18,138 research outputs found
Neuro-fuzzy knowledge processing in intelligent learning environments for improved student diagnosis
In this paper, a neural network implementation for a fuzzy logic-based model of the diagnostic process is proposed as a means to achieve accurate student diagnosis and updates of the student model in Intelligent Learning Environments. The neuro-fuzzy synergy allows the diagnostic model to some extent "imitate" teachers in diagnosing students' characteristics, and equips the intelligent learning environment with reasoning capabilities that can be further used to drive pedagogical decisions depending on the student learning style. The neuro-fuzzy implementation helps to encode both structured and non-structured teachers' knowledge: when teachers' reasoning is available and well defined, it can be encoded in the form of fuzzy rules; when teachers' reasoning is not well defined but is available through practical examples illustrating their experience, then the networks can be trained to represent this experience. The proposed approach has been tested in diagnosing aspects of student's learning style in a discovery-learning environment that aims to help students to construct the concepts of vectors in physics and mathematics. The diagnosis outcomes of the model have been compared against the recommendations of a group of five experienced teachers, and the results produced by two alternative soft computing methods. The results of our pilot study show that the neuro-fuzzy model successfully manages the inherent uncertainty of the diagnostic process; especially for marginal cases, i.e. where it is very difficult, even for human tutors, to diagnose and accurately evaluate students by directly synthesizing subjective and, some times, conflicting judgments
ΠΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠ° Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΠΌΠ½ΠΎΠ³ΠΎΠΌΠ΅ΡΠ½ΡΡ Π½Π΅ΡΠ΅ΡΠΊΠΈΡ ΠΎΡΠ½ΠΎΡΠ΅Π½ΠΈΠΉ
Π ΠΎΠ·Π³Π»ΡΠ½ΡΡΠΎ Π²ΡΠ΄Π½ΠΎΠ²Π»Π΅Π½Π½Ρ ΠΏΡΠΈΡΠΈΠ½ (Π΄ΡΠ°Π³Π½ΠΎΠ·ΡΠ²) Π·Π° ΡΠΏΠΎΡΡΠ΅ΡΠ΅ΠΆΡΠ²Π°Π½ΠΈΠΌΠΈ Π½Π°ΡΠ»ΡΠ΄ΠΊΠ°ΠΌΠΈ (ΡΠΈΠΌΠΏΡΠΎΠΌΠ°ΠΌΠΈ) Π½Π° ΠΎΡΠ½ΠΎΠ²Ρ Π±Π°Π³Π°ΡΠΎΠ²ΠΈΠΌΡΡΠ½ΠΈΡ
Π½Π΅ΡΡΡΠΊΠΈΡ
Π²ΡΠ΄Π½ΠΎΡΠ΅Π½Ρ Ρ ΡΠΎΠ·ΡΠΈΡΠ΅Π½ΠΎΠ³ΠΎ ΠΊΠΎΠΌΠΏΠΎΠ·ΠΈΡΡΠΉΠ½ΠΎΠ³ΠΎ ΠΏΡΠ°Π²ΠΈΠ»Π° Π²ΠΈΠ²Π΅Π΄Π΅Π½Π½Ρ. ΠΡΠΎΠ΅ΠΊΡΡΠ²Π°Π½Π½Ρ Π½Π΅ΡΡΡΠΊΠΎΡ ΡΠΈΡΡΠ΅ΠΌΠΈ Π΄ΡΠ°Π³Π½ΠΎΡΡΠΈΠΊΠΈ ΠΏΠΎΠ»ΡΠ³Π°Ρ Ρ ΡΠΎΠ·Π²βΡΠ·Π°Π½Π½Ρ Π½Π΅ΡΡΡΠΊΠΈΡ
Π»ΠΎΠ³ΡΡΠ½ΠΈΡ
ΡΡΠ²Π½ΡΠ½Ρ ΡΡΠΌΡΡΠ½ΠΎ Π· Π½Π°Π»Π°ΡΡΡΠ²Π°Π½Π½ΡΠΌ Π½Π΅ΡΡΡΠΊΠΈΡ
Π²ΡΠ΄Π½ΠΎΡΠ΅Π½Ρ Π½Π° ΠΎΡΠ½ΠΎΠ²Ρ Π΅ΠΊΡΠΏΠ΅ΡΡΠ½ΠΎ-Π΅ΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΠ°Π»ΡΠ½ΠΎΡ ΡΠ½ΡΠΎΡΠΌΠ°ΡΡΡ. ΠΠ°ΠΏΡΠΎΠΏΠΎΠ½ΠΎΠ²Π°Π½ΠΎ ΠΌΠ΅ΡΠΎΠ΄ ΡΠΎΠ·Π²βΡΠ·Π°Π½Π½Ρ ΡΠΈΡΡΠ΅ΠΌ Π½Π΅ΡΡΡΠΊΠΈΡ
Π»ΠΎΠ³ΡΡΠ½ΠΈΡ
ΡΡΠ²Π½ΡΠ½Ρ Π· ΡΠΎΠ·ΡΠΈΡΠ΅Π½ΠΎΡ max-min ΠΊΠΎΠΌΠΏΠΎΠ·ΠΈΡΡΡΡ. ΠΠΎΠ²Π΅Π΄Π΅Π½ΠΎ Π²Π»Π°ΡΡΠΈΠ²ΠΎΡΡΡ ΠΌΠ½ΠΎΠΆΠΈΠ½ΠΈ ΡΠΎΠ·Π²βΡΠ·ΠΊΡΠ² ΡΠ°ΠΊΠΈΡ
ΡΠΈΡΡΠ΅ΠΌ. ΠΠ°Π΄Π°ΡΡ Π·Π½Π°Ρ
ΠΎΠ΄ΠΆΠ΅Π½Π½Ρ ΠΌΠ½ΠΎΠΆΠΈΠ½ΠΈ ΡΠΎΠ·Π²βΡΠ·ΠΊΡΠ² ΡΡΠΎΡΠΌΡΠ»ΡΠΎΠ²Π°Π½ΠΎ Ρ Π²ΠΈΠ³Π»ΡΠ΄Ρ Π·Π°Π΄Π°ΡΡ ΠΎΠΏΡΠΈΠΌΡΠ·Π°ΡΡΡ, Π΄Π»Ρ ΡΠΎΠ·Π²βΡΠ·Π°Π½Π½Ρ ΡΠΊΠΎΡ Π²ΠΈΠΊΠΎΡΠΈΡΡΠ°Π½ΠΎ Π³Π΅Π½Π΅ΡΠΈΠΊΠΎ-Π½Π΅ΠΉΡΠΎΠ½Π½ΠΈΠΉ ΠΏΡΠ΄Ρ
ΡΠ΄. ΠΠ°Π»Π°ΡΡΡΠ²Π°Π½Π½Ρ ΠΏΠΎΠ»ΡΠ³Π°Ρ Ρ Π²ΠΈΠ±ΠΎΡΡ ΡΠ°ΠΊΠΈΡ
ΡΡΠ½ΠΊΡΡΠΉ Π½Π°Π»Π΅ΠΆΠ½ΠΎΡΡΡ Π½Π΅ΡΡΡΠΊΠΈΡ
ΠΏΡΠΈΡΠΈΠ½ Ρ Π½Π°ΡΠ»ΡΠ΄ΠΊΡΠ², Π° ΡΠ°ΠΊΠΎΠΆ Π½Π΅ΡΡΡΠΊΠΈΡ
Π²ΡΠ΄Π½ΠΎΡΠ΅Π½Ρ, ΡΠΊΡ ΠΌΡΠ½ΡΠΌΡΠ·ΡΡΡΡ ΡΡΠ·Π½ΠΈΡΡ ΠΌΡΠΆ ΠΌΠΎΠ΄Π΅Π»ΡΠ½ΠΈΠΌΠΈ Ρ Π΅ΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΠ°Π»ΡΠ½ΠΈΠΌΠΈ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠ°ΠΌΠΈ Π΄ΡΠ°Π³Π½ΠΎΡΡΠΈΠΊΠΈ. ΠΠ°ΠΏΡΠΎΠΏΠΎΠ½ΠΎΠ²Π°Π½ΠΈΠΉ ΠΏΡΠ΄Ρ
ΡΠ΄ ΠΏΡΠΎΡΠ»ΡΡΡΡΠΎΠ²Π°Π½ΠΎ ΠΊΠΎΠΌΠΏβΡΡΠ΅ΡΠ½ΠΈΠΌ Π΅ΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΠΎΠΌ Ρ ΠΏΡΠΈΠΊΠ»Π°Π΄ΠΎΠΌ ΡΠ΅Ρ
Π½ΡΡΠ½ΠΎΡ Π΄ΡΠ°Π³Π½ΠΎΡΡΠΈΠΊΠΈ.This paper deals with restoration of the causes (diagnoses) through the observed effects (symptoms) on the basis of multivariable fuzzy relations and the extended compositional rule of inference. The design of a diagnostic fuzzy system consists of solving fuzzy relational equations together with tuning of fuzzy relations on the basis of information from experts and experiments. We propose a method for solving fuzzy relational equations with the extended max-min composition. We also prove the properties of the solution set for such systems. The problem of finding the solution set is formulated in the form of the optimization problem, which is solved using genetic algorithms and neural networks. The essence of tuning consists of the selection such membership functions for fuzzy causes and effects, and also fuzzy relations, which minimize the difference between model and experimental results of a diagnosis. The proposed approach is illustrated by the computer experiment and the example of a technical diagnosis.Π Π°ΡΡΠΌΠΎΡΡΠ΅Π½ΠΎ Π²ΠΎΡΡΡΠ°Π½ΠΎΠ²Π»Π΅Π½ΠΈΠ΅ ΠΏΡΠΈΡΠΈΠ½ (Π΄ΠΈΠ°Π³Π½ΠΎΠ·ΠΎΠ²) ΠΏΠΎ Π½Π°Π±Π»ΡΠ΄Π°Π΅ΠΌΡΠΌ ΡΠ»Π΅Π΄ΡΡΠ²ΠΈΡΠΌ (ΡΠΈΠΌΠΏΡΠΎΠΌΠ°ΠΌ) Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΠΌΠ½ΠΎΠ³ΠΎΠΌΠ΅ΡΠ½ΡΡ
Π½Π΅ΡΠ΅ΡΠΊΠΈΡ
ΠΎΡΠ½ΠΎΡΠ΅Π½ΠΈΠΉ ΠΈ ΡΠ°ΡΡΠΈΡΠ΅Π½Π½ΠΎΠ³ΠΎ ΠΊΠΎΠΌΠΏΠΎΠ·ΠΈΡΠΈΠΎΠ½Π½ΠΎΠ³ΠΎ ΠΏΡΠ°Π²ΠΈΠ»Π° Π²ΡΠ²Π΅Π΄Π΅Π½ΠΈΡ. ΠΡΠΎΠ΅ΠΊΡΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ Π½Π΅ΡΠ΅ΡΠΊΠΎΠΉ ΡΠΈΡΡΠ΅ΠΌΡ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠΈ ΡΠΎΡΡΠΎΠΈΡ Π² ΡΠ΅ΡΠ΅Π½ΠΈΠΈ Π½Π΅ΡΠ΅ΡΠΊΠΈΡ
Π»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΡΠ°Π²Π½Π΅Π½ΠΈΠΉ ΡΠΎΠ²ΠΌΠ΅ΡΡΠ½ΠΎ Ρ Π½Π°ΡΡΡΠΎΠΉΠΊΠΎΠΉ Π½Π΅ΡΠ΅ΡΠΊΠΈΡ
ΠΎΡΠ½ΠΎΡΠ΅Π½ΠΈΠΉ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΡΠΊΡΠΏΠ΅ΡΡΠ½ΠΎ-ΡΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΠ°Π»ΡΠ½ΠΎΠΉ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ. ΠΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½ ΠΌΠ΅ΡΠΎΠ΄ ΡΠ΅ΡΠ΅Π½ΠΈΡ ΡΠΈΡΡΠ΅ΠΌ Π½Π΅ΡΠ΅ΡΠΊΠΈΡ
Π»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΡΠ°Π²Π½Π΅Π½ΠΈΠΉ Ρ ΡΠ°ΡΡΠΈΡΠ΅Π½Π½ΠΎΠΉ max-min ΠΊΠΎΠΌΠΏΠΎΠ·ΠΈΡΠΈΠ΅ΠΉ. ΠΠΎΠΊΠ°Π·Π°Π½Ρ ΡΠ²ΠΎΠΉΡΡΠ²Π° ΠΌΠ½ΠΎΠΆΠ΅ΡΡΠ²Π° ΡΠ΅ΡΠ΅Π½ΠΈΠΉ ΡΠ°ΠΊΠΈΡ
ΡΠΈΡΡΠ΅ΠΌ. ΠΠ°Π΄Π°ΡΠ° Π½Π°Ρ
ΠΎΠΆΠ΄Π΅Π½ΠΈΡ ΠΌΠ½ΠΎΠΆΠ΅ΡΡΠ²Π° ΡΠ΅ΡΠ΅Π½ΠΈΠΉ ΡΡΠΎΡΠΌΡΠ»ΠΈΡΠΎΠ²Π°Π½Π° Π² Π²ΠΈΠ΄Π΅ Π·Π°Π΄Π°ΡΠΈ ΠΎΠΏΡΠΈΠΌΠΈΠ·Π°ΡΠΈΠΈ, Π΄Π»Ρ ΡΠ΅ΡΠ΅Π½ΠΈΡ ΠΊΠΎΡΠΎΡΠΎΠΉ ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΠ΅ΡΡΡ Π³Π΅Π½Π΅ΡΠΈΠΊΠΎ-Π½Π΅ΠΉΡΠΎΠ½Π½ΡΠΉ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄. ΠΠ°ΡΡΡΠΎΠΉΠΊΠ° ΡΠΎΡΡΠΎΠΈΡ Π² Π²ΡΠ±ΠΎΡΠ΅ ΡΠ°ΠΊΠΈΡ
ΡΡΠ½ΠΊΡΠΈΠΉ ΠΏΡΠΈΠ½Π°Π΄Π»Π΅ΠΆΠ½ΠΎΡΡΠΈ Π½Π΅ΡΠ΅ΡΠΊΠΈΡ
ΠΏΡΠΈΡΠΈΠ½ ΠΈ ΡΠ»Π΅Π΄ΡΡΠ²ΠΈΠΉ, Π° ΡΠ°ΠΊΠΆΠ΅ Π½Π΅ΡΠ΅ΡΠΊΠΈΡ
ΠΎΡΠ½ΠΎΡΠ΅Π½ΠΈΠΉ, ΠΊΠΎΡΠΎΡΡΠ΅ ΠΌΠΈΠ½ΠΈΠΌΠΈΠ·ΠΈΡΡΡΡ ΠΎΡΠ»ΠΈΡΠΈΠ΅ ΠΌΠ΅ΠΆΠ΄Ρ ΠΌΠΎΠ΄Π΅Π»ΡΠ½ΡΠΌΠΈ ΠΈ ΡΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΠ°Π»ΡΠ½ΡΠΌΠΈ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠ°ΠΌΠΈ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠΈ. ΠΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½Π½ΡΠΉ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄ ΠΏΡΠΎΠΈΠ»Π»ΡΡΡΡΠΈΡΠΎΠ²Π°Π½ ΠΊΠΎΠΌΠΏΡΡΡΠ΅ΡΠ½ΡΠΌ ΡΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΠΎΠΌ ΠΈ ΠΏΡΠΈΠΌΠ΅ΡΠΎΠΌ ΡΠ΅Ρ
Π½ΠΈΡΠ΅ΡΠΊΠΎΠΉ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠΈ
The VEX-93 environment as a hybrid tool for developing knowledge systems with different problem solving techniques
The paper describes VEX-93 as a hybrid environment for developing
knowledge-based and problem solver systems. It integrates methods and
techniques from artificial intelligence, image and signal processing and
data analysis, which can be mixed. Two hierarchical levels of reasoning
contains an intelligent toolbox with one upper strategic inference engine
and four lower ones containing specific reasoning models: truth-functional
(rule-based), probabilistic (causal networks), fuzzy (rule-based) and
case-based (frames). There are image/signal processing-analysis capabilities
in the form of programming languages with more than one hundred primitive
functions.
User-made programs are embeddable within knowledge basis, allowing the
combination of perception and reasoning. The data analyzer toolbox contains
a collection of numerical classification, pattern recognition and ordination
methods, with neural network tools and a data base query language at
inference engines's disposal.
VEX-93 is an open system able to communicate with external computer programs
relevant to a particular application. Metaknowledge can be used for
elaborate conclusions, and man-machine interaction includes, besides windows
and graphical interfaces, acceptance of voice commands and production of
speech output.
The system was conceived for real-world applications in general domains, but
an example of a concrete medical diagnostic support system at present under
completion as a cuban-spanish project is mentioned.
Present version of VEX-93 is a huge system composed by about one and half
millions of lines of C code and runs in microcomputers under Windows 3.1.Postprint (published version
CBR and MBR techniques: review for an application in the emergencies domain
The purpose of this document is to provide an in-depth analysis of current reasoning engine practice and the integration strategies of Case Based Reasoning and Model Based Reasoning that will be used in the design and development of the RIMSAT system.
RIMSAT (Remote Intelligent Management Support and Training) is a European Commission funded project designed to:
a.. Provide an innovative, 'intelligent', knowledge based solution aimed at improving the quality of critical decisions
b.. Enhance the competencies and responsiveness of individuals and organisations involved in highly complex, safety critical incidents - irrespective of their location.
In other words, RIMSAT aims to design and implement a decision support system that using Case Base Reasoning as well as Model Base Reasoning technology is applied in the management of emergency situations.
This document is part of a deliverable for RIMSAT project, and although it has been done in close contact with the requirements of the project, it provides an overview wide enough for providing a state of the art in integration strategies between CBR and MBR technologies.Postprint (published version
AI and OR in management of operations: history and trends
The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested
Intelligent systems in manufacturing: current developments and future prospects
Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS
An Intelligent Knowledge Management System from a Semantic Perspective
Knowledge Management Systems (KMS) are important tools by which organizations can better use information and, more importantly, manage knowledge. Unlike other strategies, knowledge management (KM) is difficult to define because it encompasses a range of concepts, management tasks, technologies, and organizational practices, all of which come under the umbrella of the information management. Semantic approaches allow easier and more efficient training, maintenance, and support knowledge. Current ICT markets are dominated by relational databases and document-centric information technologies, procedural algorithmic programming paradigms, and stack architecture. A key driver of global economic expansion in the coming decade is the build-out of broadband telecommunications and the deployment of intelligent services bundling. This paper introduces the main characteristics of an Intelligent Knowledge Management System as a multiagent system used in a Learning Control Problem (IKMSLCP), from a semantic perspective. We describe an intelligent KM framework, allowing the observer (a human agent) to learn from experience. This framework makes the system dynamic (flexible and adaptable) so it evolves, guaranteeing high levels of stability when performing his domain problem P. To capture by the agent who learn the control knowledge for solving a task-allocation problem, the control expert system uses at any time, an internal fuzzy knowledge model of the (business) process based on the last knowledge model.knowledge management, fuzzy control, semantic technologies, computational intelligence
Decision Making in the Medical Domain: Comparing the Effectiveness of GP-Generated Fuzzy Intelligent Structures
ABSTRACT: In this work, we examine the effectiveness of two intelligent models in medical domains. Namely, we apply grammar-guided genetic programming to produce fuzzy intelligent structures, such as fuzzy rule-based systems and fuzzy Petri nets, in medical data mining tasks. First, we use two context-free grammars to describe fuzzy rule-based systems and fuzzy Petri nets with genetic programming. Then, we apply cellular encoding in order to express the fuzzy Petri nets with arbitrary size and topology. The models are examined thoroughly in four real-world medical data sets. Results are presented in detail and the competitive advantages and drawbacks of the selected methodologies are discussed, in respect to the nature of each application domain. Conclusions are drawn on the effectiveness and efficiency of the presented approach
- β¦