Cognitive Task Analysis: An Approach to Knowledge Acquisition for Intelligent System Design

The object of the study is intelligent decision support systems. The problem addressed in the research is to improve the accuracy of modeling the functioning process of intelligent decision support systems. A polymodel complex for resource management of intelligent decision support systems has been developed. The originality of the study lies in: – the comprehensive description of the functioning processes of intelligent decision support systems; – the capability to model both an individual process occurring in intelligent decision support systems and to perform comprehensive modeling of the processes taking place within them; – the establishment of conceptual dependencies in the functioning process of intelligent decision support systems. This makes it possible to describe the interaction of individual models at all stages of solving computational tasks; – the description of coordination processes in hybrid intelligent decision support systems, which ensures an increase in the reliability of managerial decision-making; – the modeling of processes for solving complex computational tasks in intelligent decision support systems through the conceptual description of the specified process; – the coordination of computational processes in intelligent decision support systems, which leads to a reduction in the number of computational resources of the systems; – the comprehensive resolution of conflicts through a set of corresponding mathematical models. The proposed polymodel complex is advisable for use in solving the problem of managing intelligent decision support systems characterized by a high degree of complexity

To survey person centered survival rate in population based screening program by an intelligent clinical decision support system. Colorectal cancer is the most common malignancy and major cause of morbidity and mortality throughout the world. Colorectal cancer is the sixth leading cause of cancer death in Iran. In this survey, we used cosine similarity as data mining technique and intelligent system for estimating survival of at risk groups in the screening plan. In the first step, we determined minimum data set (MDS). MDS was approved by experts and reviewing literatures. In the second step, MDS were coded by python language and matched with cosine similarityformula. Finally, survival rate by percent was illustrated in the user interface of national intelligent system. The national intelligent system was designed in PyCharm environment. Main data elements of intelligent system consist demographic information, age, referral type, risk group, recommendation and survival rate. Minimum data set related to survival comprise of clinical status, past medical history and socio-demographic information. Information of the covered population as a comprehensive database was connected to intelligent system and survival rate estimated for each patient. Mean range of survival of HNPCC patients and FAP patients were respectively 77.7% and 75.1%. Also, the mean range of the survival rate and other calculations have changed with the entry of new patients in the CRC registry by real-time. National intelligent system monitors the entire of risk group and reports survival rates by electronic guidelines and data mining technique and also operates according to the clinical process. This web base software has a critical role in the estimation survival rate in order to health care planning.

A SDSS combines database storage technologies, geographic information systems (GIS) and decision modeling into tools which can be used to address a wide variety of decision support areas (Eklund, Kirkby, and Pollitt, 1996). Recently, various emerging technologies in computer hardware and software such as speedy microprocessors, gigabit network connections, fast internet mapping servers along with Web-based technologies like extensible markup language (XML), Web services, etc provide promising opportunities to take the traditional spatial decision support systems one step further to provide easy-to-use, round-the-clock access to spatial data and decision support over the Web. Traditional DSS and Web-based spatial DSS can be further improved by integrating expert knowledge and utilizing intelligent software components (such as expert systems and intelligent agents) to emulate the human intelligence and decision making. These kinds of decision support systems are classified as intelligent decision support systems. The objective of this chapter is to discuss the development of an intelligent web-based spatial decision support system and demonstrate it with a case study for planning snow removal operations.

Intelligent decision support (IDS) systems leverage artificial intelligence techniques to generate recommendations that guide human users through the decision making phases of a task. However, a key challenge is that IDS systems are not perfect, and in complex real-world scenarios may produce suboptimal output or fail to work altogether. The field of explainable AI (XAI) has sought to develop techniques that improve the interpretability of black-box systems. While most XAI work has focused on single-classification tasks, the subfield of explainable AI planning (XAIP) has sought to develop techniques that make sequential decision making AI systems explainable to domain experts. Critically, prior work in applying XAIP techniques to IDS systems has assumed that the plan being proposed by the planner is always optimal, and therefore the action or plan being recommended as decision support to the user is always optimal. In this work, we examine novice user interactions with a non-robust IDS system – one that occasionally recommends suboptimal actions, and one that may become unavailable after users have become accustomed to its guidance. We introduce a new explanation type, subgoal-based explanations, for plan-based IDS systems, that supplements traditional IDS output with information about the subgoal toward which the recommended action would contribute. We demonstrate that subgoal-based explanations lead to improved user task performance in the presence of IDS recommendations, improve user ability to distinguish optimal and suboptimal IDS recommendations, and are preferred by users. Additionally, we demonstrate that subgoal-based explanations enable more robust user performance in the case of IDS failure, showing the significant benefit of training users for an underlying task with subgoal-based explanations.

In the coming years the quantity of information will grow exponentially, creating real labyrinths where tourists and tourist professionals will have to solve complex problems quickly. In the most far-sighted visions intelligent systems will have an important role [4, 18]. They will be imperatively used as research tools in tourism, but also as integrators of existing technologies and performers of such tasks that until now have only been executed by people. The article illustrates some achievements in the application of intelligent systems in tourism obtained with the project “Intelligent decision support systems in management of complex systems”. The aim of the project is to research and create the methodology of developing the second generation of intelligent systems [9, 10]. Next, second generation of intelligent systems, according to F. Hayes-Roth, should overcome the limitations of today’s intelligent systems, introducing some new characteristics: “context reusable building blocks, reusable knowledge, high value component functions, composite architecture for multi-task systems, architecture for semantic interoperability, and a small number of domains, which should be attacked persistently [2]”.

The object of the study is the clinical decision-making process for selecting of hepatocellular carcinoma (HCC) treatment method based on the patient's medical data. The process of the HCC treatment method selection remains poorly formalized and is characterized by multi-criteria and the presence of numerous clinical situations, for each of which it is necessary to promptly identify the most accurate therapeutic solution. This study develops an intelligent medical decision support system for HCC treatment method selection based on knowledge applicable in clinical practice. It offers architectural and functional principles of the intelligent decision support system, classifying clinical situations by HCC treatment method, consisting of multiple possible combinations of 44 informative parameters. Based on the current values of these parameters, expert knowledge is transformed into production rules identifying HCC treatment methods. A heuristic procedure treatment selection is developed based on production rule analysis in accordance with current parameter values, reproducing the reasoning patterns of participants in a multidisciplinary council during their consensus decision-making process regarding HCC treatment appointment. A software implementation of a decision-making model for HCC treatment selection, implemented in C# using the Visual Study 2019 platform, enabled the integration of an intelligent system with web technologies. The intelligent medical decision support system automates the unique experience of professionals and helps physicians in a multidisciplinary consultation make prompt and informed decisions online regarding the appointment of personalized therapy. The system was piloted with expert physicians in several iterations until complete match between the consensus decision of the multidisciplinary council and the decision made by the developed system in accordance with clinical recommendations was achieved

The article is devoted to the field of software development. The considered scientific contradiction lies in the fact that, on the one hand, the use of manual labor of a programmer is necessary in this area, and on the other hand, the presence of a human factor negatively affects the safety of the resulting code. To resolve the contradiction, it is proposed to use machine learning, which is traditionally used to solve the problem of classification, regression, search for anomalies, clustering, generalization and search for associations. It is shown that the majority of publications on this solution are of a private nature and do not cover the entire spectrum of possibilities. Various ways of automating the programming process using solutions for the specified machine learning problems are considered and substantiated. The demand for a system that combines such methods is indicated; Also, for the first time, its author’s definition is introduced: «Intelligent Programming Support System – a computer automated system based on artificial intelligence technologies, the purpose of which is to help developers of program code in the interests of reducing and simplifying manual labor, as well as increasing the safety of the final product». A comparative analysis of automation methods based on machine learning is given according to 8 criteria that this intelligent system must meet. The ways of further continuation of the research are indicated.

In order to improve the effect of precision intelligent agricultural decision support system, this paper combines big data technology to carry out precision mining of agricultural data, and combines decision tree algorithm to carry out data classification processing. Moreover, this paper obtains the most effective agricultural decision reference data through data mining, combines the agricultural decision support process to set the functional modules of the decision system, and analyzes the implementation process of each functional module. In addition, this paper studies the theoretical basis and key technologies of the agricultural production structure optimisation decision support system, and builds a precision and intelligent agricultural decision support system based on big data analysis. The system mainly performs accurate processing of agricultural data and makes effective predictions, and then makes scientific decision results. Finally, this paper verifies the structure of the model in this paper combined with experimental analysis. From the comparison of experiments, it can be seen that the precision and intelligent agricultural decision support system constructed in this paper has significant effects.

Decision making tasks are subject to limitation and also depends on human knowledge, experiences, judgments and preferences. In this case, Intelligent Decision System (IDS) technologies can be used to provide realistic and consistent decisions, besides to improve the effectiveness of decision making processes. Intelligent Decision Support System (IDSS) is a contributory of IDS technology to assist decision makers in high level phases of decision making by integrating human knowledge with modeling tools. Nowadays, data mining (DM) techniques is also can be used to support Knowledge Management (KM) tasks especially for knowledge discovery and knowledge engineering. DM is emerging data analysis tool and widely used in order to produce valuable knowledge for decision making as knowledge modeling task. In Human Resource (HR), managing talent is among the challenges of HR professionals which can be handle by using IDSS and data mining technologies. For that reason, in this article, we discussed the potential to uses IDSS approach for talent management using DM techniques by proposing IDSS architecture and a case study on talent classification. This study consists of three parts; the first part is to know the previous works on IDSS, talent management and DM. The second part is discussion on proposed IDSS architecture for talent management. Finally, the third part is a case study on the use of DM classification method for talent forecasting, especially for employee's job promotion.

A New Approach In Learning For Intelligent Multi Agent Systems

The main aspects of the development of an intelligent internal audit support system (ISPSA) of a business entity are considered. A brief conceptual description of the algorithm for the development of internal audit support systems is given, with a statement of the main difficulties in the selection of components. The development of an intelligent internal audit support system is an important and necessary task, as internal audit plays an important role in ensuring effective management and control of a business entity. An intelligent system can help increase the efficiency of audit processes and provide more accurate results of automation and application of analytical tools. Over time, a business entity accumulates more and more data, and their analysis becomes an extremely difficult task. An intelligent system can quickly process large amounts of data, identify trends, anomalies, and risks that allow for more informed decisions; it can also identify unusual transactions or shortages that may indicate the possibility of fraud or error; it can provide auditors with valuable information and analytics to make informed decisions decisions on improving management processes and ensuring effective control mechanisms. This will help prevent the possibility of financial losses and preserve the reputation of the business entity. Consideration of some steps of the ISPVA algorithm is based on a brief comparative analysis of mathematical support methods.

In this chapter of the research, a method of processing various types of data in intelligent management systems of the network and server architecture of the internet of military equipment is proposed. The basis of this research is the theory of artificial intelligence, namely evolving artificial neural networks, basic genetic algorithm procedures, neuro-fuzzy expert systems, as well as bio-inspired algorithms. In the course of the research, the authors proposed:– a complex model of processing various types of data in intelligent decision-making support systems;– a method of processing various types of data in intelligent management systems of network and server architecture;– a method of increasing the efficiency of processing various types of data in intelligent management systems of network and server architecture. The use of methods of processing various types of data in intelligent management systems of the network and server architecture of the internet of military equipment:– to reduce the probability of premature convergence of the metaheuristic algorithm while processing various types of data in intelligent management systems of the network and server architecture of the internet of military equipment;– to maintain a balance between the speed of convergence of the metaheuristic algorithm and diversification while processing various types of data in the intelligent control systems of the network and server architecture of the internet of military equipment;– to take into account the type of uncertainty and noise of the data of the metaheuristic algorithm while processing various types of data in the intelligent control systems of the network and server architecture of the internet of military equipment;– to take into account the available computing resources of the system while processing various types of data in the intelligent management systems of the network and server architecture of the internet of military equipment;– to take into account the priority of search by swarm agents of the meta-heuristic algorithm while processing various types of data in intelligent management systems of the network and server architecture of the internet of military equipment;– to conduct an initial display of flock individuals taking into account the type of uncertainty;– to conduct accurate training of metaheuristic algorithms;– to conduct a local and global search taking into account the degree of data noise while processing various types of data in intelligent management systems of the network and server architecture of the internet of military equipment.

Introduction. Based on the analysis, it was found that there is a need to develop tools for managing conflicts between stakeholders in projects for the creation and development of medical facilities. These projects are characterized by complex relationships between government agencies, medical staff, NGOs, patients, and investors, that have their own interests, contradicting each other. The main scientific and applied challenge is the lack of a structured approach to reconciling the interests of stakeholders, high level of conflict, lack of transparency in decision-making processes, and insufficient use of quantitative methods for assessing management effectiveness. The lack of tools for managing conflicts leads to delays in project implementation, inefficient use of resources, and dissatisfaction of stakeholders.Purpose. To substantiate the structure of the process of managing conflicts between stakeholders in projects for the creation and development of medical institutions, which will allow timely alignment of the interests of project stakeholders, reduce the level of conflicts, and ensure effective project implementation, taking into account the specifics of the medical sector.Methods. The study uses a systematic approach, which includes an analysis of scientific research on project management and conflicts in the medical field. Based on the methods of classification and generalization, the main indicators of the effectiveness of conflict management are identified, and approaches to planning and implementing conflict management processes are summarized. To analyze the relationships between the indicators, correlation analysis, construction of dependency graphs, and regression analysis was used. The practical part of the study is based on the example of projects of medical institutions in Lviv region, for which a quantitative assessment of the effectiveness of managing conflicts between stakeholders was carried out.Results. The main stakeholders of projects for the creation and development of medical institutions are identified. Seven main types of stakeholders were identified. Based on the analysis of their interests and contradictions, the structure of the process of managing contradictions and conflicts was substantiated, which consists of five management operations providing analysis of stakeholder interests, identification of potential contradictions and conflicts, planning of the process of managing contradictions and conflicts, implementation of the plan for managing contradictions and conflicts and determining the effectiveness of the process of managing contradictions and conflicts. The proposed structure enables reconciliation of the interests of stakeholders, reduce the number and level of conflicts, and ensure the effective implementation of projects for the creation and development of medical institutions. For the practical implementation of the substantiated process, the author quantifies eight indicators that provide an objective assessment of the effectiveness of managing conflicts between stakeholders in projects for the creation and development of medical institutions and the validity of decision-making. Based on the use of the developed toolkit, a quantitative assessment of medical projects implemented in the Lviv region was carried out. It was found that the conflict index of the projects ranged from 2.0 to 2.5, the level of risks - from 15% to 40%, the consolidated efficiency index - from 0.5 to 0.826, and the level of goal achievement - from 66.6% to 83.3%. The highest performance indicators were achieved in the outpatient clinic opening project, which indicates the effectiveness of stakeholder alignment.Conclusions and proposals. The obtained results indicate that the proposed structure of the conflict management process is an effective tool for aligning the interests of stakeholders and ensuring the successful implementation of projects for the creation and development of medical institutions. Implementation of this process structure will reduce the number of contradictions and the level of conflicts between stakeholders, optimize the use of resources, and increase their satisfaction. Further research is needed to integrate modern information technologies, such as artificial intelligence and intelligent decision support systems, into the process of managing stakeholder conflicts in healthcare facility creation and development projects.

Intelligent decision support for effectively evaluating and selecting ships under uncertainty in marine transportation

This paper describes the structure and function of the intelligent decision support system (IDSS) on the process of nickel matte smelter. The knowledge and model base system based on fuzzy-decision rules are specially suggested. The IDSS possesses the self-learning and adaptive properties, and has been used for managing and analyzing the production information, optimizing the composition of the charge mixture, and deciding the optimal operational conditions. Electric energy consumption has been reduced remarkably and the yield of nickel has been increased.