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Books on the topic 'Multi-Agent-System'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 January 2025

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1

Garijo, Francisco J., and Magnus Boman, eds. Multi-Agent System Engineering. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48437-x.

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2

Schumacher, Michael. Objective Coordination in Multi-Agent System Engineering. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44933-7.

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3

Ning, Zhao. Multi-agent system in electronic share trading. Oxford: Oxford Brookes University, 2002.

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4

Patel, Keyurkumar, and Axaykumar Mehta. Discrete-Time Sliding Mode Protocols for Discrete Multi-Agent System. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-6311-9.

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5

Raman, Paranjape, and Sadanand Asha, eds. Multi-agent systems for healthcare simulation and modeling: Applications for system improvement. Hershey PA: Medical Information Science Reference, 2010.

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6

Raman, Paranjape, and Sadanand Asha, eds. Multi-agent systems for healthcare simulation and modeling: Applications for system improvement. Hershey, PA: Medical Information Science Reference, 2010.

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7

European, Workshop on Modelling Autonomous Agents in a. Multi-Agent World (9th 1999 Valencia Spain). Multi-agent system engineering: 9th European Workshop on Modelling Autonomous Agents in a Multi-Agent World, MAAMAW'99, bValencia, Spain, June/July, 1999 : proceedings. Berlin: Springer, 1999.

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8

Roddy, Kimberly A. Modeling human and organizational behavior using a relation-centric multi-agent system design paradigm. Monterey, Calif: Naval Postgraduate School, 2000.

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9

Murav'ev, Dmitriy, Aleksandr Rahmangulov, Nikita Osincev, Sergey Kornilov, and Aleksandr Cyganov. The system "seaport - "dry" port". ru: INFRA-M Academic Publishing LLC., 2022. http://dx.doi.org/10.12737/1816639.

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The monograph presents an approach to solving the problem of increasing the throughput and processing capacity of seaports in conditions of limiting their territorial dislocation and increasing the unevenness of external and internal cargo flows. The basis of the approach is the proposed system of the main parameters of the dry port and the methodology of simulation modeling of the functioning of the system "seaport - dry port". The material is illustrated with examples of the implementation of the developed approach, including model scenarios of multi-agent optimization of the parameters of the system under study. The proposed approach and the developed methodology can be used to justify management decisions on the balanced development of transport and logistics infrastructure of the regions hosting sea and dry ports. It is intended for specialists of transport and logistics companies, engineering and technical workers engaged in solving problems in the field of logistics, supply chain management and transport infrastructure design. In addition, it is recommended to students in the following programs: postgraduate studies 23.06.01 "Land transport engineering and technology" (focus "Transport and transport-technological systems of the country, its regions and cities, organization of production in transport") and 27.06.01 "Management in technical systems" (focus "Management of transportation processes"); master's degree 23.04.01 "Technology of transport processes" (profile "Organization of transportation and management in a single transport system"); bachelor's degree 38.03.02 "Management" (profile "Logistics") and 23.03.01 "Technology of transport processes".

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10

Potiron, Katia. From Fault Classification to Fault Tolerance for Multi-Agent Systems. London: Springer London, 2013.

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11

Mařík, Vladimír. Holonic and multi-agent systems for manufacturing: Proceedings. Berlin: Springer, 2003.

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12

Nagi, Khaled. Transactional Agents: Towards a Robust Multi-Agent System. Springer London, Limited, 2003.

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13

Transactional agents: Towards a robust multi-agent system. Berlin: Springer, 2001.

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14

Modeling Tactical Level Combat Using a Multi-Agent System Design Paradigm (GI Agent). Storming Media, 2001.

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15

Abraham, Ajith, Anand Jayant Kulkarni, and Kang Tai. Probability Collectives: A Distributed Multi-Agent System Approach for Optimization. Springer, 2015.

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16

Mehta, Axaykumar, and Keyurkumar Patel. Discrete-Time Sliding Mode Protocols for Discrete Multi-Agent System. Springer Singapore Pte. Limited, 2021.

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17

Mehta, Axaykumar, and Keyurkumar Patel. Discrete-Time Sliding Mode Protocols for Discrete Multi-Agent System. Springer, 2020.

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18

Abraham, Ajith, Anand Jayant Kulkarni, and Kang Tai. Probability Collectives: A Distributed Multi-Agent System Approach for Optimization. Springer International Publishing AG, 2016.

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19

Abraham, Ajith, Anand Jayant Kulkarni, and Kang Tai. Probability Collectives: A Distributed Multi-agent System Approach for Optimization. Springer, 2015.

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20

Schumacher, Michael. Objective Coordination in Multi-Agent System Engineering: Design and Implementation. Springer, 2003.

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21

Zhao, Guanglei, and Hailong Cui. Cooperative Control of Multi-Agent Systems: A Hybrid System Approach. Springer, 2024.

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22

Plinere, Darja. Development of a Multi-agent System for Supply Chain Management Efficiency Improvement. RTU Press, 2021. http://dx.doi.org/10.7250/9789934226915.

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The aim of the doctoral thesis is to develop an approach to the multi-agent system development that ensures the improvement of supply chain management efficiency. The developed approach allows creating a multi-agent system that can be used for various supply chain participants, as it reviews main supply chain processes and tasks to be solved. Various inventory management methods were used in the developed multi-agent system: forecasting algorithms, ABC analysis, and inventory replenishment point determination. Production scheduling and rescheduling were also used in the multi-agent system to reduce the total production time.

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23

Extensible Multi-Agent System for Heterogeneous Database Association Rule Mining and Unification. Storming Media, 1999.

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24

Nagi, Khaled. Transactional Agents: Towards a Robust Multi-Agent System (Lecture Notes in Computer Science). Springer, 2002.

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25

Ding, Lei, Qing-Long Han, and Boda Ning. Distributed Control and Optimization of Networked Microgrids: A Multi-Agent System Based Approach. Springer International Publishing AG, 2022.

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26

Distributed Control and Optimization of Networked Microgrids: A Multi-Agent System Based Approach. Springer International Publishing AG, 2023.

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27

Garijo, Francisco J., and Magnus Boman. Multi-Agent System Engineering: 9th European Workshop on Modelling Autonomous Agents in a Multi-Agent World, MAAMAW'99 Valencia, Spain, June 30 - July 2, 1999 Proceedings. Springer, 2007.

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28

Modeling Bluetooth Radio Technology Simulation Using Multi-Agent Based System and Genetic Algorithm Design Paradigm. Storming Media, 2001.

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29

Objective Coordination in Multi-Agent System Engineering: Design and Implementation (Lecture Notes in Computer Science). Springer, 2001.

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30

Multi-Agent System Engineering: 9th European Workshop on Modelling Autonomous Agents in a Multi-Agent World, MAAMAW'99 Valencia, Spain, June 30 - July ... / Lecture Notes in Artificial Intelligence). Springer, 1999.

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31

Evaluation of a Multi-Agent System for Simulation and Analysis of Distributed Denial-of-Service Attacks. Storming Media, 2003.

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32

Aryeh, Luis David. Semiotic Mind of God: A Multi-Agent System Model of the Creative Processes in the God's Mind. Gur Aryeh Institute, 2024.

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33

Modeling Conventional Land Combat in a Multi-Agent System Using Generalization of the Different Combat Entities and Combat Operations. Storming Media, 2001.

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34

Bordini, Rafael H., Mehdi Dastani, and Amal El Fallah Seghrouchni. Multi-Agent Programming. Springer, 2008.

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35

Lindemann, Gabriela, Mario Paolucci, and Daniel Moldt. Regulated Agent-Based Social Systems: First International Workshop, RASTA 2002, Bologna, Italy, July 16, 2002, Revised Selected and Invited Papers. Springer London, Limited, 2004.

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36

Chen, Guanrong, Wenwu Yu, Guanghui Wen, and Jinde Cao. Distributed Cooperative Control of Multi-Agent Systems. Wiley & Sons, Limited, John, 2016.

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37

Chen, Guanrong, Wenwu Yu, Guanghui Wen, and Jinde Cao. Distributed Cooperative Control of Multi-Agent Systems. Wiley & Sons, Incorporated, John, 2016.

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38

Chen, Guanrong, Wenwu Yu, Guanghui Wen, and Jinde Cao. Distributed Cooperative Control of Multi-Agent Systems. Wiley & Sons, Incorporated, John, 2016.

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39

Chen, Guanrong, Wenwu Yu, Guanghui Wen, and Jinde Cao. Distributed Cooperative Control of Multi-Agent Systems. Wiley & Sons, Limited, John, 2016.

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40

Bordini, Rafael H., Jürgen Dix, Mehdi Dastani, and Amal El Fallah Seghrouchni. Multi-Agent Programming: Languages, Platforms and Applications. Springer, 2010.

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41

Bordini, Rafael H., Jürgen Dix, Mehdi Dastani, and Amal El Fallah Seghrouchni. Multi-Agent Programming : : Languages, Tools and Applications. Springer, 2014.

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42

Smart, Paul R. Mandevillian Intelligence. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198801764.003.0013.

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Mandevillian intelligence is a specific form of collective intelligence in which individual cognitive shortcomings, limitations, and biases play a positive functional role in yielding various forms of collective cognitive success. When this idea is transposed to the epistemological domain, mandevillian intelligence emerges as the idea that individual forms of intellectual vice may, on occasion, support the epistemic performance of some form of multi-agent ensemble, such as a socio-epistemic system, a collective doxastic agent, or an epistemic group agent. As a specific form of collective intelligence, mandevillian intelligence is relevant to a number of debates in social epistemology, especially those that seek to understand how group (or collective) knowledge arises from the interactions between a collection of individual epistemic agents.

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43

Ehresmann, Andrée. Applications of Categories to Biology and Cognition. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198748991.003.0015.

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Mathematical models used in biology are generally adapted from physics and relate to specific local processes. Category theory helps developing global dynamic models account for the main specificities of living systems: (i) The system is evolutionary, with a tangled hierarchy of interacting components, which change over time. (ii) It develops a robust and flexible memory up to the emergence of components and processes of increasing complexity. (iii) It has a multi-agent, multi-temporality, self-organization. This chapter presents such a model, the Memory Evolutive Systems, which in particular characterizes the property at the root of emergence and flexibility. A main application is the model MENS for a neurocognitive system which proposes a physically based “theory of mind”, up to the emergence of higher cognitive processes such as consciousness, anticipation, and creativity.

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44

Multi-Agent Programming: Languages, Platforms and Applications (Multiagent Systems, Artificial Societies, and Simulated Organizations Book 15). Springer, 2006.

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45

Integrated Fault Diagnosis and Control Design of Linear Complex Systems. The Institution of Engineering and Technology, 2018.

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46

Meskin, Nader, Mohammadreza Davoodi, and Khashayar Khorasani. Integrated Fault Diagnosis and Control Design of Linear Complex Systems. Institution of Engineering & Technology, 2018.

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47

Vinod, Nikhra. COVID-19 and Long Covid: Organs Damage and Dysfunctions, and Implications for Clinical Course. Heighten Science Publications Inc., 2021. http://dx.doi.org/10.29328/ebook1005.

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Like any other infectious disease, the prognosis of COVID-19 is influenced by infecting agent, the SARS-CoV-2 virus load and the extent of organs affliction and damage. COVID-19 having a propensity for multiorgan involvement carries an adverse prognosis during the clinical course as well as later during the post-recovery period persisting as Long Covid. The direct cytopathic effects of SARS-CoV-2 virus and the erratic and hyper-inflammatory response lead to tissue injury in various organs coupled with physiological dysfunctions and complications. In fact, the multi-system manifestations of COVID-19 are caused by a combination of specific host defence responses with associated inflammatory activity and vascular involvement with coagulopathy and a distinct propensity to develop thromboembolic complications. Simultaneously, comorbidities such as diabetes, hypertension and cardiovascular diseases influence the disease severity and mortality.

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48

(Editor), Torsten Eymann, Franziska Klügl (Editor), Winfried Lamersdorf (Editor), Matthias Klusch (Editor), and Michael N. Huhns (Editor), eds. Multiagent System Technologies: Third German Conference, MATES 2005, Koblenz, Germany, September 11-13, 2005, Proceedings (Lecture Notes in Computer Science). Springer, 2005.

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49

(Editor), Koichi Kurumatani, Shu-Heng Chen (Editor), and Azuma Ohuchi (Editor), eds. Multi-Agent for Mass User Support: International Workshop, MAMUS 2003Acapulco, Mexico, August 10, 2003Revised and Invited Papers (Lecture Notes in Computer ... / Lecture Notes in Artificial Intelligence). Springer, 2004.

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50

Trzebiatowski, Gabriela Lindemann-v., Jörg Denzinger, Rainer Unland, and Ingo J. Timm. Multiagent System Technologies: Second German Conference, MATES 2004, Erfurt, Germany, September 29-30, 2004, Proceedings. Springer London, Limited, 2004.

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