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In order to improve the performance of UAV's autonomous maneuvering decision-making, this paper proposes a decision-making method based on situational continuity. The algorithm in this paper designs a situation evaluation function with strong guidance, then trains the Long Short-Term Memory (LSTM) under the framework of Deep Q Network (DQN) for air combat maneuvering decision-making. Considering the continuity between adjacent situations, the method takes multiple consecutive situations as one input of the neural network. To reflect the difference between adjacent situations, the method takes the difference of situation evaluation value as the reward of reinforcement learning. In different scenarios, the algorithm proposed in this paper is compared with the algorithm based on the Fully Neural Network (FNN) and the algorithm based on statistical principles respectively. The results show that, compared with the FNN algorithm, the algorithm proposed in this paper is more accurate and forward-looking. Compared with the algorithm based on the statistical principles, the decision-making of the algorithm proposed in this paper is more efficient and its real-time performance is better. [ABSTRACT FROM AUTHOR]

The necessity of parallel programming for leveraging the full potential of modern multicore processors cannot be overstated. One of the fundamental algorithms used extensively across a multitude of fields is the array sum reduction algorithm. This algorithm is pivotal in scenarios requiring the aggregation of array elements, a task that can benefit significantly from parallel processing techniques. Implementing the array sum reduction algorithm on a multiprocessor system can be approached using various parallel programming models. This paper delves into a comparative analysis of two prominent parallel programming approaches for the array sum reduction algorithm on a shared memory microprocessor environment. We explore the implementation and performance characteristics of C with OpenMP, a well-established and widely adopted parallel programming model in the C programming ecosystem, and C# with Parallel.For, a robust and flexible parallel programming framework within the .NET environment. Our analysis involves a rigorous empirical evaluation and benchmarking process to assess the execution efficiency, scalability, and ease of implementation associated with each approach. By systematically examining these factors, we aim to elucidate the specific contexts and application scenarios where each parallel programming paradigm excels or may present limitations. Furthermore, we consider the development overhead and learning curve associated with adopting these technologies, providing a holistic view of their practicality in real-world applications. In addition to performance metrics, we examine the debugging and maintenance complexities that developers may encounter when working with these paradigms. We also assess the extent to which each approach supports advanced parallelism features such as task parallelism and load balancing. Another important aspect of our study is the interoperability of these programming models with other tools and libraries commonly used in high-performance computing. Through this comparative study, we seek to provide developers and researchers with a comprehensive understanding of the trade-offs involved in selecting between C with OpenMP and C# with Parallel.For. The insights garnered from our evaluation will aid in making informed decisions, ensuring optimal performance and resource utilization in parallel programming applications tailored to modern multicore processors. Ultimately, our findings contribute to the broader discourse on parallel computing best practices and the future directions of parallel algorithm development. Our results aim to serve as a reference point for future research, highlighting potential areas for enhancement in both programming models. We hope to inspire further innovation in parallel computing techniques, fostering advancements that can lead to more efficient and scalable software solutions. [ABSTRACT FROM AUTHOR]

At the heart of this paper is an examination of the colloquial concept of a 'shadow ban'. It reveals ways in which algorithms on the Facebook platform have the effect of suppressing content distribution without specifically targeting it for removal, and examines the consequential stifling of users' speech. It reveals how the Facebook shadow ban is implemented by blocking dissemination of content in News Feed. The decision-making criteria are based on 'behaviour', a term that relates to activity of the page that is identifiable through patterns in the data. It's a technique that is rooted in computer security, and raises questions about the balance between security and freedom of expression. The paper is situated in the field of responsibility of online platforms for content moderation. It studies the experience of the shadow ban on 20 UK-based Facebook Pages over the period from November 2019 to January 2021. The potential harm was evaluated using human rights standards and a comparative metric produced from Facebook Insights data. The empirical research is connected to recent legislative developments: the EU's Digital Services Act and the UK's Online Safety Bill. Its most salient contribution may be around 'behaviour' monitoring and its interpretation by legislators. [ABSTRACT FROM AUTHOR]

This paper presents a new perspective on the problem of bias in artificial intelligence (AI)-driven decision-making by examining the fundamental difference between AI and human rationality in making sense of data. Current research has focused primarily on software engineers' bounded rationality and bias in the data fed to algorithms but has neglected the crucial role of algorithmic rationality in producing bias. Using a Weberian distinction between formal and substantive rationality, we inquire why AI-based algorithms lack the ability to display common sense in data interpretation, leading to flawed decisions. We first conduct a rigorous text analysis to uncover and exemplify contextual nuances within the sampled data. We then combine unsupervised and supervised learning, revealing that algorithmic decision-making characterizes and judges data categories mechanically as it operates through the formal rationality of mathematical optimization procedures. Next, using an AI tool, we demonstrate how formal rationality embedded in AI-based algorithms limits its capacity to perform adequately in complex contexts, thus leading to bias and poor decisions. Finally, we delineate the boundary conditions and limitations of leveraging formal rationality to automatize algorithmic decision-making. Our study provides a deeper understanding of the rationality-based causes of AI's role in bias and poor decisions, even when data is generated in a largely bias-free context. [ABSTRACT FROM AUTHOR]

Decision making and path planning in case of highly transient dynamics of the surrounding as well as the effect of road condition are the issues that are not completely solved in the previous researches. The goal is to perform a safe and comfortable lane change that includes flexible re-planning capabilities. In this paper, a novel structure for path planning and decision making part of a vehicle automatic lane change has been introduced which comprehensively considers both longitudinal and lateral dynamics of the vehicle. The presented method is able to perform re-planning even in the middle of a lane change maneuver according to new traffic condition. Inclusion of the dynamics of all involved vehicles and providing online performance are the other advantages of the proposed system. The algorithm is simulated and various scenarios are constructed to evaluate the efficiency of the system. The results show that the system has completely acceptable performance. [ABSTRACT FROM AUTHOR]

This paper aims to extend the concept of Neutrosophic Hypersoft Matrix (NHSM) theory. NHSM is the matrix representation of a Neutrosophic Hypersoft Set (NHSS), where NHSS is the combination of a Neutrosophic set and a Hypersoft set. An NHSS can be stored in computer memory using the matrix notion, which is very useful and applicable. Based on NHSM, we provide some new notions (operations) such as NHS-sub-matrix, Equal NHSM, Null NHSM, Universal NHSM, Complement NHSM, NH-choice matrix (NHCM), product of NHCM and combined NHCM along with examples and characterizations. Additionally, we develop an NHSM algorithm using a value matrix, grace matrix, and mean matrix to solve a decision-making problem based on NHSSs. Finally, the NHSM algorithm was used to select the critically suffering chronic kidney disease (CKD) patient. [ABSTRACT FROM AUTHOR]

Continuity of the objective functions and compactness of their domain are classical assumptions widely used to obtain existence results for solutions of optimization problems. Due to the lack of compactness in general spaces, under some moderate assumptions concerning the objective function and the feasible set, we derive existence results for vector-valued optimization problems and corresponding results for associated scalarized problems in this paper. Furthermore, we apply our results to special vector-valued approximation problems, especially to multi-objective location problems where the whole set of efficient solutions can be generated by a geometric primal-dual algorithm. Moreover, by using the nonlinear scalarizing functional introduced by Gerstewitz, we perform an optimization according to the preferences of a decision maker on the generated set of efficient solutions from which we derive a single solution of this set that corresponds to the preferences of the decision maker. [ABSTRACT FROM AUTHOR]

Reinforcement learning (RL) is a subdomain of machine learning concerned with achieving optimal behavior by interacting with an unknown and potentially stochastic environment. The exploration strategy for choosing actions is an important component for enabling the decision agent to discover how to obtain high rewards. If constructed well, it may reduce the learning time of the decision agent. Exploration in discrete problems has been well studied, but there are fewer strategies applicable to continuous dynamics. In this paper, we propose a Low-Discrepancy Action Selection (LDAS) process, a novel exploration strategy for environments with continuous states and actions. This algorithm focuses on prioritizing unknown regions of the state-action space with the intention of finding ideal actions faster than pseudo-random action selection. Results of experimentation with three benchmark environments elucidate the situations in which LDAS is superior and introduce a metric for quantifying the quality of exploration. [ABSTRACT FROM AUTHOR]

Aiming at the problem of multi-UAV pursuit-evasion confrontation, a UAV cooperative maneuver method based on an improved multi-agent deep reinforcement learning (MADRL) is proposed. In this method, an improved CommNet network based on a communication mechanism is introduced into a deep reinforcement learning algorithm to solve the multi-agent problem. A layer of gated recurrent unit (GRU) is added to the actor-network structure to remember historical environmental states. Subsequently, another GRU is designed as a communication channel in the CommNet core network layer to refine communication information between UAVs. Finally, the simulation results of the algorithm in two sets of scenarios are given, and the results show that the method has good effectiveness and applicability. [ABSTRACT FROM AUTHOR]

Bionic robots possess inherent advantages for underwater operations, and research on motion control and intelligent decision making has expanded their application scope. In recent years, the application of reinforcement learning algorithms in the field of bionic underwater robots has gained considerable attention, and continues to grow. In this paper, we present a comprehensive survey of the accomplishments of reinforcement learning algorithms in the field of bionic underwater robots. Firstly, we classify existing reinforcement learning methods and introduce control tasks and decision making tasks based on the composition of bionic underwater robots. We further discuss the advantages and challenges of reinforcement learning for bionic robots in underwater environments. Secondly, we review the establishment of existing reinforcement learning algorithms for bionic underwater robots from different task perspectives. Thirdly, we explore the existing training and deployment solutions of reinforcement learning algorithms for bionic underwater robots, focusing on the challenges posed by complex underwater environments and underactuated bionic robots. Finally, the limitations and future development directions of reinforcement learning in the field of bionic underwater robots are discussed. This survey provides a foundation for exploring reinforcement learning control and decision making methods for bionic underwater robots, and provides insights for future research. [ABSTRACT FROM AUTHOR]

This paper investigates the path planning method of unmanned aerial vehicle (UAV) in threedimensional map. Firstly, in order to keep a safe distance between UAV and obstacles, the obstacle grid in the map is expanded. By using the data structure of octree, the octree map is constructed, and the search nodes is significantly reduced. Then, the lazy theta* algorithm, including neighbor node search, line-of-sight algorithm and heuristics weight adjustment is improved. In the process of node search, UAV constraint conditions are considered to ensure the planned path is actually flyable. The redundant nodes are reduced by the line-of-sight algorithm through judging whether visible between two nodes. Heuristic weight adjustment strategy is employed to control the precision and speed of search. Finally, the simulation results show that the improved lazy theta* algorithm is suitable for path planning of UAV in complex environment with multi-constraints. The effectiveness and flight ability of the algorithm are verified by comparing experiments and real flight. [ABSTRACT FROM AUTHOR]

Green treatment on Waste Electrical and Electronic Equipmenthas increasingly attracted attention due to its significant environmental benefits and potential recovery earnings. Automated disassembly has been regarded as a powerful solution to enable more efficient recovery operations. Although numerous studies have contributed to the issues of disassembly, there are few researches that focus on decision model for selecting disassembly system scheme and recovery route in automated disassembly. In this paper, we propose a two-phase joint decision-making model to address this problem with the goal of balancing disassembly profit with environmental impact. First, we establish a multi-objective optimisation model to obtain the Pareto optimal recovery routes for each automated disassembly system scheme. Both recovery profit and energy consumption are evaluated for multi-station disassembly system. We design a multi-objective hybrid particle swarm optimisation algorithm based on symbiotic evolutionary mechanism to solve the proposed model. Then, we compare the Pareto optimal solutions of all the system schemes using a fuzzy set method and identify the best scheme. Finally, we conduct real case studies on the automated disassembly of different waste electric metres. The results demonstrate the superiority of automated disassembly and validate the effectiveness of our proposed model and algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we apply a computational model for multicriteria decision making in traffic jam problems. First, we propose a system to determine the optimal shortcut road by reading the number of cars in each street using Radio Frequency Identification (RFID). Then, we have processed the data of traffic jam problems using Choquet integral with writing algorithm and computer program as a working procedure. [ABSTRACT FROM AUTHOR]

Making decisions in complex environments is a key challenge in artificial intelligence (AI). Situations involving multiple decision makers are particularly complex, leading to computational intractability of principled solution methods. A body of work in AI has tried to mitigate this problem by trying to distill interaction to its essence: how does the policy of one agent influence another agent? If we can find more compact representations of such influence, this can help us deal with the complexity, for instance by searching the space of influences rather than the space of policies. However, so far these notions of influence have been restricted in their applicability to special cases of interaction. In this paper we formalize influence-based abstraction (IBA), which facilitates the elimination of latent state factors without any loss in value, for a very general class of problems described as factored partially observable stochastic games (fPOSGs). On the one hand, this generalizes existing descriptions of influence, and thus can serve as the foundation for improvements in scalability and other insights in decision making in complex multiagent settings. On the other hand, since the presence of other agents can be seen as a generalization of single agent settings, our formulation of IBA also provides a sufficient statistic for decision making under abstraction for a single agent. We also give a detailed discussion of the relations to such previous works, identifying new insights and interpretations of these approaches. In these ways, this paper deepens our understanding of abstraction in a wide range of sequential decision making settings, providing the basis for new approaches and algorithms for a large class of problems. [ABSTRACT FROM AUTHOR]

In control theory, to solve a finite-horizon sequential decision problem (SDP) commonly means to find a list of decision rules that result in an optimal expected total reward (or cost) when taking a given number of decision steps. SDPs are routinely solved using Bellman's backward induction. Textbook authors (e.g. Bertsekas or Puterman) typically give more or less formal proofs to show that the backward induction algorithm is correct as solution method for deterministic and stochastic SDPs. Botta, Jansson and Ionescu propose a generic framework for finite horizon, monadic SDPs together with a monadic version of backward induction for solving such SDPs. In monadic SDPs, the monad captures a generic notion of uncertainty, while a generic measure function aggregates rewards. In the present paper, we define a notion of correctness for monadic SDPs and identify three conditions that allow us to prove a correctness result for monadic backward induction that is comparable to textbook correctness proofs for ordinary backward induction. The conditions that we impose are fairly general and can be cast in category-theoretical terms using the notion of Eilenberg–Moore algebra. They hold in familiar settings like those of deterministic or stochastic SDPs, but we also give examples in which they fail. Our results show that backward induction can safely be employed for a broader class of SDPs than usually treated in textbooks. However, they also rule out certain instances that were considered admissible in the context of Botta et al. 's generic framework. Our development is formalised in Idris as an extension of the Botta et al. framework and the sources are available as supplementary material. [ABSTRACT FROM AUTHOR]

This paper conducts an analysis of various strategies for incorporating the headless chicken macromutation operator into a dynamic particle swarm optimization algorithm. Seven variations of the dynamic headless chicken guaranteed convergence particle swarm optimization algorithm are proposed and evaluated on a diverse set of single-objective dynamic benchmark problems. Competitive performance was demonstrated by the headless chicken PSO algorithms when compared to, amongst others, a quantum particle swarm optimization algorithm. [ABSTRACT FROM AUTHOR]

To manage issues with incompleteness, indeterminacy, and awareness of inconsistent information, Maji presented the idea of a neutrosophic soft set by merging the ideas of a neutrosophic set and a soft set. The generalized neutrosophic soft set (GNSS) is an extension of this idea, which has now been developed further. At the beginning of this paper, we describe the definition of a generalized neutrosophic soft set. Then, we focus on the concepts of GNSS operations, such as AND, OR, complement, intersection, and union, and provide illustrated examples to describe a number of associated properties. Finally, a description of an algorithm and an application that uses GNSS to address challenges that arise when making decisions that need the experience of more than one specialist is offered here. [ABSTRACT FROM AUTHOR]

This paper interrogates the fundamental logic of data-driven decision-making (DDDM) as it has taken hold in education and argues for a critical analysis of data-driven education via an attitude of historical ontology. Though influenced by Foucault's understanding of this concept, I center Colin Koopman's recent analysis of the 'informational person' to point attention to the ways in which the very formatting of data may be understood as historically contingent and, thus, more contestable. After examining the background of DDDM and relevant critiques of it, I suggest that investigating the socio-historical formations of data itself are important for indicating its potential dangers within education. Such a critical analysis identifies the doubled politics of data formatting, with its ability to tether subjectivity to data, while potentially baking modes of power into a variety of technological data apparatuses. To conclude, I consider Koopman's work in terms of how it may extend critical studies of educational datafication. [ABSTRACT FROM AUTHOR]

Purpose – The purpose of this paper is to maximize the expected revenue of the outpatient department considering patient preferences and choices. Design/methodology/approach – Patient preference refers to the preferred physician and time slot that patients hold before asking for appointments. Patient choice is the appointment decision the patient made after receiving a set of options from the scheduler. The relationship between patient choices and preferences is explored. A dynamic programming (DP) model is formulated to optimize appointment scheduling with patient preferences and choices. The DP model is transformed to an equivalent linear programming (LP) model. A decomposition method is proposed to eliminate the number of variables. A column generation algorithm is used to resolve computation problem of the resulting LP model. Findings – Numerical studies show the benefit of multiple options provided, and that the proposed algorithm is efficient and accurate. The effects of the booking horizon and arrival rates are studies. A policy about how to make use of the information of patient preferences is compared to other naive polices. Experiments show that more revenue can be expected if patient preferences and choices are considered. Originality/value – This paper proposes a framework for appointment scheduling problem in outpatient departments. It is concluded that more revenue can be achieved if more choices are provided for patients to choose from and patient preferences are considered. Additionally, an appointment decision can be made timely after receiving patient preference information. Therefore, the proposed model and policies are convenient tools applicable to an outpatient department. [ABSTRACT FROM AUTHOR]

Financial markets are regarded as one of the most popular investment options since they offer high returns in a short period of time. However, because of its highly volatile and unpredictable character, it is considered a dangerous investment option. As per popular estimates, up to 90% of those who engage in the stock market lose money, including both rookie and experienced traders. And the reasons behind this can include a lack of information, a lack of trading discipline, and decision-making based on emotion. As a result, the Algorithmic Trading System is an attempt to make the lives of investors easier through study and the development of profitable trading techniques by examining and evaluating market conditions. And trading with algorithms may be able to generate fair trades and more profitable chances with financial instruments. In this paper we have discussed various applications of algorithmic trading system. [ABSTRACT FROM AUTHOR]

The best-of-n problem (BSTn) is a collective decisionmaking problem in which a swarm of robots needs to make a collective decision about a set of n choices; specifically, to decide what choice offers the best alternative [1]. The BSTn captures the structure and logic of the discrete consensus achievement problems that appear in several swarm robotics scenarios. Although numerous algorithms have been proposed recently to deal with more than two choices, the number of choices that can be dealt with is not large. The bias and raising threshold (BRT) algorithm proposed by Phung et al. [2] enables swarms to deal with a large number of choices (n≫ 2). However, the algorithm's goodness has not been evaluated in any practical problems, and it is necessary to evaluate the algorithm in a problem where a large number of choices exist. In this paper, we consider the best of proportions (BOP) problem; that is a version of BSTn in which a large number of choices can be dealt with by adjusting the values of different proportions. In previous research on swarms that needed to solve the BOP problem, there is only a study on the response threshold models for the division of labor. In the present study, we investigate a scenario of the BOP and apply the BRT algorithm to find the best proportion. In our previous work [3], a fixed proportion setting method has been adopted. Here, we adopt a stochastic proportion setting method to verify the relationship between the efficiency and the number of choices in a more general case. The results show that with a larger number of choices, the decision making becomesmore efficient with high equality; that is a result that has not been found in [3]. [ABSTRACT FROM AUTHOR]

The paper presents the process of taking global decisions on the basis of the knowledge of local decision systems, in which sets of conditional attributes are different but not necessarily disjoint. We propose the organization of local decision systems into a multi-agent system with a hierarchical structure. The structure of multi-agent systems and the theoretical aspects of the organization of the system are presented. An editing and a condensing algorithm have been used in the process of global decision making. Also a density-based algorithm has been used in the process of taking global decisions to resolve conflicts. Furthermore, the paper presents the results of experiments conducted using some data sets from UCI repository. [ABSTRACT FROM AUTHOR]

The combination of increased availability of large amounts of fine-grained human behavioral data and advances in machine learning is presiding over a growing reliance on algorithms to address complex societal problems. Algorithmic decision-making processes might lead to more objective and thus potentially fairer decisions than those made by humans who may be influenced by greed, prejudice, fatigue, or hunger. However, algorithmic decision-making has been criticized for its potential to enhance discrimination, information and power asymmetry, and opacity. In this paper, we provide an overview of available technical solutions to enhance fairness, accountability, and transparency in algorithmic decision-making. We also highlight the criticality and urgency to engage multi-disciplinary teams of researchers, practitioners, policy-makers, and citizens to co-develop, deploy, and evaluate in the real-world algorithmic decision-making processes designed to maximize fairness and transparency. In doing so, we describe the Open Algortihms (OPAL) project as a step towards realizing the vision of a world where data and algorithms are used as lenses and levers in support of democracy and development. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of target detection in adaptive arrays in situations where only a small number of training samples is available. Within the framework of two-stage adaptive detection paradigm, the paper proposes a class of rapidly adaptive CFAR (Constant False Alarm Rate) detection algorithms, which are referred to as joint loaded persymmetric-Toeplitz adaptive matched filter (JLPT-AMF) detectors. A JLPT-AMF detector combines, using a joint detection rule, individual scalar CFAR decisions from two rapidly adaptive two-stage (TS) detectors: a TS TAMF detector and a TS LPAMF detector. The former is based on a TMI filter, which is an adaptive array filter employing a Toeplitz covariance matrix (CM) estimate inversion. The latter is based on an adaptive LPMI filter that uses diagonally loaded persymmetric CM estimate inversion. The proposed class of adaptive detectors may incorporate any rapidly adaptive TS TAMF and TS LPAMF detectors, which, in turn, may employ any scalar CFAR detection algorithms that satisfy an earlier derived linearity condition. The two-stage adaptive processing structure of the JLPT-AMF detectors ensures the CFAR property independently of the antenna array dimension M, the interference CM, and the number of training samples NCME to be used for estimating this CM. Moreover, the rapidly adaptive JLPT-AMF detectors exhibit highly reliable detection performances, which are robust to the angular separation between the sources, even when NCME is about m/2 ~ m, m is the number of interference sources. The robustness is analytically proven and verified with statistical simulations. For several representative scenarios when the interference CM has m dominant eigenvalues, comparative performance analysis for the proposed rapidly adaptive detectors is provided using Monte-Carlo simulations. [ABSTRACT FROM AUTHOR]

The paper presented the results of research on the choice of management approaches to ensure the effective functioning of the environmental management system of business entities. New challenges contribute to the formation of a new system of approaches to decision-making. Implementation of environmental management systems is conducted in accordance with the requirements of the international standard ISO 14001: 2015. Effective EMS is based on a balanced choice of management approaches, the main objective of which is to ensure environmental performance at each stage of the EMS implementation. The clear control of indicators of environmental-organizational changes and environmental performance criteria of environmental management system requires the formation of a set of complementary management approaches that promote productive decision-making to reduce risks and prevent criticality of the environment. The result of the study is the development of an algorithm of the procedure for selecting effective management approaches. The formation of structural model of approaches in the process of implementation and functioning of the environmental management system has helped to identify the organizational activities that ensure the environmental safety of business entities. A thorough analysis of each stage of the development, implementation and operation of environmental management system enabled identifying a set of management approaches, forming a vector of environmental and organizational changes in the activities of business entities. Taking into account the developed algorithm of the procedure of selecting managerial approaches and the conceptual model of assessing managerial approaches to the effective functioning of the environmental management system, a structural model of applying managerial approaches at each stage of implementation and functioning of the EMS to improve the environmental safety of business entities was built. The study pertaining to the stages of structural model and application of QFD-methodology (Quality Function Deployment) allowed building a matrix diagram, which defines a vector of orientation of approaches, taking into account their complexity and materiality. [ABSTRACT FROM AUTHOR]

The subject of this paper is to provide a robust user satisfaction evaluation of an educational distance-learning platform with the use of multicriteria satisfaction analysis (MUSA), an innovative and consistent decision-making algorithm, which leads to analytical satisfaction charts and improvement action charts. The educational platform evaluated is Moodle. MUSA algorithm criteria used for the purposes of the present analysis are: (1) technical dimension, (2) possibilities of teachers, (3) possibilities of participants, (4) pedagogical dimension, and (5) automated functions. The originality of this re-search is the fact that MUSA algorithms criteria weights are calculated both for the total number of participants in the present study and for smaller sample subgroups, which represent various levels of satisfaction (above average grade represents overall satisfied users and below average grade represents overall dissatisfied users), age, gender and identity (teachers or university students). The selected cluster sampling leads to differentiated criteria weights and action diagram in MUSA algorithm. The selected methodology is a crucial step for the optimization of the existing user satisfaction algorithm and leads to more robust and valid results. As a result, the modified method is called cluster sampling MUSA algorithm (CSMUSA) and leads to an enhanced decision-making procedure, which is considered fundamental for the constant improvement of any educational platform and software and could be implemented by software companies during the design process. [ABSTRACT FROM AUTHOR]

When several radio access technologies (e.g., HSPA, LTE, WiFi, and WiMAX) cover the same region, deciding to which one mobiles connect is known as the Radio Access Technology (RAT) selection problem. To reduce network signaling and processing load, decisions are generally delegated to mobile users. Mobile users aim to selfishly maximize their utility. However, as they do not cooperate, their decisions may lead to performance inefficiency. In this paper, to overcome this limitation, we propose a network-assisted approach. The network provides information for the mobiles to make more accurate decisions. By appropriately tuning network information, user decisions are globally expected to meet operator objectives, avoiding undesirable network states. Deriving network information is formulated as a semi-Markov decision process (SMDP), and optimal policies are computed using the Policy Iteration algorithm. Also, and since network parameters may not be easily obtained, a reinforcement learning approach is introduced to derive what to signal to mobiles. The performances of optimal, learning-based, and heuristic policies, such as blocking probability and average throughput, are analyzed. When tuning thresholds are pertinently set, our heuristic achieves performance very close to the optimal solution. Moreover, although it provides lower performance, our learning-based algorithm has the crucial advantage of requiring no prior parameterization. [ABSTRACT FROM AUTHOR]

This paper investigates the use of a Computer-Based Team Selection Algorithm (CBTSA) in a capstone project course involving Information Systems, Human Resources, and Supply Chain/Operations undergraduate students. Data from three semesters of the course before this genetic algorithm was put into use are compared to data from three semesters after it was adopted. More than 50 % of the teams formed by the CBTSA were able to meet in part or in whole at times arranged by the CBTSA. Students in the CBTSA-formed teams experienced considerably less general time, procrastination and motivation problems, or specific scheduling conflicts. The percentage of students reporting that the team had to change its meeting schedule to accommodate them dropped to zero percent for the CBTSA-formed teams. Fewer CBTSA-formed teams seemed to break down with one or more low performing members. However, 42 % of the CBTSA-formed teams were created such that there were few really open meeting slots for every member. Some improvements to the genetic algorithm used in the CBTSA are investigated, but regardless of improvements, it is difficult to provide every team with rich meeting times and still achieve parity for the teams on a number of other criteria. [ABSTRACT FROM AUTHOR]

This paper considers a Markov decision process in Borel state and action spaces with the aggregated (or say iterated) coherent risk measure to be minimised. For this problem, we establish the Bellman optimality equation as well as the value and policy iteration algorithms, and show the existence of a deterministic stationary optimal policy. The cost function, while being allowed to be unbounded from below (in the sense that its negative part needs be bounded by some nonnegative real-valued possibly unbounded weight function), can be arbitrarily unbounded from above and possibly infinitely valued. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to propose a method to estimate missing preference values when handling incomplete linguistic preference relations assessed by using 2-tuple linguistic preference values. We assert that additive transitivity alone may result in estimated values that do not qualify as 2-tuple linguistic preference values. Therefore, an upper bound condition based on additive transitivity is proposed to deal with incomplete 2-tuple linguistic preference relations (2TLPRs). We prove that if experts with incomplete information abide by this property, then the missing preference values can be estimated such that they are expressible. Additionally, because of this property, the resultant complete 2TLPR is proved to be consistent. Based on additive transitivity and the upper bound condition, an interactive completing algorithm is developed to estimate the missing entries in incomplete 2TLPR. When the upper bound condition is not satisfied or there exist entries that do not coincide with the real opinions of the experts, the proposed algorithm can automatically adjust the known entries by interacting with the experts. By using the proposed algorithm, the completed 2TLPR is not only additively consistent, but also can accurately reflect the actual preference of the experts. [ABSTRACT FROM AUTHOR]

Next generation multimode terminals have the capability to support different classes of calls simultaneously as well as the ability to connect to two or more radio access technologies (RATs), at the same time, in a heterogeneous wireless network. For a mobile terminal having multiple classes of simultaneous handoff calls (such as file download and video sessions), RAT selection decisions can be made independently for individual calls in the group or jointly for the entire group of calls. Both independent and group RAT selection decisions for multiple calls have advantages and disadvantages. Existing RAT selection algorithms have focused on RAT selection decisions for single calls. Therefore, this paper investigates independent call and group call RAT selection decisions for multiple calls in heterogeneous wireless networks, and proposes a scheme that makes RAT selection decisions for multiple calls based on a consensus level among the multiple calls to be admitted. When this consensus level is among multiple calls to be admitted into a particular RAT and is equal to or above a certain threshold value, a group decision is used. Otherwise, independent decisions are made. The performance of the proposed RAT selection scheme is evaluated in a three service three RAT heterogeneous network, supporting multihomed terminals. Simulation results are given to show the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

The problem of manufacturing process optimization is one of the most addressed issues across literature concerning the manufacturing field. Until now, this optimization has been based on indirect description of manufacturing, through prefabricated algorithmic models (be they analytical or numerical), as well as on information processing, by composing this kind of models. This is why the optimization is difficult and complicated, even in the case of the simplest processes. This paper presents a new approach of the manufacturing optimization concept, having as main defining aspects: i) The manufacturing process is regarded as decisional instead of physical process, ii) The optimization object is the manufacturing task, defined as product transition from current state up to final state, iii) The manufacturing model consists in a dataset, and, iv) The optimization problem solution consists in task configuring, task accomplishment, and task modeling. According to the new approach, the optimization result consists in the ensemble formed by an optimal structure (optimal configuration of the given task), an optimal process (optimal values of the process variables), and an optimal model (obtained by iterative rebuilding of the manufacturing model). The application of the proposed approach is sampled through a comparative illustration. [ABSTRACT FROM AUTHOR]

Active digitization of people's daily life leads to the use of the decision-making support systems (DMSS). DMSS is actively used in data processing, forecasting the course of various processes, providing informational support for the decision-making process by decision makers. However, a number of problems arise while evaluating monitoring objects, namely: a large number of destabilizing factors affecting the efficiency of the processes of information collection, processing and transmission; high dynamism of changes in the state and composition of heterogeneous monitoring objects during the conduct of hostilities (operations); high dynamism of conducting hostilities (operations); the uncertainty of the initial situation and the noise of the initial data. In this article, a method of finding solutions based on an improved bee colony algorithm was developed. The efficiency of information processing is achieved by learning the architecture of artificial neural networks; taking into account the type of uncertainty of the information to be evaluated; the use of an improved algorithm of the bee colony, the use of an unordered linguistic scale of measurements with adjustment coefficients for the degree of awareness and the degree of noise of the initial data. An approbation of the use of the proposed method was carried out on the example of assessing the state of the operational grouping of troops (forces). The method is proposed to be used in the development of software for automated systems of control of troops and weapons, namely, in the modernization of existing and development of new automated systems of control of troops and weapons. The evaluation of the effectiveness of the proposed method showed an increase in the efficiency of the evaluation at the level of 21-28 % in terms of the efficiency of information processing. [ABSTRACT FROM AUTHOR]

Decision-making on discrete event systems with alternative structural configurations is a field with application to the efficient design and operation of many systems, ranging from manufacturing facilities to communication networks. The solution of this problem may be afforded by its transformation into an optimization problem. A variety of statements for this optimization problem can be presented by using different formalisms able to describe the model of the system. These different statements allow developing diverse optimization algorithms for solving the problem, which may be very demanding for a computer. In this paper, several approaches are presented in order to reduce the computing requirements needed by the mentioned algorithms, some of them are implemented in one processor and others are based on distributed computing. In particular, this paper presents a new distributed methodology, which associates sets of alternative structural configurations of the system to different alternative aggregation Petri net (AAPNs), regarding the number of available processors. Under certain conditions, this methodology alleviates the computational requirements for every processor and speeds up the optimization process. A case-study is presented and different techniques are applied to solve it, for illustrating diverse distributed and non-distributed methodologies, regarding the available processors, as well as for comparing their relative performance. [ABSTRACT FROM PUBLISHER]

In this paper, we present a method for explaining the results produced by dynamic programming (DP) algorithms. Our approach is based on retaining a granular representation of values that are aggregated during program execution. The explanations that are created from the granular representations can answer questions of why one result was obtained instead of another and therefore can increase the confidence in the correctness of program results. Our focus on dynamic programming is motivated by the fact that dynamic programming offers a systematic approach to implementing a large class of optimization algorithms which produce decisions based on aggregated value comparisons. It is those decisions that the granular representation can help explain. Moreover, the fact that dynamic programming can be formalized using semirings supports the creation of a Haskell library for dynamic programming that has two important features. First, it allows programmers to specify programs by recurrence relationships from which efficient implementations are derived automatically. Second, the dynamic programs can be formulated generically (as type classes), which supports the smooth transition from programs that only produce result to programs that can run with granular representation and also produce explanations. Finally, we also demonstrate how to anticipate user questions about program results and how to produce corresponding explanations automatically in advance. [ABSTRACT FROM AUTHOR]

This paper focuses on the berth allocation problem, which is to determine where and when the vessels to be loaded and unloaded at a terminal within a given planning horizon, with consideration of uncertain factors, mainly including the arrival and operation time of the calling vessels. Based on the concept of service level which is commonly used in the inventory system, a decision model is constructed to minimize the cost of baseline schedule, which includes delay cost and nonoptimal berthing location cost. According to the specific characteristics of the model, the upper and lower bounds are found. And due to the NP-hardness of the constructed model, an adaptive differential evolution is employed to solve the problem. Finally, extensive numerical experiments are conducted to test the performance of the proposed models and solution approaches. [ABSTRACT FROM AUTHOR]

In this paper, we proposed a new clustering algorithm called UDSC for uncertain data stream. According to the existence probability of cluster, clusters are divided into three levels: strong, transitional and weak. An effective strategy for choosing candidate cluster is developed based on four factors: distance, cluster compactness, existence probability of cluster and existence strength of cluster, which used to find optimal accepted cluster for each continuously arriving data. Then we proposed several rules to describe drifts and shifts in clusters, which used to help users known the changes clearly in clusters and help users make new decisions accurately. Experiment results shows that UDSC clustering algorithm outperforms the existing methods in efficiency and effectiveness. [ABSTRACT FROM AUTHOR]

In this paper, a complete cross-correlation-based fault-diagnostic method is proposed for real-time digital-signal-processor (DSP) applications that cover both the fault-monitoring and decision-making stages. In practice, a motor driven by an inverter or utility line is run at various operating points where the frequency, amplitude, and phase of the fault signatures vary unexpectedly. These changes are considered to be one of the common factors that yield erroneous fault tracking and unstable fault detection. In this paper, the proposed algorithms deal with the ambiguities of line-current noise or sensor-resolution errors and operating-point-dependent threshold issues. It is theoretically and experimentally verified that a motor fault can be continuously tracked when the sensor errors are within a limited range through the adaptively determined threshold definition of noise conditions. The offline experiments are performed via Matlab using actual line-current data obtained by a data-acquisition system. These results are verified on a DSP-based motor drive in real time where drive sensors and a digital signal processor are employed both for motor-control and fault-diagnostic purposes. [ABSTRACT FROM AUTHOR]

Constraint-based scheduling is a powerful tool for solving complex real-life scheduling problems thanks to a natural integration of special solving algorithms encoded in so-called global constraints. Global constraints describe subproblems of the scheduling problem, eg, allocation of activities to a disjunctive resource. Filtering algorithms behind these constraints are used to prune the search space by eliminating options that violate the constraint. The filtering algorithms are frequently expressed in the form of propagation rules that react to some change appearing during problem solving, eg, adding a new precedence relation, by proposing a derived restriction, eg, shrinking a time window. These changes can be invoked by other constraints or they can be a result of some search decision. This paper describes new incremental propagation rules integrating propagation of precedence relations and time windows for activities allocated to a disjunctive resource. Moreover, the rules also cover so-called optional activities that may or may not be present in the final schedule. [ABSTRACT FROM AUTHOR]

A representative skyline contains k skyline points that can represent its corresponding full skyline. The existing measuring criteria of k representative skylines are specifically designed for static data, and they cannot effectively handle streaming data. In this paper, we focus on the problem of calculating the k representative skyline over data streams. First, we propose a new criterion to choose k skyline points as the k representative skyline for data stream environments, termed the k largest dominance skyline (k-LDS), which is representative to the entire data set and is highly stable over the streaming data. Second, we propose an efficient exact algorithm, called Prefix-based Algorithm (PBA), to solve the k-LDS problem in a 2-dimensional space. The time complexity of PBA is only O((M −k ) × k) where M is the size of the full skyline set. Third, the k-LDS problem for a d -dimensional (d ≥ 3) space turns out to be very complex. Therefore, a greedy algorithm is designed to answer k-LDS queries. To further accelerate the calculation, we propose a ϵ-greedy algorithm which can achieve an approximate factor of ...(...). Experimental results on both synthetic and real-world data show that our k-LDS significantly outperforms its competitors in data stream environments. Furthermore, we demonstrate that the proposed ϵ-greedy algorithm can solve k-LDS efficiently and with a competitive accuracy. [ABSTRACT FROM AUTHOR]

Since decision-making algorithms on high-performance computing yield large-size policies, compression methods are necessary for utilizing them on small robots or on robots that must store a number of policies for various tasks. A height task of the Acrobot, which is a well-known problem, is solved by value iteration and its decision-making policy is compressed to the utmost limit in this paper. From the result, we discuss the availability of brute force approach under severe limitations of memory available on robots. We have obtained a 7060 bit policy using vector quantization and run-length encoding from a policy on a 60,484,176 bit look-up table though the table is complicated by the chaotic nature of the Acrobot. Moreover, we have evaluated the size of software for using the compressed policy, and have created a 15,424 bit object code for an ARM processor. The total size 22,752 bit is in the same range with those of policies implemented as several if-then rules or equations coded by hand. [ABSTRACT FROM AUTHOR]

Linguistic hesitant fuzzy sets (LHFSs), which can be used to represent decision-makers’ qualitative preferences as well as reflect their hesitancy and inconsistency, have attracted a great deal of attention due to their flexibility and efficiency. This paper focuses on a multi-criteria decision-making approach that combines LHFSs with the evidential reasoning (ER) method. After reviewing existing studies of LHFSs, a new order relationship and Hamming distance between LHFSs are introduced and some linguistic scale functions are applied. Then, the ER algorithm is used to aggregate the distributed assessment of each alternative. Subsequently, the set of aggregated alternatives on criteria are further aggregated to get the overall value of each alternative. Furthermore, a nonlinear programming model is developed and genetic algorithms are used to obtain the optimal weights of the criteria. Finally, two illustrative examples are provided to show the feasibility and usability of the method, and comparison analysis with the existing method is made. [ABSTRACT FROM AUTHOR]

Band selection is an important preprocessing step for hyperspectral image processing. Many valid criteria have been proposed for band selection, and these criteria model band selection as a single-objective optimization problem. In this paper, a novel multiobjective model is first built for band selection. In this model, two objective functions with a conflicting relationship are designed. One objective function is set as information entropy to represent the information contained in the selected band subsets, and the other one is set as the number of selected bands. Then, based on this model, a new unsupervised band selection method called multiobjective optimization band selection (MOBS) is proposed. In the MOBS method, these two objective functions are optimized simultaneously by a multiobjective evolutionary algorithm to find the best tradeoff solutions. The proposed method shows two unique characters. It can obtain a series of band subsets with different numbers of bands in a single run to offer more options for decision makers. Moreover, these band subsets with different numbers of bands can communicate with each other and have a coevolutionary relationship, which means that they can be optimized in a cooperative way. Since it is unsupervised, the proposed algorithm is compared with some related and recent unsupervised methods for hyperspectral image band selection to evaluate the quality of the obtained band subsets. Experimental results show that the proposed method can generate a set of band subsets with different numbers of bands in a single run and that these band subsets have a stable good performance on classification for different data sets. [ABSTRACT FROM AUTHOR]

In this paper, we propose an automated multi-agent negotiation framework for decision making in the construction domain. It enables software agents to conduct negotiations and autonomously make decisions. The proposed framework consists of two types of components, internal and external. Internal components are integrated into the agent architecture while the external components are blended within the environment to facilitate the negotiation process. The internal components are negotiation algorithm, negotiation style, negotiation protocol, and solution generators. The external components are the negotiation base and the conflict resolution algorithm. We also discuss the decision making process flow in such system. There are three main processes in decision making for specific projects, which are propose solutions, negotiate solutions and handling conflict outcomes (conflict resolution). We finally present the proposed architecture that enables software agents to conduct automated negotiation in the construction domain. [ABSTRACT FROM AUTHOR]

There is a continuous need to improve existing project management decision-making support models, particularly those for monitoring and control are needed to increase chances for success. In this paper, potential of early warning in project management was focused, along with its connection to project success via project success factors. First, a systematic literature review was conducted, along with the focus group method, in order to identify project success factors. The selected success factors were also collected on 93 water infrastructure projects through a survey. By the means of linear regression analysis, critical success factors were finally determined. The results were integrated in the early warning system algorithm, composed by three modules - detection, validation and response module. The response module is composed by three dimensions: 1) risk, constraint and change management, 2) incorporation of project management competences and 3) application of project management methods, tools and techniques. The proposed early warning system was tested on three infrastructure projects. The results confirmed that improved early warning system can contribute in increase of project management success. As original database is composed by projects from only one country (Croatia), in the final stage of the research proposed approach was checked in five countries from the Central or South East Europe (Czech Republic, Slovenia, Serbia, North Macedonia, Bosnia and Herzegovina). The results from the final stage confirmed potential of the proposed approach as declared originally, so it is reasonable to expect success in early warning system's implementation due to similarities of critical success factors on projects as well as project management problems in general, that countries in regions share. This research presents new and creative way in linking early warning and project success, as well as interpretation of early response through different dimensions in project management. Also, based on the results of this research, it is possible to create a useful practical tool for managing other types of projects. [ABSTRACT FROM AUTHOR]

The paper describes the problem of stopping the text field recognition process in a video stream, which is a novel problem, particularly relevant to real-time mobile document recognition systems. A decision-theoretic framework for this problem is provided, and similarities with existing stopping rule problems are explored. Following the theoretical works on monotone stopping rule problems, a strategy is proposed based on thresholding the estimation of the expected difference between consequent recognition results. The efficiency of this strategy is evaluated on an openly accessible dataset. The results show that this method outperforms the previously published methods based on identical results cluster size thresholding. Notes on future work include incorporation of recognition result confidence estimations in the proposed model and more precise evaluation of the observation cost. [ABSTRACT FROM AUTHOR]