a wide range of XAI methods into four components: (a) the target inference, (b) the explanation, (c) the explainee model, and (d) the explainer model. The abstraction afforded by Bayesian Teaching to decompose XAI methods elucidates the invariances among them. The decomposition of XAI systems enables modular validation, as each of the first three components listed can be tested semi-independently. This decomposition also promotes generalization through recombination of components from different XAI systems, which facilitates the generation of novel variants. These new variants need not be evaluated one by one provided that each component has been validated, leading to an exponential decrease in development time. Finally, by making the goal of explanation explicit, Bayesian Teaching helps developers to assess how suitable an XAI system is for its intended real-world use case. Thus, Bayesian Teaching provides a theoretical framework that encourages systematic, scientific investigation of XAI. [ABSTRACT FROM AUTHOR]

This study investigated the resource budget available to a typical honey bee swarm as it gathered information and decided which was the best of some 13–34 potential nest sites to move into and become its new home. After a brief description of the decision‐making mechanisms involved, the utilization of the swarm's finite energy, memory and carrier resources is described. It was found that each individual scout decision maker only carried enough energy reserves to provide for 2.6 days of visits to evaluate distant sites and, effectively, only enough memory space to remember the location and quality of approximately one site. Scouts, who only formed 4.4% of the swarm bees, consumed 22.8% of the swarm's energy budget, showing that their best‐of‐N computations were energetically very expensive. A typical swarm of 12,000 bees only contained enough energy reserves to last for 13.5 days after which foraging had to restart. [ABSTRACT FROM AUTHOR]

Policy PointsWith increasing integration of artificial intelligence and machine learning in medicine, there are concerns that algorithm inaccuracy could lead to patient injury and medical liability.While prior work has focused on medical malpractice, the artificial intelligence ecosystem consists of multiple stakeholders beyond clinicians. Current liability frameworks are inadequate to encourage both safe clinical implementation and disruptive innovation of artificial intelligence.Several policy options could ensure a more balanced liability system, including altering the standard of care, insurance, indemnification, special/no‐fault adjudication systems, and regulation. Such liability frameworks could facilitate safe and expedient implementation of artificial intelligence and machine learning in clinical care. [ABSTRACT FROM AUTHOR]

Policy PointsNudges steer people toward certain options but also allow them to go their own way. "Dark nudges" aim to change consumer behavior against their best interests. "Sludge" uses cognitive biases to make behavior change more difficult.We have identified dark nudges and sludge in alcohol industry corporate social responsibility (CSR) materials. These undermine the information on alcohol harms that they disseminate, and may normalize or encourage alcohol consumption.Policymakers and practitioners should be aware of how dark nudges and sludge are used by the alcohol industry to promote misinformation about alcohol harms to the public. Context: "Nudges" and other behavioral economic approaches exploit common cognitive biases (systematic errors in thought processes) in order to influence behavior and decision‐making. Nudges that encourage the consumption of harmful products (for example, by exploiting gamblers' cognitive biases) have been termed "dark nudges." The term "sludge" has also been used to describe strategies that utilize cognitive biases to make behavior change harder. This study aimed to identify whether dark nudges and sludge are used by alcohol industry (AI)–funded corporate social responsibility (CSR) organizations, and, if so, to determine how they align with existing nudge conceptual frameworks. This information would aid their identification and mitigation by policymakers, researchers, and civil society. Methods: We systematically searched websites and materials of AI CSR organizations (e.g., IARD, Drinkaware, Drinkwise, Éduc'alcool); examples were coded by independent raters and categorized for further analysis. Findings: Dark nudges appear to be used in AI communications about "responsible drinking." The approaches include social norming (telling consumers that "most people" are drinking) and priming drinkers by offering verbal and pictorial cues to drink, while simultaneously appearing to warn about alcohol harms. Sludge, such as the use of particular fonts, colors, and design layouts, appears to use cognitive biases to make health‐related information about the harms of alcohol difficult to access, and enhances exposure to misinformation. Nudge‐type mechanisms also underlie AI mixed messages, in particular alternative causation arguments, which propose nonalcohol causes of alcohol harms. Conclusions: Alcohol industry CSR bodies use dark nudges and sludge, which utilize consumers' cognitive biases to promote mixed messages about alcohol harms and to undermine scientific evidence. Policymakers, practitioners, and the public need to be aware of how such techniques are used to nudge consumers toward industry misinformation. The revised typology presented in this article may help with the identification and further analysis of dark nudges and sludge. [ABSTRACT FROM AUTHOR]

In the Anthropocene, human actions affect the persistence and abundance of nearly all critical natural resources, including the biodiversity on which indispensable ecosystem services depend (Dirzo et al. 2014; Ellis 2019). Game Development for Social-Ecological Researchers Open-ended online social videogames (hereafter videogames) provide immersive simulated environments for millions of players worldwide (e.g., Burroughs 2014; Del-Moral Pérez & Guzmán-Duque 2014). Data Collection and Application to Social-Ecological Models The videogame platform that we propose could be used to efficiently create complex game designs and automate data collection in game sessions as they are currently conducted in social-ecological research. Online multiplayer games as virtual laboratories for collecting data on social-ecological decision making. [Extracted from the article]

Software-defined networking (SDN) represents a promising networking architecture that combines central management and network programmability. SDN separates the control plane from the data plane and moves the network management to a central point, called the controller that can be programmed and used as the brain of the network. Recently, the research community has shown an increased tendency to benefit from the recent advancements in the artificial intelligence (AI) field to provide learning abilities and better decision making in SDN. In this study, the authors provide a detailed overview of the recent efforts to include AI in SDN. The study showed that the research efforts focused on three main sub-fields of AI namely: machine learning, meta-heuristics and fuzzy inference systems. Accordingly, in this work, the authors investigate their different application areas and potential use, as well as the improvements achieved by including AI-based techniques in the SDN paradigm. [ABSTRACT FROM AUTHOR]

Many contemporary computer games, notably action and role-playing games, represent an interesting class of navigation-intensive dynamic real-time simulations inhabited by autonomous intelligent virtual agents (IVAs). Although higher level reasoning of IVAs in these domains seems suited for action planning, planning is not widely adopted in existing games and similar applications. Moreover, statistically rigorous study measuring performance of planners in decision making in a game-like domain is missing. Here, five classical planners were connected to the virtual environment of Unreal Development Kit along with a planner for delete-free domains (only positive preconditions and positive effects). Performance of IVAs employing those planners and IVAs with reactive architecture was measured on a class of game-inspired test environments of various sizes and under different levels of external interference. The analysis has shown that planning agents outperform reactive agents if (i) the size of the problem is small or if (b) the environment changes are either hostile to the agent or infrequent. In delete-free domains, specialized approaches are inferior to classical planners because the lower expressivity of delete-free domains results in lower plan quality. These results can help to determine when planning is advantageous in games and for IVAs control in other dynamic real-time environments. [ABSTRACT FROM AUTHOR]

ABSTRACT The group ranking problem involves constructing coherent aggregated results from users' preference data. The goal of most group ranking problems is to generate an ordered list of all items that represents the user consensus. There are, however, two weaknesses to this approach. First, a complete list of ranked items is always output even when there is no consensus or only a slight consensus. Second, due to similarity of performance, in many practical situations, it is very difficult to differentiate whether one item is really better than another within a set. These weaknesses have motivated us to apply the clustering concept to the group ranking problem, to output an ordered list of segments containing a set of similarly preferred items, called consensus ordered segments. The advantages of our approach are that (i) the list of segments is based on the users' consensuses, (ii) the items with similar preferences are grouped together in the same segment, and (iii) the relationships between items can be easily seen. An algorithm is developed to construct the consensus of the ordered segments from the users' total ranking data. Finally, the experimental results indicate that the proposed method is computationally efficient, and can effectively identify consensus ordered segments. [ABSTRACT FROM AUTHOR]

This study proposes the use of a modified genetic algorithm (MGA), a global search technique, as a training method to improve generalizability and to identify relevant inputs in a neural network (NN) model. Generalizability refers to the NN model's ability to perform well on exemplars (observations) that were not used during training (out-of-sample); improved generalizability enhances NN's acceptability as a valid decision-support tool. The MGA improves generalizability by setting unnecessary weights (or connections) to zero and by eliminating these weights. Because the eliminated weights have no further impact on the training (in-sample or out-of-sample data), the relevant variables can be identified from the model. By eliminating unnecessary weights, the MGA is able to search and find a parsimonious model that generalizes well. Unlike the traditional NN, the MGA identifies the model variables that contribute to an outcome, helping decision makers to rationalize output and accept results with greater confidence. The study uses real-life data to demonstrate the use of MGA. [ABSTRACT FROM AUTHOR]

The purpose of this research is to explore the promise of autonomous software agents in Decision Support Systems (DSS). Because definitions of software agents extant in the literature are divergent, we develop and provide a descriptive definition useful for our purpose. The benefits of agents and the particular characteristics of agents leading to DSS enrichment are examined. To facilitate this we build a DSS described elsewhere in the literature and enhance it with different types of autonomous software agents. From this experience, a general framework for agent-enabled DSS is suggested. It is concluded that such a DSS in general will be more difficult to build than traditional DSS, but at least some agent-enabled DSS will bring significant benefit to users. [ABSTRACT FROM AUTHOR]

The central issue of this research is the extent to which computer facilities can be used to support organizational decision-making processes beyond mere performance of information retrieval. This depends upon the extent to which computers can be made to emulate human perceptual and judgmental processes. We present a framework for understanding these Cognitive processes and examine how it applies to organizational decisions. Moreover, the framework furnishes a basis for the design of a generalized, intelligent problem processor. This processor is general in the sense of its ability to support a decision maker's activities, regardless of the decision maker's application area (e.g., urban planning, water-quality planning, etc.). It is intelligent in the sense of its ability to comprehend English-like queries and subsequently formulate models, interface appropriate data with those models, and execute the models to produce some facts or expectations about the problem under consideration. [ABSTRACT FROM AUTHOR]

This laboratory experiment studied the effects of source and participant anonymity, and differences in initial opinions in an electronic meeting system (EMS) context. Results indicated that (a) inhibition to participate did not mediate the effect of anonymity on participation as suggested in prior deindividuation and EMS literature; and (b) the effect of anonymity on participation, satisfaction, and postdiscussion agreement depended on the level and type of anonymity involved and whether initial opinions among group members were similar or different. A mechanism that suggests that anonymity may cause participants to focus more on their task by turning attention away from other participants partly accounts for the effects of source and participant anonymity on participation in this study. Future work should focus on determining how different types and levels of anonymity influence process and outcomes in different types of groups. [ABSTRACT FROM AUTHOR]

Effective production scheduling requires consideration of the dynamics and unpredictability of the manufacturing environment. An automated learning scheme, utilizing genetic search, is proposed for adaptive control in typical decentralized factory-floor decision making. A high-level knowledge representation for modeling production environments is developed, with facilities for genetic learning within this scheme. A multiagent framework is used, with individual agents being responsible for the dispatch decision making at different workstations. Learning is with respect to stated objectives, and given the diversity of scheduling goals, the efficacy of the designed learning scheme is judged through its response under different objectives. The behavior of the genetic learning scheme is analyzed and simulation studies help compare how learning under different objectives impacts certain aggregate measures of system performance. [ABSTRACT FROM AUTHOR]

The selection of competent contractors is a critical function in all business organizations. In contrast to other types of vendors (e.g., distributors, manufacturers, etc.), contractors are typically accredited before any business transaction takes place. In such situations, there is often a considerable amount of uncertainty associated with the accreditation process. This research presents a probabilistic model for accrediting contractors. We discuss a methodology in which probability measures are used to capture the uncertainty inherent in the decision process. These probabilities are estimated from data on (i) past applicants and (ii) their eventual performance, if accredited. Furthermore, these probabilities are used to determine when additional information about an applicant should be collected, as well as what kind of information would be most relevant for the vendor under consideration. [ABSTRACT FROM AUTHOR]