Models of neuro-fuzzy agents in intelligent environments

被引:2
作者
Shvetcov, Anatoliy [1 ]
机构
[1] Vologda State Univ, 15 Lenin St, Vologda, Russia
来源
XII INTERNATIONAL SYMPOSIUM INTELLIGENT SYSTEMS 2016, (INTELS 2016) | 2017年 / 103卷
基金
俄罗斯基础研究基金会;
关键词
intelligent agents; neuro-fuzzy models; agent-oriented systems; behavioral models; SYSTEMS;
D O I
10.1016/j.procs.2017.01.029
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The set of agent-oriented information systems that interact in a changing structure and operation conditions, is considered as a distributed intellectual environment. Such a representation is corresponding in geographically distributed systems (region, city), corporate information systems of enterprises and organizations, social groups within the various territorial structures, distributed heterogeneous systems and networks territorial entities, enterprises and organizations. On the basis of known models of neuro-fuzzy agents are proposed a model of the fundamental intelligent agent (FIA), operating in a dynamic heterogeneous information environment and carrying out reception of input messages in a usual source language and fuzzy input language, the creation of messages in the inner language to perform reasoning and reflection which can be either conventional or fuzzy, and transmitting to the external environment accurate and fuzzy messages in appropriate languages. A classification of possible FIA models are constructed in distributed intelligent systems and environments. A behaviour model of the FIA, defined Post calculus axiom schemes, combining precise and fuzzy representation of variables and manipulate the internal states, base attributes and logical formulas in fuzzy logic and first-order predicate calculus are constructed. (C) 2017 Published by Elsevier B.V.
引用
收藏
页码:135 / 141
页数:7
相关论文
共 10 条
[1]  
[Anonymous], 2012, SHVETC AN AG OR SYST
[2]   Neural-based learning classifier systems [J].
Dam, Hai H. ;
Abbass, Hussein A. ;
Lokan, Chris ;
Yao, Xin .
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2008, 20 (01) :26-39
[3]   A fuzzy embedded agent-based approach for realizing ambient intelligence in intelligent inhabited environments [J].
Doctor, F ;
Hagras, H ;
Callaghan, V .
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS, 2005, 35 (01) :55-65
[4]  
Fougeres AJ, 2011, Int J of Computer Science Issues, V8, P8
[5]   Cognitive complexity and dynamic personality in agent simulation [J].
Ghasem-Aghaee, Nasser ;
Oren, Tuncer I. .
COMPUTERS IN HUMAN BEHAVIOR, 2007, 23 (06) :2983-2997
[6]   Genetic fuzzy systems: Taxonomy, current research trends and prospects [J].
Herrera F. .
Evolutionary Intelligence, 2008, 1 (01) :27-46
[7]   Fuzzy Emotional COCOMO II Software Cost Estimation (FECSCE) using Multi-Agent Systems [J].
Kazemifard, M. ;
Zaeri, A. ;
Ghasem-Aghaee, N. ;
Nematbakhsh, M. A. ;
Mardukhi, F. .
APPLIED SOFT COMPUTING, 2011, 11 (02) :2260-2270
[8]   Fuzzy agents for product configuration in collaborative and distributed design process [J].
Ostrosi, Egon ;
Fougeres, Alain-Jerome ;
Ferney, Michel .
APPLIED SOFT COMPUTING, 2012, 12 (08) :2091-2105
[9]   Optimization of product configuration assisted by fuzzy agents [J].
Ostrosi E. ;
Fougères A.-J. .
International Journal on Interactive Design and Manufacturing (IJIDeM), 2011, 5 (1) :29-44
[10]   A fuzzy-based approach for partner selection in multi-agent systems [J].
Ren, Fenghui ;
Zhang, Minjie ;
Bai, Quart .
6TH IEEE/ACIS INTERNATIONAL CONFERENCE ON COMPUTER AND INFORMATION SCIENCE, PROCEEDINGS, 2007, :457-+