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Reviews have long been recognized as among the most important forms of scientific communication. The rapid growth of the primary literature has further increased the need for reviews to distill and interpret the literature. This review on Reviews and Reviewing: Approaches to Research Synthesis encompasses the evolution of the review literature, taxonomy of review literature, uses and users of reviews, the process of preparing reviews, assessment of review quality and impact, the impact of information technology on the preparation of reviews, and research opportunities for information science related to reviews and reviewing. In addition to providing a synthesis of prior research, this review seeks to identify gaps in the published research and to suggest possible future research directions. [ABSTRACT FROM AUTHOR]

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]

Summary: Artificial Intelligence (AI) based cyber threat detection tools are widely used to process and analyze a large amount of data for improved intrusion detection performance. However, these models are often considered as black box by the cybersecurity experts due to their inability to comprehend or interpret the reasoning behind the decisions. Moreover, AI‐based threat hunting is data‐driven and is usually modeled using the data provided by multiple cloud vendors. This is another critical challenge, as a malicious cloud can provide false information (i.e., insider attacks) and can degrade the threat‐hunting capability. In this paper, we present a blockchain‐enabled eXplainable AI (XAI) for enhancing the decision‐making capability of cyber threat detection in the context of Smart Healthcare Systems. Specifically, first, we use blockchain to validate and store data between multiple cloud vendors by implementing a Clique Proof‐of‐Authority (C‐PoA) consensus. Second, a novel deep learning‐based threat‐hunting model is built by combining Parallel Stacked Long Short Term Memory (PSLSTM) networks with a multi‐head attention mechanism for improved attack detection. The extensive experiment confirms its potential to be used as an enhanced decision support system by cybersecurity analysts. [ABSTRACT FROM AUTHOR]

In agriculture, important unanswered questions about machine learning and artificial intelligence (ML/AI) include will ML/AI change how food is produced and will ML algorithms replace or partially replace farmers in the decision process. As ML/AI technologies become more accurate, they have the potential to improve profitability while reducing the impact of agriculture on the environment. However, despite these benefits, there are many adoption barriers including cost, and that farmers may be reluctant to adopt a decision tool they do not understand. The goal of this special issue is to discuss cutting‐edge research on the use of ML/AI technologies in agriculture, barriers to the adoption of these technologies, and how technologies can affect our current workforce. The papers are separated into three sections: Machine Learning within Crops, Pasture, and Irrigation; Machine Learning in Predicting Crop Disease; and Society and Policy of Machine Learning. Core Ideas: There are concerns that decision‐making tools using machine learning and artificial intelligence (ML/AI) in farming could replace human knowledge and labor.ML/AI technologies have the potential to improve precision farming using site‐specific algorithms.This special issue aims to open a dialogue about the role of ML/AI in agriculture and identify inequalities that may result from its use. [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]

This paper puts forth the foundation for a different type of negotiation that reflects in a more realistic way the behavior of human beings when making complex decisions, aligned with the cognitive process involved. The positive and the negative aspects of decisions reflect two components in the human brain: the nucleus accumbens and the amygdala. The first component deals with behavior related to rewards or potential gains, and the other with behavior related to risk aversion or potential losses. Negotiations require communication, learning, accommodation of positions, and development of alternatives and modification of constraints. Negotiation support systems help and advice negotiators; structure and analyze the problem; elicit preferences to construct a preference function; visualize different aspects of the problem and the process; and facilitate communication and learning. The methodology used is based on the Analytic Hierarchy Process previously employed in a retributive conflict in which each party calculates the incremental benefits it gets and the costs to its opponent. The Israeli–Palestinian conflict is used to show the viability of the approach and the type of inputs we need to study conflicts with this approach. The negotiation platform that we describe here help the actors to start and engage a negotiation from noncooperative parties. It can also be used in human– negotiations to expedite reaching an agreement if one exists. However, we are not seeking automated negotiations. Advantages and risks of Artificial Intelligence contribution in negotiation support systems for cognitive and retributive conflict resolution based on AHP (CRCR‐AHP) are also discussed. [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]

Artificial intelligence (AI) is increasingly being used in decision making across various industries, including the public health arena. Bias in any decision‐making process can significantly skew outcomes, and AI systems have been shown to exhibit biases at times. The potential for AI systems to perpetuate and even amplify biases is a growing concern. Bias, as used in this paper, refers to the tendency toward a particular characteristic or behavior, and thus, a biased AI system is one that shows biased associations entities. In this literature review, we examine the current state of research on AI bias, including its sources, as well as the methods for measuring, benchmarking, and mitigating it. We also examine the biases and methods of mitigation specifically relevant to the healthcare field and offer a perspective on bias measurement and mitigation in regulatory science decision making. [ABSTRACT FROM AUTHOR]

Summary: Although microscopy is often treated as a quasi‐static exercise for obtaining a snapshot of events and structure, it is clear that a more dynamic approach, involving real‐time decision making for guiding the investigation process, may provide deeper insights, more efficiently. On the other hand, many applications of machine learning involve the interpretation of local circumstances from experience gained over many observations; that is, machine learning potentially provides an ideal solution for more efficient microscopy. This paper explores the potential for informing the microscope's observation strategy while characterising critical events. In particular, the identification of regions likely to experience twin activity (twin interaction with grain boundary) in AZ31 magnesium is attempted, from only local information. EBSD‐based observations in the neighbourhoods of twin activity are fed into a machine‐learning environment to inform the future search for such events, and the accuracy of the resultant decisions is quantified relative to the number of prior observations. The potential for utilising different types of local information, and their resultant value in the prediction process, is also assessed. After applying an attribute selection filter, and various other machine‐learning tools, a decision‐tree model is able to classify likely neighbourhoods of twin activity with 85% accuracy. The resultant framework provides the first step towards an intelligent microscopy for efficient observation of stochastic events during in situ microscopy campaigns. Lay description: One role of artificial intelligence is to predict future events after learning from many previous observations. In materials science, various phenomena (such as crack nucleation) are difficult to predict because they have been insufficiently observed. Furthermore, observation is difficult, precisely because their location cannot be predicted, leading to a chicken and egg conundrum. This paper applies machine learning to the search for twin nucleation sites in a magnesium alloy, in an attempt to guide the observation of twin nucleation events in a microscope based on previous observations. As more data is obtained, the accuracy of the location prediction will increase. In the current case, the machine‐learning tool achieved 85% accuracy for predicting the location of twin interactions with grain boundaries after several thousand observations. The resultant framework provides the first step towards an intelligent microscopy for efficient observation of stochastic events during in situ microscopy campaigns. [ABSTRACT FROM AUTHOR]

Research suggests that algorithms—based on artificial intelligence or linear regression models—make better predictions than humans in a wide range of domains. Several studies have examined the degree to which people use algorithms. However, these studies have been mostly cross‐sectional and thus have failed to address the dynamic nature of algorithm use. In the present paper, we examined algorithm use with a novel longitudinal approach outside the lab. Specifically, we conducted two ecological momentary assessment studies in which 401 participants made financial predictions for 18 days in two tasks. Relying on the judge‐advisor system framework, we examined how time interacted with advice source (human vs. algorithm) and advisor accuracy to predict advice taking. Our results showed that when the advice was inaccurate, people tended to use algorithm advice less than human advice across the period studied. Inaccurate algorithms were penalized logarithmically; the effect was initially strong but tended to fade over time. This suggests that first impressions are crucial and produce significant changes in advice taking at the beginning of the interaction, which later tends to stabilize as days go by. Therefore, inaccurate algorithms are more likely to accrue a negative reputation than inaccurate humans, even when having the same level of performance. [ABSTRACT FROM AUTHOR]

Prior research in judgment and decision making (JDM) has investigated the effect of problem framing on human preferences. Furthermore, research in JDM documented the absence of such reversal of preferences when making decisions from experience. However, little is known about the effect of context on preferences under the combined influence of problem framing and problem format. Also, little is known about how cognitive models would account for human choices in different problem frames and types (general/specific) in the experience format. One of the primary objectives of this research is to investigate the presence of preference reversals under the influence of problem framing (gain/loss), problem format (experience/description), and problem type (general/specific). Another objective of this research is to develop cognitive models to account for human choices across different problem frames and types in the experience format. A total of 320 participants from India were randomly assigned to one of eight between‐subjects conditions that differed in problem frame, format, and type. Results revealed preference reversals in the description condition; however, they were absent in the experience condition. Moreover, preference reversals were less pronounced in the general problem framing compared to the specific problem framing. Furthermore, specific problems influenced risk‐seeking behavior among participants. We developed cognitive and heuristics models using instance‐based learning theory and natural mean heuristic. Results reveal models' dependency on recent and frequent observations during information sampling. These experience‐based cognitive models could help build artificial intelligence models with fewer preference reversals. This paper focuses on investigating the presence of preference reversals under the influence of problem framing (gain/loss), problem format (experience/description), and problem type (general/specific). Furthermore, Uttrani et al. develop cognitive models using instance‐based learning theory and natural mean heuristic to account for human choices across different problem frames and types in the experience format. [ABSTRACT FROM AUTHOR]

To date, many AI initiatives (eg, AI4K12, CS for All) developed standards and frameworks as guidance for educators to create accessible and engaging Artificial Intelligence (AI) learning experiences for K‐12 students. These efforts revealed a significant need to prepare youth to gain a fundamental understanding of how intelligence is created, applied, and its potential to perpetuate bias and unfairness. This study contributes to the growing interest in K‐12 AI education by examining student learning of modelling real‐world text data. Four students from an Advanced Placement computer science classroom at a public high school participated in this study. Our qualitative analysis reveals that the students developed nuanced and in‐depth understandings of how text classification models—a type of AI application—are trained. Specifically, we found that in modelling texts, students: (1) drew on their social experiences and cultural knowledge to create predictive features, (2) engineered predictive features to address model errors, (3) described model learning patterns from training data and (4) reasoned about noisy features when comparing models. This study contributes to an initial understanding of student learning of modelling unstructured data and offers implications for scaffolding in‐depth reasoning about model decision making. Practitioner notes: What is already known about this topic Scholarly attention has turned to examining Artificial Intelligence (AI) literacy in K‐12 to help students understand the working mechanism of AI technologies and critically evaluate automated decisions made by computer models.While efforts have been made to engage students in understanding AI through building machine learning models with data, few of them go in‐depth into teaching and learning of feature engineering, a critical concept in modelling data.There is a need for research to examine students' data modelling processes, particularly in the little‐researched realm of unstructured data. What this paper adds Results show that students developed nuanced understandings of models learning patterns in data for automated decision making.Results demonstrate that students drew on prior experience and knowledge in creating features from unstructured data in the learning task of building text classification models.Students needed support in performing feature engineering practices, reasoning about noisy features and exploring features in rich social contexts that the data set is situated in. Implications for practice and/or policy It is important for schools to provide hands‐on model building experiences for students to understand and evaluate automated decisions from AI technologies.Students should be empowered to draw on their cultural and social backgrounds as they create models and evaluate data sources.To extend this work, educators should consider opportunities to integrate AI learning in other disciplinary subjects (ie, outside of computer science classes). [ABSTRACT FROM AUTHOR]

Our research reveals the continued and evolving role of the human factor in decision making in digitalized retail supply chains. We compare managerial roles in a pre‐ and post‐COVID era through conducting in‐depth interviews of 25 executives spanning the retail supply chain ecosystem. We use grounded theory to develop four main contributions. First, we find that the involvement of managerial judgment is found to be progressively greater moving up the retail supply chain, away from the customer and the demand signal. Second, integration of analytics and judgment is now the primary method of decision making, and we identify elements needed for success. Third, we develop an essential framework for a successful integration process. Fourth, we isolate the necessary components of a successful process for analytics/artificial intelligence (AI) implementation. Our paper offers important insights into how analytics and AI are—and should be used—in judgment and decision making and opportunities for researchers to understand the changing role of the human factor in digitalized retail supply chains. [ABSTRACT FROM AUTHOR]

In recent years there has been an explosion of interest in Artificial Intelligence (AI) both in health care and academic philosophy. This has been due mainly to the rise of effective machine learning and deep learning algorithms, together with increases in data collection and processing power, which have made rapid progress in many areas. However, use of this technology has brought with it philosophical issues and practical problems, in particular, epistemic and ethical. In this paper the authors, with backgrounds in philosophy, maternity care practice and clinical research, draw upon and extend a recent framework for shared decision‐making (SDM) that identified a duty of care to the client's knowledge as a necessary condition for SDM. This duty entails the responsibility to acknowledge and overcome epistemic defeaters. This framework is applied to the use of AI in maternity care, in particular, the use of machine learning and deep learning technology to attempt to enhance electronic fetal monitoring (EFM). In doing so, various sub‐kinds of epistemic defeater, namely, transparent, opaque, underdetermined, and inherited defeaters are taxonomized and discussed. The authors argue that, although effective current or future AI‐enhanced EFM may impose an epistemic obligation on the part of clinicians to rely on such systems' predictions or diagnoses as input to SDM, such obligations may be overridden by inherited defeaters, caused by a form of algorithmic bias. The existence of inherited defeaters implies that the duty of care to the client's knowledge extends to any situation in which a clinician (or anyone else) is involved in producing training data for a system that will be used in SDM. Any future AI must be capable of assessing women individually, taking into account a wide range of factors including women's preferences, to provide a holistic range of evidence for clinical decision‐making. [ABSTRACT FROM AUTHOR]

The goals of learning health systems (LHS) and of AI in medicine overlap in many respects. Both require significant improvements in data sharing and IT infrastructure, aim to provide more personalized care for patients, and strive to break down traditional barriers between research and care. However, the defining features of LHS and AI diverge when it comes to the people involved in medicine, both patients and providers. LHS aim to enhance physician‐patient relationships while developments in AI emphasize a physicianless experience. LHS also encourage better coordination of specialists across the health system, but AI aims to replace many specialists with technology and algorithms. This paper argues that these points of conflict may require a reconsideration of the role of humans in medical decision making. Although it is currently unclear to what extent machines will replace humans in healthcare, the parallel development of LHS and AI raises important questions about the exact role for humans within AI‐enabled healthcare. [ABSTRACT FROM AUTHOR]

Digital technologies have become a crucial factor in nursing. Given the fact that many tasks could also be done by robots or AI systems, the place for the nurse in this scenario is unclear. In what way and to what extent will the implementation of ever more sophisticated technology affect nursing practice? It is the aim of this paper to analyse the potential challenges of nursing practice in the digital age. The analysis is conducted through the lens of new materialism, a set of theoretical models that understand the relationship between humans and technology as dynamic and performative. According to this view, there is no prefixed essence of technology. Rather, the meaning of technology is enacted in concrete practice. The analysis shows that in past debates on technology use in nursing, the nurses' role has been defined as guardians of humanity, defending the patient against the dehumanizing effects of technology. This role has been transferred to the digital age, where it is the duty of nurses to cushion the negative effects of digital technology. As an alternative to this outdated role, nurses should be included in processes of technology design and policymaking. Enabling nursing professionals to shape the circumstances of a digitally enhanced holistic practice may empower their status within the healthcare system and also benefit the patient by contributing to a more person‐centred care. [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 describes the development of an integrated intelligent (expert) system and conventional information system to support decision making of a diagnostic decision in a multi-agent environment. It argues that many a priori requirements for the information system can be elicited by accounting for the specific nature of the diagnostic decision and for the single/multiple-actor environment. This finding is generalized to a framework that indicates that a priori requirements take into account the basic theories on which the information system supporting the decision task is ultimately based. In particular, attention is focused on the nature of the task (e.g. diagnostic), whether the application is for a single- or multiple-agent situation and the nature of the multi-agent structure (e.g. product or functional form of organization). [ABSTRACT FROM AUTHOR]

It has been argued in the literature that although specific types of computer-based information systems (CBIS) are powerful tools in certain parts of the decision-making processes in modern organisations, none of them provides integrated support. This lack becomes even more acute when one considers the operation of CBIS within an organisational setting where consistent assistance of a multitude of users in different departments is required, together with facilities for modelling such features as cooperation, conflict, negotiation, etc. On the other hand, there are indications that the incorporation of abilities such as perception, interpretation, reasoning, explanation, goal-setting, and learning in computer-aided support would greatly enhance the aforementioned decision-making processes. This paper reviews the evolution of CBIS, and attempts to synthesise current research towards the goal of competent and intelligent aiding of decision-making in organisational settings. The main argument of the paper is that decision-making functions in multi-participant organisations can be facilitated by the use of intelligent software entities with autonomous processing capabilities which possess coordination and negotiation facilities and are organised in distributed, hierarchical societies. This paper presents a conceptual definition of these entities, outlines their structural characteristics, and describes a framework for research towards their development. [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]

Because of the environments in which they will operate, future autonomous systems must be capable of reconfiguring quickly and safely following faults or environmental changes. Past research has shown how, by considering autonomous systems to perform phased missions, reliability analysis can support decision making by allowing comparison of the probability of success of different missions following reconfiguration. Binary decision diagrams (BDDs) offer fast, accurate reliability analysis that could contribute to real-time decision making. However, phased mission analysis using existing BDD models is too slow to contribute to the instant decisions needed in time-critical situations. This paper investigates 2 aspects of BDD models that affect analysis speed: variable ordering and quantification efficiency. Variable ordering affects BDD size, which directly affects analysis speed. Here, a new ordering scheme is proposed for use in the context of a decision-making process. Variables are ordered before a mission, and reordering is unnecessary no matter how the mission configuration changes. Three BDD models are proposed to address the efficiency and accuracy of existing models. The advantages of the developed ordering scheme and BDD models are demonstrated in the context of their application within a reliability analysis methodology used to support decision making in an unmanned aerial vehicle. [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]

Dental trauma in children and adolescents is a common problem, and the prevalence of these injuries has increased in the last 10-20 years. A dental injury should always be considered an emergency and, thus, be treated immediately to relieve pain, facilitate reduction of displaced teeth, reconstruct lost hard tissue, and improve prognosis. Rational therapy depends upon a correct diagnosis, which can be achieved with the aid of various examination techniques. It must be understood that an incomplete examination can lead to inaccurate diagnosis and less successful treatment. Good knowledge of traumatology and models of treatments can also reduce stress and anxiety for both the patient and the dental team. Knowledge-based Systems (KBS) are a practical implementation of Artificial Intelligence. In complex domains which humans find difficult to understand, KBS can assist in making decisions and can also add knowledge. The aim of this paper is to describe the structure of a knowledge-based system for structured examination, diagnosis and therapy for traumatised primary and permanent teeth. A commercially available program was used as developmental tool for the programming (XpertRule, Attar, London, UK). The paper presents a model for a computerised decision support system for traumatology. [ABSTRACT FROM AUTHOR]

This paper describes INSOLVE--an expert system for corporate recovery decisions. INSOLVE was built to understand the decision-making processes of corporate recovery experts who deal with companies in financial difficulties. INSOLVE has been developed using a multi-phase process similar to that widely adopted in software engineering. The expert system is described in terms of the assessment task and interpretation models of CommonKADS. The detailed results of the validation of INSOLVE with 17 experts show that it is an accurate model of human expertise in this domain. [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]

Question answering has emerged as an intuitive way of querying structured data sources and has attracted significant advancements over the years. A large body of recent work on question answering over knowledge graphs (KGQA) employs neural network‐based systems. In this article, we provide an overview of these neural network‐based methods for KGQA. We introduce readers to the formalism and the challenges of the task, different paradigms and approaches, discuss notable advancements, and outline the emerging trends in the field. Through this article, we aim to provide newcomers to the field with a suitable entry point to semantic parsing for KGQA, and ease their process of making informed decisions while creating their own QA systems. This article is categorized under:Technologies > Machine LearningTechnologies > PredictionTechnologies > Artificial Intelligence [ABSTRACT FROM AUTHOR]

The wide availability of computer technology and large electronic storage media has led to an enormous proliferation of databases in almost every area of human endeavour. This naturally creates an intense demand for powerful methods and tools for data analysis. Current methods and tools are primarily oriented toward extracting numerical and statistical data characteristics. While such characteristics are very important and useful, they are often insufficient. A decision maker typically needs an interpretation of these findings, and this has to be done by a data analyst. With the growth in the amount and complexity of the data, making such interpretations is an increasingly difficult problem. As a potential solution, this paper advocates the development of methods for conceptual data analysis. Such methods aim at semi-automating the processes of determining high-level data interpretations, and discovering qualitative patterns in data. It is argued that these methods could be built on the basis of algorithms developed in the area of machine learning. An exemplary system utilizing such algorithms, INLEN, is discussed. The system integrates machine learning and statistical analysis techniques with database and expert system technologies. Selected capabilities of the system are illustrated by examples from implemented modules. [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]

Of all of the new technologies emerging in the late 20th century, the production of artificial intelligence may provide the most profound impacts on organizational decision making. Because the development of artificial intelligence technologies and models has largely been based on psychological models of human cognition, the effects of their implementation in complex social settings have not been thoroughly examined. This paper is an attempt to generate research which will develop a comprehensive understanding of the impacts of artificial intelligence and its role in complex organizations. A set of 11 hypotheses has been developed which examine the relationships between artificial intelligence technologies and the dimensions of organizational decision making. It is argued here that the implementation of expert systems will lead to less complex and political decision processes, while the implementation of natural language systems will lead to more complex and political decision processes. [ABSTRACT FROM AUTHOR]

New developments in technology have increased the computing power available for problem solving and have made technology and algorithms much more accessible by providing new ways of communicating with models and tools. Supporting decision makers in complex problems involving multiple stakeholders, multiple criteria, uncertainty and risk or other factors has been an important goal of Operations Research since the field emerged. [Extracted from the article]

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]

SUMMARY This paper summarizes the body of research on the development of a practical approach for conceptual performance-based design, and the semi-graphical design decision support system (DDSS) devised for its application. The objective of the study presented here is to improve the previous implementation of DDSS, which was based on representing losses at the building-level, to a more refined form in which losses at the story/floor level are addressed. DDSS provides engineers with insight in making informed decisions on the choice of an effective structural system and its stiffness (period), base shear strength, and other important global structural parameters for target performance objectives, which are defined by placing limits on direct (monetary) loss and probability of collapse. A byproduct of this performance-based design process is that it can be used as simplified performance-based assessment process by only changing the flow of information from ground motion hazard to loss estimation. The DDSS proposed in this study is supported with a database of structural response parameters obtained for a wide-range of combination in structural parameters of frame and wall structural systems. The proposed process is illustrated in an example to demonstrate its practicality. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

ABSTRACT Decision aiding can be abstractly described as the process of assisting a user/client/decision maker by recommending possible courses of his action. This process has to be able to cope with incomplete and/or inconsistent information and must adapt to the dynamics of the environment in which it is carried out. Indeed, on the one hand, complete information about the environment is almost impossible, and on the other hand, the information provided by the user is often affected by uncertainty; it may contains inconsistencies and may dynamically be revised because of various reasons. The aim of this paper is to present a model of the decision aiding process that is amenable to automation. The main features of the approach is that it models decision aiding as an iterative defeasible reasoning process, and it uses argumentation for capturing important aspects of the process. More specifically, argumentation is used for representing the relations between the cognitive artefacts that are involved in decision aiding, as well as for modelling the artefacts themselves. In modelling the cognitive artefacts, we make use of the notion of argument schemes and specify the related critical questions. More specifically, the work reported here aims at initiating a systematic study of the use of argumentation in future decision aiding tools. Our ambition is twofold: (i) enhance decision support capabilities of an analyst representing explicitly and accountably the reasons for which he recommend a solution for a decision maker and (ii) enhance decision support capabilities of an (semi) automatic device to handle (at least partially) the dialogue with the user. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Exploring the laws of the nature and the rules of human society is the grand challenge of sciences. The Internet, Web, various communication networks and digital devices are connecting each other to form an enormous cyber space. The cyber space gradually connects the natural physical space via various cyber-physical interfaces to form Cyber Physical Systems. Interacting with humans, Cyber Physical Systems will connect human society to form a new world-Cyber Physical Society. Humans' daily life and work will become increasingly relying on the new world. Billions of years' evolution creates the most elegant and efficient natural ecosystem. It will be ideal if the Cyber Physical Society can be an ecosystem that harmoniously evolves with society, economy, culture, sciences and technologies. The natural ecosystem gives us inspiration to ensure the harmony of the Cyber Physical Society. From ecosystem point of view, this paper proposes the notion, method, scientific issues, general architecture and resource management of Cyber Physical Society. The study of the Cyber Physical Socio Ecology (CPSE) could lead to a new branch of sciences. Copyright © 2010 John Wiley & Sons, Ltd. [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]

Collaborative spatial decision support systems (C-SDSS) have been used to help groups of stakeholders understand data and search for opportunities at resolving local and regional decision problems in various domains including land use, trans- portation, and water resources. The key issue in designing an effective C-SDSS is the anticipation of user information needs. Knowledge of user information needs can guide system designers in achieving a C-SDSS that fits the decision process. In this paper we present a design approach that is informed by stakeholder concerns, as part of a user needs assessment. The approach is based on the premise that knowing stakeholders’ concerns can help anticipate user information needs and consequently lead to a more usable C-SDSS. We demonstrate the approach with the example of a spatio-temporal decision problem involving conjunctive water administration in the Boise River Basin in southwestern Idaho. The spatial dimension of the decision task involves delineating the areas of conjunctive water administration while the temporal dimension involves selecting the year in which a given area will start to be administered. We show how the elicitation of stakeholder concerns leads to functional specification of a collaborative spatio-temporal decision support system. [ABSTRACT FROM AUTHOR]

This paper presents the development of an integrated system, namely the COMAND-DSS (Decision Support System created under project COMAND), for crisis management and real-time decision support in shipboard flooding emergencies. It employs the case-based reasoning approach to provide swift and effective decision-making support for shipboard personnel during an emergency on ship. The developed system, which focuses on decision support for flooding control, is also thought to be useful as a crew training system. In order to deal with a flooding damage in the COMAND-DSS, a multiple attribute decision-making methodology—Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)—is used to be able to choose the set of counter-flooding tanks that should be filled in order to achieve an optimal response to a flooding accident. The COMAND-DSS is composed of a knowledge base, a reasoning process and a graphical user interface for the shipboard personnel to interact with the system. The friendly windows-based user interface of the COMAND-DSS enables the user to take full advantage of the capabilities of the system in order to make quick decisions. Using this system, when a crisis occurs the optimal decisions and responses can be made in a short time, thereby reducing the risk to life, property and environment. Copyright © 2006 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Automated decision-making systems have been deployed in many industrial, commercial, and financial applications. The needs for such systems are usually motivated by requirements for variation reduction, capacity increase, cost and cycle time reduction, and end-to-end traceability of the transaction or product. Before we can use any automated decision-making system in a production environment we must develop a strategy to insure high quality throughout its entire lifecycle. We need to guarantee its performance through a rigorous Design for Six Sigma process (DFSS). This process includes validation, tuning, and production testing of the system. Once the system is in production we must monitor and maintain its performance over its lifecycle. In this paper we will outline the Six Sigma process that led to the deployment of an automated decision-making system in one of the General Electric Financial Assurance businesses. Copyright © 2005 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Over the past three decades, significant improvements in the computer and computational sciences have enabled automated support for increasingly complex decision situations. One example of this progress is the influence diagram, which is simultaneously a graphical and mathematical model of a decision situation. Influence diagrams are a proven asset in the tool kit of decision analysts and they are making the power of decision analytic modelling more accessible to professionals in other disciplines. To develop influence diagrams, decision analysts guide decisionmakers and subject matter experts through a discovery process that necessarily migrates from the unstructured to the structured. These highly complex decision situations often require inputs from many different people with diverse expertise. One shortcoming of influence diagrams (and other methods) is that they do not specify a cohesive and comprehensive process for structuring the interactions of options, values, and uncertainties during the initial development of a decision model. This complicates (and sometimes precludes) the process of developing a comprehensive model because the model reaches a level of complexity that is very difficult for individual domain experts to think about. This paper introduces a formally specified method for eliciting influence diagram structure. We extend influence diagram notation to include special categories of value nodes that more explicitly define fundamental objectives hierarchy components. We then provide provably correct methods for decomposing the model, eliciting additional detail, and reassembling the model into a single graph. These methods make two important contributions to the modelling science: first, it is a necessary step toward providing automated model elicitation tools; second, it is an example of technology transfer from Bayesian Belief Networks to Influence Diagrams. Copyright © 2005 John Wiley & Son, Ltd. [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]

In this paper we propose a method to enhance the performance of knowledge-based decision-support systems, knowledge of which is volatile and incomplete by nature in a dynamically changing situation, by providing meta-knowledge augmented by the Qualitative Reasoning (QR) approach. The proposed system intends to overcome the potential problem of completeness of the knowledge base. Using the deep meta-knowledge incorporated into the QR module, along with the knowledge we gain from applying inductive learning, we then identify the ongoing process and amplify the effects of each pending process to the attribute values. In doing so, we apply the QR models to enhance or reveal the patterns which are otherwise less obvious. The enhanced patterns can eventually be used to improve the classification of the data samples. The success factor hinges on the completeness of the QR process knowledge base. With enough processes taking place, the influences of each process will lead prediction in a direction that can reflect more of the current trend. The preliminary results are successful and shed light on the smooth introduction of Qualitative Reasoning to the business domain from the physical laboratory application. [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]