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We discuss the three-way rough set based approach for approximation of decision granules in Intelligent Systems (IS's). The novelty of the approach is in presenting a new concept of approximation space which is based on advanced reasoning tools. Many generalisations of the rough set approaches developed over the years are mainly concentrated around reasoning concerning (partial) inclusion of sets. However, such approximation spaces are not satisfactory to deal with important aspects of approximate reasoning by IS's aiming to construct of the high quality approximations of compound decision granules. We demonstrate a number of examples supporting this claim. In particular, in solving the considered in the paper problems are involved complex algorithmic optimization processes directed by reasoning tools supporting searching for (semi-)optimal approximations of decision granules in huge spaces. This paper is a step toward developing tools for derivation of granules supporting IS's in perceiving situations to a degree satisfactory for making the right decisions. [ABSTRACT FROM AUTHOR]

Purpose: With the increase in the adoption of artificial intelligence (AI)-based decision-making, organizations are facilitating human–AI collaboration. This collaboration can occur in a variety of configurations with the division of labor, with differences in the nature of interdependence being parallel or sequential, along with or without the presence of specialization. This study intends to explore the extent to which humans express comfort with different models human–AI collaboration. Design/methodology/approach: Situational response surveys were adopted to identify configurations where humans experience the greatest trust, role clarity and preferred feedback style. Regression analysis was used to analyze the results. Findings: Some configurations contribute to greater trust and role clarity with AI as a colleague. There is no configuration in which AI as a colleague produces lower trust than humans. At the same time, the human distrust in AI may be less about human vs AI and more about the division of labor in which human–AI work. Practical implications: The study explores the extent to which humans express comfort with different models of an algorithm as partners. It focuses on work design and the division of labor between humans and AI. The finding of the study emphasizes the role of work design in human–AI collaboration. There is human–AI work design that should be avoided as they reduce trust. Organizations need to be cautious in considering the impact of design on building trust and gaining acceptance with technology. Originality/value: The paper's originality lies in focusing on the design of collaboration rather than on performance of the team. [ABSTRACT FROM AUTHOR]

Three-way decision (3WD) and rough set are two influential theories of study knowledge discovery and uncertain artificial intelligence. A central notion of 3WD is a tri-level thinking paradigm consisting of trisecting, acting, and outcome (i.e., TAO model). As is well known, movement-based on three-way decision (M-3WD) and change-based TAO model, mainly started from the perspective of effectiveness measure, are two outcome evolution studies about the three-way decision, which could lead to some limitations in application. This paper builds a change-based three-way decision (C-3WD) based on confidence level, and an application to rough set is also discussed. Furthermore, the (α , β) -approximate probability regions of rough set are re-decided by the change model, and a medical decision example is introduced to explain how to make C-3WD in the classification of rough set. By comparing the effectiveness of the traditional three-way decision method with ours, it is again verified from the two aspects of cost and earns and concludes that the model in this paper is more suitable for the decision process that includes trisecting and acting. Some experiments on various datasets to demonstrate the effectiveness of our methods. [ABSTRACT FROM AUTHOR]

To address the challenge of facilitating overall problem solving and improving the overall efficiency of intelligent systems efforts through group decision making (GDM) by experts at all stages of the systems, this paper proposes an approach that focuses on improving consistency and enhancing local consensus. The proposed approach specifically considers the probabilistic linguistic preference relations (PLPRs) and incorporates personalized individual semantics (PISs) of decision makers (DMs). For the consistency procedure, we construct an expectation-additive consistency-driven semantic model to acquire PISs of different DMs. Secondly, the preference relation (PR) which does not satisfy the consistency is improved based on a minimum adjustment model. In particular, according to the expected value with PISs this paper, DMs offer PLPRs can be transformed into fuzzy preference relations (FPRs) correspondently for making decisions. For the FPRs, a virtue consensus measure is explored for the consensus reaching process (CRP) from two levels to combine the average value and variance of pair similarities, which effectively improves the accuracy of the consensus measurement. Next, a collective consensus level is calculated to replace the consensus threshold objectively, and then the DMs and alternative pairs that do not reach consensus are identified locally from two levels. Subsequently, an optimisation model that combines the two objectives is developed to improve the consensus level while ensuring consistency. Finally, our method is applied to a publicly available air quality dataset, and the consensus and ranking results are discussed in comparison with other advanced methods to confirm the superiority of the established method. [ABSTRACT FROM AUTHOR]

The use of artificial intelligence for decision support and automation has shown tremendous potential in many areas. The ability to explain the decisions made by a machine learning algorithm is fundamental to facilitating the widespread use of this type of tool. There are many important real-world problems where the cost of the decisions depends on the characteristics of each example: these are called example-dependent cost (EDC) problems. For this type of classification problem, an appropriate formulation that takes into account the decision costs is fundamental both for the design of the classifier and for the explanation of its decisions. In this paper, we propose COCOA, an explanation method designed for EDC problems based on a Bayesian discriminant. The proposed method can provide counterfactual samples generated by considering decision costs. The COCOA method provides valid and plausible counterfactuals with a high success rate, which can be actionable, diverse, and sparse, achieving a remarkable improvement in terms of cost over five state-of-the-art methods on six real-world datasets. • COCOA provides actionable, plausible, sparse and diverse counterfactuals. • Decision costs are taken into account when generating counterfactuals. • The obtained counterfactuals improve cost savings. • The method is presented for MLPs, but other architectures can be used. [ABSTRACT FROM AUTHOR]

The main aim of the paper is to discuss the architecture for the future Intelligent Systems (IS's) and Decision Support Systems (DS's) dealing with complex phenomena such as supporting medical decisions (diagnosis and therapy) and to emphasize challenges in designing such systems. More precisely, the paper presents arguments for developing a specialized computing model based on the interactive granular computing paradigm which can help to design IS's and DS's more close to the prototypes of real life decision making. In this regard, the paper brings to the fore different experiences faced during designing other medical IS's or DS's.As a starting step, the paper considers the experience of developing the OvuFriend platform and outlines some possible extension of it in the framework of the proposed architecture on the basis of Interactive Granular Computing (IGrC) model. Specifically, our attempt is to analyze a scheme, which is being used in the platform of OvuFriend for determining health risks and possibilities of a woman to conceive a child, from the perspective of IGrC. The target of the paper is two fold. Firstly, to show how the underlying AI algorithm of this scheme can be related with the notion of computing in the context of IGrC. Secondly, to identify possible extensions of the existing scheme so that it becomes more dynamic, interactive, and close to personalized medicine. [ABSTRACT FROM AUTHOR]

Purpose The purpose of this paper is to develop a methodology based on TODIM (an acronym in Portuguese for interactive and multicriteria decision-making) approach for the selection of the best alternative in the context of multi criteria group decision-making (MCGDM) problems under possibilistic uncertainty in interval-valued Pythagorean fuzzy (IVPF) environment.Design/methodology/approach In this paper, IVPF-TODIM method is proposed. Some new point operator-based similarity measures (POSMs) for IVPF sets (IVPFSs) are introduced which have the capability to reduce the degree of uncertainty of the elements in the universe of discourse corresponding to IVPFS. Then the newly defined POSMs are used to compute the measure of relative dominance of each alternative over other alternatives in the IVPF-TODIM context. Finally, generalized mean aggregation operator is used to find the best alternative.Findings As the TODIM method is used to solve the MCGDM problems under uncertainty, POSMs are developed by using three parameters which can control the effect of decision-makers' psychological perception under risk.Research limitations/implications The decision values are used in IVPF numbers (IVPFNs) format.Practical implications The proposed method is capable to solve real-life MCGDM problems with not only IVPFNs format but also with interval-valued intuitionistic fuzzy numbers.Originality/value As per authors' concern, no approach using TODIM with IVPFNs is found in literature to solve MCGDM problems under uncertainty. The final judgment values of alternatives using the extended TODIM methodology are highly corroborate in compare to the results of existing methods, which proves its great potentiality in solving MCGDM problems under risk. [ABSTRACT FROM AUTHOR]

The ancient oriental game of Go has long been considered a grand challenge for artificial intelligence. For decades, computer Go has defied the classical methods in game tree search that worked so successfully for chess and checkers. However, recent play in computer Go has been transformed by a new paradigm for tree search based on Monte-Carlo methods. Programs based on Monte-Carlo tree search now play at human-master levels and are beginning to challenge top professional players. In this paper, we describe the leading algorithms for Monte-Carlo tree search and explain how they have advanced the state of the art in computer Go. [ABSTRACT FROM AUTHOR]

Flexible representations are required in order to understand and generate expert behavior. Although production rules with quantifiers can encode experiential knowledge, they often have assumptions implicit in them, making them brittle in problem scenarios where these assumptions do not hold. Qualitative models achieve flexibility by representing the domain entities and their interrelationships explicitly. However, in problem domains where assumptions underlying such models change periodically, it is necessary to be able to synthesize and maintain qualitative models in response to the changing assumptions. In this paper we argue for a representation that contains partial model components that are synthesized into qualitative models containing entities and relationships relevant to the domain. The model components can be replaced and rearranged in response to changes in the task environment. We have found this "model constructor" to be useful in synthesizing models that explain and generate expert behavior, and have explored its ability to support decision making in the problem domain of business resource planning, where reasoning is based on models that evolve in response to changing external conditions or internal policies. [ABSTRACT FROM AUTHOR]

The Designer Problem Solver (DPS) demonstrates that the computer can perform simple design tasks. In particular, it designs furniture and equipment layouts. This task was chosen because it is simple, welt defined, and characteristic of many design tasks in architecture, engineering, urban planning, and natural resource management. These space planning tasks usually involve manipulating two-dimensional representations of objects to create feasible or optimal solutions for problems involving topological and metric spatial constraints. The paper describes extensive tests performed on the program, DPS is a heuristic problem solver with a planning phase prefixed to It. It uses the planning process to give it a sense of direction, diagnostic procedures to locate difficulties, and remedial actions to recover from difficulties. It uses a convex polygon representation to accurately describe the objects and the layout. This representation allows topological and metric constraints to be tested and the design to be easily updated. ups has been applied to 50 problems. While it is slow and limited in scope, the ideas behind it are general. It demonstrates the need for selectivity in controlling search and the methods used to achieve it: task-specific information, planning, diagnostic procedures, remedial actions, and selective alternative generators. [ABSTRACT FROM AUTHOR]

Purpose -- Traffic congestion is becoming a serious problem that has adverse consequences on the socio-economy, environment, and public health of various cities worldwide. The purpose of this paper is to contribute to the continuous search for new alternative solutions to prevent or alleviate these concerns. It particularly deals with the development of decision support system based on a data fusion for the management and control of traffic at signalized intersections. The role of such systems is to manage the existing infrastructure to ease congestion and respond to crises. The proposed system is based on multi-detector data fusion, a data processing function that combines imperfect information collected from systems involving several detectors. The developed system is then tested on a virtual junction, and the results obtained are reported and discussed. Design/methodology/approach -- This paper presents a new traffic light control based on multi-detectors data fusion theory. The system uses a new multi-detectors data fusion method for traffic data analysis. Moreover, the system integrates a method for the estimation of the reliability degree of different detectors taking into account their imperfection and the conflict between them. These estimated reliability degrees are combined using Dempster's rule of combination. Findings -- The paper provides a decision support system for traffic regulation at intersection based on multi-sensors. It suggests the fusion of captured data by many sensors for measuring information. The system use the Belief Functions Theory for information fusion and decision making using combination and decision rules. Originality/value -- The paper proposes a new Adaptive Traffic Control System based on a new data fusion approach that include a method for the estimation of the reliability degree of different detectors taking into account their imperfection and the conflict between them. These estimated reliability degrees are combined using Dempster's rule of combination. [ABSTRACT FROM AUTHOR]

In recent years, much research has been conducted on path replanning in a dynamic environment, as it is considered to be a more complex problem than path planning in a static environment. However, with respect to determining when to replan a path, most research on dynamic path replanning typically generates a new path if obstacles collide with the current path, or with reference to each time unit interval. As these approaches do not focus on effective path regeneration, they result in redundant and unnecessary path regeneration. In this context, this paper proposes an intelligent decision-making mechanism for path regeneration to determine whether a new path should be generated when obstacles move over time. Based on the identification of certain important obstacles, we let these obstacles propagate influence, and then obtain path values that represent the distance between the obstacles and the path. Then, the necessity of path regeneration is determined according to the changes in path values in a dynamic environment. Using this approach, it is possible to both enhance paths in terms of distance and resolve any infeasibilities due to moving obstacles. In the experiment, we test the proposed methodology to verify that appropriate decisions are made, and compare it with other approaches that regenerate a path at each time interval or on collision. Our results show that the paths are regenerated only if it is necessary to improve the current path or to resolve infeasibilities, indicating that effective and intelligent decisions for path regeneration can be obtained using the proposed decision mechanism. [ABSTRACT FROM AUTHOR]

Purpose – The purpose of this present study is to find a scientific method for the evaluation of environmental impacts according to the requirement 4.3.1. Design/methodology/approach – To realize the objectives, the authors worked with a representative sample from certified ISO 14001 organizations. The data aim to identify and evaluate (according to the organization's methodology) significant environmental impacts. In this study, the authors created two models for the evaluation of environmental impacts based on an artificial neural network applied in the pilot organization and compared the results obtained from these models with those obtained by applying an analytic hierarchy process (AHP) method. AHP is part of an multi-criteria decision making method and provides good multi-criteria support for decision making for problems that can be structured hierarchically. Findings – This paper presents a new approach that uses a backpropagation neural network to evaluate environmental impacts regardless of the organization type. Originality/value – This paper presents a unique approach for the reliable and objective evaluation of environmental impacts. [ABSTRACT FROM AUTHOR]

Purpose – The purpose of this paper is to establish a situation evaluation model of the robot and ball in SimuroSot5vs5 platforms and enhance the strength of the team in a SimuroSot5vs5. Design/methodology/approach – This paper presents a mathematical model based on situation evaluations which can improve the strength of the team in SimuroSot5vs5. The situation evaluation focus on four aspects includes scores of both sides, possession of teams on ground, ball strategy, and treat. The statistical analysis of the score can verify validity and stability of current strategy in confrontation. To evaluate the situation more effectively without blindness, possession on both teams is, respectively, evaluated. Ball strategy analyzes coordinate transformation to the ball on the ground and illustrates the circumstance of both teams on the offensive position accurately in length and breadth. To know the circumstance on the field more completely and synthetically, a threat situation evaluation model is built. An effective and practical algorithm for comprehensive situation evaluation is successfully finished. The experiments prove validity and performance of the proposed situation evaluation model. Findings – A mathematical model is designed to achieve situation evaluation, and the strategy can change in accordance with different situations on the ground. Research limitations/implications – The system is specifically applied to SimuroSot5vs5 platform. The extensibility of the system is limited. Practical implications – When the robot and ball is in high speed movement, a large calculated amount will slow the speed of the system. Originality/value – The paper shows that situation evaluation in SimuroSot decision support systems will enhance the battle effectiveness of the soccer team. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we compare the inference capabilities of three different types of fuzzy cognitive maps (FCMs). A fuzzy cognitive map is a recurrent artificial neural network that creates models as collections of concepts/neurons and the various causal relations that exist between these concepts/neurons. In the paper, a variety of industry/engineering FCM applications is presented. The three different types of FCMs that we study and compare are the binary, the trivalent and the sigmoid FCM, each of them using the corresponding transfer function for their neurons/concepts. Predictions are made by viewing dynamically the consequences of the various imposed scenarios. The prediction making capabilities are examined and presented. Conclusions are drawn concerning the use of the three types of FCMs for making predictions. Guidance is given, in order FCM users to choose the most suitable type of FCM, according to (a) the nature of the problem, (b) the required representation capabilities of the problem and (c) the level of inference required by the case. [Copyright &y& Elsevier]

Purpose - The purpose of this paper is to discuss the issue of transparency in policy-making processes. Design/methodology/approach - Development and application of conceptual framework for policy studies. Findings - Complementarity of Habermas's theory of communicative action and Beer's Viable System Model. Relevance of the ideas of requisite organisation in policy-making and of "orthogonality" in social communications. Practical implications - Transparency of policy processes is more than making information available to stakeholders; it is enabling the development of an effective organisational system for the policy of concern. Originality/value - This paper relates Beer's organizational cybernetics and Habermas' political philosophy and produces an original model of transparency in policy-making. [ABSTRACT FROM AUTHOR]

It is widely accepted that using a set of cellular neural networks (CNNs) in parallel can achieve higher level information processing and reasoning functions either from application or biologics points of views. Such an integrated CNN system can solve more complex intelligent problems. In this paper, we propose a novel framework for automatically constructing a multiple-CNN integrated neural system in the form of a recurrent fuzzy neural network. This system, called recurrent fuzzy CNN (RFCNN), can automatically learn its proper network structure and parameters simultaneously. The structure learning includes the fuzzy division of the problem domain and the creation of fuzzy rules and CNNs. The parameter learning includes the tuning of fuzzy membership functions and CNN templates. In the RFCNN, each learned fuzzy rule corresponds to a CNN. Hence, each CNN takes care of a fuzzily separated problem region, and the functions of all CNNs are integrated through the fuzzy inference mechanism. A new online adaptive independent component analysis mixture-model technique is proposed for the structure learning of RFCNN, and the ordered-derivative calculus is applied to derive the recurrent learning rules of CNN templates in the parameter-learning phase. The proposed RFCNN provides a solution to the current dilemma on the decision of templates and/or fuzzy rules in the existing integrated (fuzzy) CNN systems. The capability of the proposed RFCNN is demonstrated on the real-world defect inspection problems. Experimental results show that the proposed scheme is effective and promising. [ABSTRACT FROM AUTHOR]

Purpose Organizations rely on social outreach campaigns to raise financial support, recruit volunteers, and increase public awareness. In order to maximize response rates, organizations face the challenging problem of designing appropriately tailored interactions for each user. An interaction consists of a specific combination of message, media channel, sender, tone, and possibly many other attributes. The purpose of this paper is to address the problem of how to design tailored interactions for each user to maximize the probability of a desired response.Design/methodology/approach A nearest-neighbor (NN) algorithm is developed for interaction design. Simulation-based experiments are then conducted to compare positive response rates obtained by two forms of this algorithm against that of several control interaction design strategies. A factorial experimental design is employed which varies three user population factors in a combinatorial manner, allowing the methods to be compared across eight distinct scenarios.Findings The NN algorithms significantly outperformed all three controls in seven out of the eight scenarios. Increases in response rates ranging from approximately 20 to 400 percent were observed.Practical implications This work proposes a data-oriented method for designing tailored interactions for individual users in social outreach campaigns which can enable significant increases in positive response rates. Additionally, the proposed algorithm is relatively easy to implement.Originality/value The problem of optimal interaction design in social outreach campaigns is scarcely addressed in the literature. This work proposes an effective and easy to implement solution approach for this problem. [ABSTRACT FROM AUTHOR]

Abstract: Ubiquitous computing technology can be effectively utilized in shared environments where groups of people are in close proximity. Shared environments are pervasive in the real world and hence the way of managing such environments will impact on not only quality of life but also business competitiveness. However, making decisions in an intelligent shared environment is never straightforward. The intelligence needs to be capable of choosing its parameters to satisfy all of its inhabitants, who have different preferences and are heterogeneous in their influences on decision. Till today, there has been no thorough research to scientifically investigate this type of decision making problems, though many systems have been already deployed. This research proposes a methodology for making decisions in such circumstances. The current and future works addressed in this paper are also conductive to any human-centric networks such as service systems, since the issues addressed here are also essential constitutes of such human-centric networks. [Copyright &y& Elsevier]

Abstract: For diagnosing dyslexia in early childhood, children have to solve non-writing based graphical tests. Human experts score these tests, and decide whether the children require further consideration on the basis of their marks. Applying artificial intelligence techniques for automating this scoring is a complex task with multiple sources of uncertainty. On the one hand, there are conflicts, as different experts can assign different scores to the same set of answers. On the other hand, sometimes the experts are not completely confident with their decisions and doubt between different scores. The problem is aggravated because certain symptoms are compatible with more than one disorder. In case of doubt, the experts assign an interval-valued score to the test and ask for further information about the child before diagnosing him. Having said that, exploiting the information in uncertain datasets has been recently acknowledged as a new challenge in genetic fuzzy systems. In this paper we propose using a genetic cooperative–competitive algorithm for designing a linguistically understandable, rule-based classifier that can tackle this problem. This algorithm is part of a web-based, automated pre-screening application that can be used by the parents for detecting those symptoms that advise taking the children to a psychologist for an individual examination. [Copyright &y& Elsevier]

Abstract: Artificial Color filters are designed to attenuate some pixels and pass others. The pass/attenuate decision is made on the basis of the learned association of spectral components with user-defined concepts. In earlier work, it has been shown that there are various ways to design Artificial Color filters using multiple user-designated classes and those filters are subjected to useful manipulations such as image processing and Boolean Aggregation. The Artificial Color filtering has always been binary. Therefore, the Boolean logic was the only choice for aggregating filters. This paper shows how to fuzzify Artificial Color filters. Fuzzy logic subsumes Boolean logic and can do so in many ways. Several different fuzzy T-norms are applied to Artificial Color filters to illustrate the richness in aggregation. Margin Setting, a supervised statistical pattern recognition method to train the filters, is very conservative in what is definitely assigned to a class while allowing a useful gradation of membership for other cases. A parametric exploration of these effects for an image is presented. [Copyright &y& Elsevier]

Abstract: The foundations and implementation of a genetic algorithm (GA) for instrumentation purposes are presented in this paper. The GA constitutes an initialization module of a decision support system for sensor network design. The method development entailed the definition of the individual’s representation as well as the design of a graph-based fitness function, along with the formulation of several other ad hoc implemented features. The performance and effectiveness of the GA were assessed by initializing the instrumentation design of an ammonia synthesis plant. The initialization provided by the GA succeeded in accelerating the sensor network design procedures. It also accomplished a great improvement in the overall quality of the resulting instrument configuration. Therefore, the GA constitutes a valuable tool for the treatment of real industrial problems. [Copyright &y& Elsevier]

Case-based reasoning is a new paradigm that provides, on the one hand, a cognitive model of the organization of memory, reasoning and human learning; and, on the other hand, it offers a new Artificial Intelligence technique for computer systems development. Today, information is considered as a key product in any business. In the entrepreneurial environment, it adds value, usefulness and importance. Hence, the need of guaranteing its security, which relies on three mainstays: technology, processes and people, the latter has the main role. This paper designs and implements a case base and generates a computer security expert that helps in the decision making, based on the information related to the main security violation events within the Organization. [ABSTRACT FROM AUTHOR]