scholarly journals HIERARCHICAL REPRESENTATION OF CAUSAL RELATIONSHIPS TO DETAIL EXPLANATIONS IN INTELLIGENT SYSTEMS

2021 ◽  
Vol 5 (4) ◽  
pp. 103-108
Author(s):  
Serhii Chalyi ◽  
Volodymyr Leshchynskyi
Keyword(s):  
Decision Making ◽  
Information System ◽  
Intelligent Systems ◽  
Input Data ◽  
Intelligent System ◽  
Decision Making Process ◽  
Causal Relationships ◽  
Hierarchical Representation ◽  
Cause And Effect ◽  
Intelligent Information

The subject of research in the article is the processes of constructing explanations in intelligent systems based on the use of causal dependencies. The aim is to develop a hierarchical representation of causal relationships between the actions of an intelligent system to form an explanation of the process of the system's operation with a given degree of generalization or detailing. Representation of the hierarchy of cause-and-effect relationships allows you to form an explanation at a given level of detail using the input data in the form of a temporally ordered sequence of events reflecting the known actions of an intelligent system. Tasks: structuring the hierarchy of cause-and-effect relationships for known variants of the decision-making process in an intelligent information system, considering the temporal ordering of the corresponding actions; development of a model of a multi-level representation of causal dependencies for description for explanations in an intelligent system. The approaches used are: counterfactual analysis of causality, used to describe alternative dependencies for possible decision-making options; linear temporal logic to reflect the temporal aspect of causation. The following results were obtained. A generalized hierarchy of cause-and-effect relationships is highlighted for the known variants of the process of obtaining recommendations in an intelligent information system based on the temporal ordering of the corresponding decision-making actions. A model of hierarchical representation of causal dependencies has been developed to describe explanations in an intellectual system with a given degree of detail. Conclusions. The scientific novelty of the results obtained is as follows. A model of hierarchical representation of time-ordered causal relationships is proposed to describe the explanations of the operation of an intelligent system with a given degree of detail. At the top level of the hierarchy, the model defines a generalized causal relationship between the event of using the input data and the event of the result of the system's operation. This connection describes the current task that the intelligent information system solves. At the lower level, cause-and-effect relationships are set between events sequential in time, between which there are no other events. At intermediate levels of the hierarchical representation, the causal dependencies of pairs of events are determined, between which there are other events. The developed model creates conditions for constructing explanations with a given degree of detailing of the actions of the decision-making process in an intelligent system. The model also provides the ability to describe early and late anticipation of alternative sequences of the decision-making process by describing causal dependencies for events between which there are other events.

scholarly journals COUNTERFACTUAL TEMPORAL MODEL OF CAUSAL RELATIONSHIPS FOR CONSTRUCTING EXPLANATIONS IN INTELLIGENT SYSTEMS

2021 ◽  
pp. 41-46
Author(s):  
Serhii Chalyi ◽  
Volodymyr Leshchynskyi ◽  
Irina Leshchynska
Keyword(s):  
Information System ◽  
Intelligent Systems ◽  
Intelligent System ◽  
System Development ◽  
Causal Relationships ◽  
Temporal Properties ◽  
Intellectual System ◽  
Transitivity Property ◽  
Temporal Model ◽  
Intelligent Information

The subject of the research is the processes of constructing explanations based on causal relationships between states or actions of an intellectualsystem. An explanation is knowledge about the sequence of causes and effects that determine the process and result of an intelligent informationsystem. The aim of the work is to develop a counterfactual temporal model of cause-and-effect relationships as part of an explanation of the process offunctioning of an intelligent system in order to ensure the identification of causal dependencies based on the analysis of the logs of the behavior ofsuch a system. To achieve the stated goals, the following tasks are solved: determination of the temporal properties of the counterfactual description ofcause-and-effect relationships between actions or states of an intelligent information system; development of a temporal model of causal connections,taking into account both the facts of occurrence of events in the intellectual system, and the possibility of occurrence of events that do not affect theformation of the current decision. Conclusions. The structuring of the temporal properties of causal links for pairs of events that occur sequentially intime or have intermediate events is performed. Such relationships are represented by alternative causal relationships using the temporal operators"Next" and "Future", which allows realizing a counterfactual approach to the representation of causality. A counterfactual temporal model of causalrelationships is proposed, which determines deterministic causal relationships for pairs of consecutive events and pairs of events between which thereare other events, which determines the transitivity property of such dependencies and, accordingly, creates conditions for describing the sequence ofcauses and effects as part of the explanation in intelligent system with a given degree of detail The model provides the ability to determine cause-andeffect relationships, between which there are intermediate events that do not affect the final result of the intelligent information system.

The Central Role of Cognitive Computations in Human-Information Interaction

2018 ◽  
Author(s):  
Wai-Tat Fu ◽  
Jessie Chin ◽  
Q. Vera Liao
Keyword(s):  
Information Systems ◽  
Cognitive Science ◽  
Intelligent Systems ◽  
Intelligent System ◽  
Levels Of Abstraction ◽  
Intelligent Information Systems ◽  
Emergent Behaviour ◽  
Information Interaction ◽  
Intelligent Information

Cognitive science is a science of intelligent systems. This chapter proposes that cognitive science can provide useful perspectives for research on technology-mediated human-information interaction (HII) when HII is cast as emergent behaviour of a coupled intelligent system. It starts with a review of a few foundational concepts related to cognitive computations and how they can be applied to understand the nature of HII. It discusses several important properties of a coupled cognitive system and their implication to designs of information systems. Finally, it covers how levels of abstraction have been useful for cognitive science, and how these levels can inform design of intelligent information systems that are more compatible with human cognitive computations.

Preparation of Sub-Criteria for Mathematical Modeling of Multi-Criteria Decision Making

2014 ◽  
Vol 1020 ◽  
pp. 765-768
Author(s):  
Eva Berankova ◽  
František Kuda ◽  
Stanislav Endel
Keyword(s):  
Decision Making ◽  
Public Service ◽  
Input Data ◽  
Decision Making Process ◽  
Multi Criteria Decision Making ◽  
Individual Selection ◽  
Qualitative And Quantitative ◽  
Data Analyses ◽  
Quantitative Characters ◽  
The Subject

The subject of this paper is to evaluate criteria in the decision-making process for choosing new usable office facilities in light of a big company or public service seeking for new usable office facilities. The criteria defining the requirements for individual selection variants enter into this decision-making process. These criteria have qualitative and quantitative characters. In order to model the criteria, it is desirable that their values are standardized. The method of standardization of these criteria is given in this paper. In this paper, attention is paid to the decision-making process in the course of choosing new usable facilities in administration objects. This decision-making process is based on input data analyses and on conclusions for a certain selection variant resulting from them.

scholarly journals A HYBRID MODEL FOR DECISION-MAKING IN THE INFORMATION AND COMMUNICATIONS TECHNOLOGY SECTOR

2015 ◽  
Vol 21 (5) ◽  
pp. 720-737 ◽  
Author(s):  
Andrés CID-LÓPEZ ◽  
Miguel J. HORNOS ◽  
Ramón Alberto CARRASCO ◽  
Enrique HERRERA-VIEDMA
Keyword(s):  
Decision Making ◽  
Intelligent System ◽  
Daily Basis ◽  
Information And Communications Technology ◽  
Decision Making Process ◽  
Computing With Words ◽  
Communications Technology ◽  
Qualitative Information ◽  
Technology Sector ◽  
Decision Making Model

The majority of businesses in the Information and Communications Technology (ICT) sector face decision-making problems on a daily basis. Most of these problems are based on contexts of uncertainty, where decisions are founded on qualitative information which may be imprecise or perception-based. In these cases, the information which is expressed by experts and users of evaluated services can be treated using processes of computing with words (CW). In this paper, we present a hybrid decision-making model especially designed for the ICT sector whereby the experts have the support of an intelligent system which provides information about the opinions of users related to those problems which are to be analysed. These opinions are obtained by using different mechanisms and techniques when users conduct business with the service provider. In addition, we employ a procedure for obtaining consensus between experts which enriches and strengthens the decision-making process.

scholarly journals An intelligent order allocation system for effective order fulfilment under changing customer demand

2019 ◽  
Vol 255 ◽  
pp. 02002
Author(s):  
K.H. Leung ◽  
K.L. Choy ◽  
H.Y. Lam
Keyword(s):  
Decision Making ◽  
Intelligent System ◽  
Fuzzy Ahp ◽  
Sudden Change ◽  
Pairwise Comparison ◽  
Decision Making Process ◽  
Order Allocation ◽  
Domain Experts ◽  
Customer Order ◽  
Study Results

In today's intense global competition, problems still exist under the umbrella of Just-in-Time application in the field of order management. The management of a firm usually faces difficulty in allocating stock to fulfil customer order, especially in the case of receiving a sudden change request from customers. In order to ease order allocation issues aroused by JIT, an intelligent system, namely, Intelligent Sales Order Handling System (ISOAS), is developed through the integration of fuzzy-AHP approach for decision making process in order allocation. This approach enables the selection of desired sales orders based on multiple criteria which may be quantitative or qualitative in nature, according to the judgment of scholars and domain experts. With ISOAS, customer orders are prioritized with respect to the values according to their performance under each decision making attributes. The degree of confidence of the decision judgements are quantified through the spread of fuzzy numbers with fuzzy pairwise comparison calculations. The approach can transform the fuzziness of human preference into the measurable number, enabling the operation of the AI-based system to assist humans in decision-making. An order allocation case study in a logistics department is demonstrated in this study. Results indicate an improved efficiency during the decision making process.

A Rollout Control Algorithm for Discrete-Time Stochastic Systems

2010 ◽  
Author(s):  
Andreas A. Malikopoulos
Keyword(s):  
Decision Making ◽  
Markov Chain ◽  
Intelligent Systems ◽  
Control Algorithm ◽  
Stochastic Systems ◽  
Intelligent System ◽  
Control Policy ◽  
Suboptimal Control ◽  
Sequential Decision ◽  
Significant Research

The growing demand for making autonomous intelligent systems that can learn how to improve their performance while interacting with their environment has induced significant research on computational cognitive models. Computational intelligence, or rationality, can be achieved by modeling a system and the interaction with its environment through actions, perceptions, and associated costs. A widely adopted paradigm for modeling this interaction is the controlled Markov chain. In this context, the problem is formulated as a sequential decision-making process in which an intelligent system has to select those control actions in several time steps to achieve long-term goals. This paper presents a rollout control algorithm that aims to build an online decision-making mechanism for a controlled Markov chain. The algorithm yields a lookahead suboptimal control policy. Under certain conditions, a theoretical bound on its performance can be established.

scholarly journals KONSEP DASAR DAN LANGKAH-LANGKAH DALAM PENGEMBANGAN SISTEM INFORMASI MANAJEMEN

2019 ◽  
Author(s):  
Dina febiyola
Keyword(s):  
Decision Making ◽  
Information System ◽  
Management Information System ◽  
System Analysis ◽  
Decision Making Process ◽  
Management Information ◽  
Literature Study ◽  
Organization Management ◽  
Present Information ◽  
Implementation Stage

The purpose of this study is to find out how the step to develop management information system. In this article, researchers ute the literature study method. Management Information System is a human / machine system that is integrated to present information to suppor the operational functions of the organization, management, and decision-making process whitin an organization. The steps in its development are conducting the system investigation stage, the system analysis stage, the system design stge, and the system implementation stage.

scholarly journals Research and Implementation of Mobile Internet Management Optimization and Intelligent Information System Based on Smart Decision

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yanqing Han ◽  
Yuyan Lei ◽  
Zimin Bao ◽  
Qingyuan Zhou
Keyword(s):  
Decision Making ◽  
Information System ◽  
Information Systems ◽  
Mobile Internet ◽  
Fusion Algorithm ◽  
Intelligent Information Systems ◽  
Tier System ◽  
Management Optimization ◽  
Intelligent Information ◽  
Fuzzy Fusion

The way by which artificial intelligence is implemented is similar to the thinking process of the human brain. People obtain information about external conditions through five senses, namely, vision, hearing, smell, taste, and touch, and, through the further processing of the brain, it forms meaningful decision-making elements. Then, through the process of analysis and reasoning, further decisions are made. In the information age, the application of intelligent management information systems in various fields has promoted the modernization and intelligence of social development. From the perspective of intelligent decision-making, this paper analyzes the requirements of intelligent information systems and designs an intelligent information system based on mobile Internet management optimization, including system management optimization, and proposes an environment-based layer, network transport layer, and the three-tier system architecture of the smart service application layer. Finally, this paper considers the problem of data fusion after system expansion. According to the existing fuzzy fusion algorithm, a weight-based fuzzy fusion algorithm is proposed. The simulation analysis shows that the algorithm can be effectively applied in intelligent information systems.

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