Skip to main content

Synergies of Soft Computing and M&S

  • Chapter
  • First Online:
Body of Knowledge for Modeling and Simulation

Abstract

This chapter of the SCS M&S Body of Knowledge starts with section on fuzzy logic and simulation, neural networks, and artificial intelligence. It then provides a detailed look at the agent metaphor and agent-based simulation.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 129.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Zadeh LA (1965) Fuzzy sets. Inf Control 8:338–353

    Article  MATH  Google Scholar 

  2. Zadeh LA (1983) The role of fuzzy logic in the management of uncertainty in expert systems. Fuzzy Sets Syst 199–227

    Google Scholar 

  3. Dubois D, Prade H (1993) Fuzzy set in approximate reasoning, part 1. Fuzzy Set Syst 40

    Google Scholar 

  4. Dubois D, Fargier H, Fortin J (2004) A generalized vertex method for computing with fuzzy intervals. In Proceedings of the international conference on fuzzy systems, IEEE edn. IEEE, Budapest, Hungary, pp 541–546

    Google Scholar 

  5. Zadeh LA (1988) Fuzzy logic. Computer 21(4):83–93

    Google Scholar 

  6. Saleem K (2008) Fuzzy time control modeling of discrete event systems. In: Proceedings of the World Congress on Engineering and Computer Science, International Association of Engineers (IAENG), pp 683–688

    Google Scholar 

  7. Son M-J, Kim T-W (2012) Torpedo evasion simulation of underwater vehicle using fuzzy-logic-based tactical decision making in script tactics manager. Expert Syst Appl 39(9):7995–8012

    Article  Google Scholar 

  8. Bisgambiglia PA, Capocchi L, Bisgambiglia P, Garredu S (2010) Fuzzy inference models for discrete event systems. In: 2010 IEEE international conference on Fuzzy Systems (FUZZ), pp 1–8

    Google Scholar 

  9. Bisgambiglia P-A, Capocchi L, de Gentili E, Bisgambiglia P (2007) Manipulation of incomplete or fuzzy data for DEVS-based systems. In: International modeling and simulation multiconference (IMSM)—conceptual modeling simulation (CMS), pp 87–92

    Google Scholar 

  10. Bisgambiglia P-A, de Gentili E, Santucci J, Bisgambiglia P (2006) DEVS-Flou: a discrete events and fuzzy sets theory-based modeling environment. In: Systems and Control in Aerospace and Astronautics, (ISSCAA), pp 95–100

    Google Scholar 

  11. Kwon Y, Park H, Jung S, Kim T (1996) Fuzzy-Devs formalism: concepts, realization and application. In Proceedings of AIS, pp 227–234

    Google Scholar 

  12. I. E. C. technical committee, Industrial process measurement and control, IEC 61131—programmable controllers, Tech. Rep., 2000, part 7: Fuzzy control programming. IEC

    Google Scholar 

  13. Kelber J, Triebel S, Pahnke K, Scarbata G (1994) Automatic generation of analogous fuzzy controller hardware using a module generator concept. In: Proceedings of 2nd European congress on intelligent techniques and soft computing, 8 pp

    Google Scholar 

  14. Nhivekar G, Nirmale S, Mudholkar R (2013) A survey of fuzzy logic tools for fuzzy-based system design, vol ICRTITCS, no 9, February 2013, pp 25–28, published by Foundation of Computer Science, New York, USA

    Google Scholar 

  15. Umano M, Mizumoto M, Tanaka K (1978) FSTDS system: a fuzzy-set manipulation system. Inf Sci 14(2):115–159

    Article  MATH  Google Scholar 

  16. Fellinger WL (1978) Specification for a fuzzy system modelling language. PhD dissertation, Oregon State University, Corvallis

    Google Scholar 

  17. Alsmadi MKS, Omar KB, Noah SA (2009) Back propagation algorithm: the best algorithm among the multi-layer perceptron algorithm. Int J Comput Sci Netw Secur (IJCSNS) 9(4):378–383

    Google Scholar 

  18. Maglogiannis IG (2007) Emerging artificial intelligence applications in computer engineering: real word AI systems with applications in Ehealth, Hci, information retrieval and pervasive technologies. Frontiers in artificial intelligence and applications, vol 160. Ios PressInc

    Google Scholar 

  19. Wilamowski BM (2011) How to not get frustrated with neural networks. In: Proceedings on IEEE international industrial technology (ICIT), pp 5–11

    Google Scholar 

  20. Beigy H, Meybodi MR (2000) Adaptation of parameters of BP algorithm using learning automata. In: Sixth Brazilian Symposium on proceedings on neural networks, pp 24–31

    Google Scholar 

  21. Sathya R, Abraham A (2013) Comparison of supervised and unsupervised learning algorithms for pattern classification. Int J Adv Res Artif Intell (IJARAI) 2(2)

    Google Scholar 

  22. Jayalakshmi T, Santhakumaran A (2011) Statistical normalization and back propagation for classification. Int J Comput Theor Eng 3(1):1793–8201

    Google Scholar 

  23. Ulrich EG, Agrawal VD, Arabian JH (1994) Concurrent and comparative discrete event simulation. Kluwer

    Google Scholar 

  24. Zeigler BP, Muzy A, Kofman E (2019) Theory of modeling and simulation, 3rd edn. Academic Press

    Google Scholar 

  25. Popovici K, Mosterman PJ (2013) Real-time simulation technologies: principles, methodologies, and applications. CRC Press

    Google Scholar 

  26. Capocchi L, Bernardi F, Federici D, Bisgambiglia P-A (2006) Bfs-devs: a general DEVs-based formalism for behavioral fault simulation. Simul Model Pract Theory 14(7):945–970

    Article  Google Scholar 

  27. Marenus M (2020) Gardne’s theory of multiple intelligences, SimplyPsychology, June 9, 2020. https://www.simplypsychology.org/multiple-intelligences.html

  28. Ören TI (1995-Invited contribution) Artificial intelligence and simulation: a typology. In: Raczynski S (ed) Proceedings of the 3rd conference on computer simulation. Mexico City, November 15–17, pp 1–5

    Google Scholar 

  29. Charniak E, McDermot D (1985) Introduction to artificial intelligence. Addison-Wesley, reading, Mqassachusetts, p 6

    Google Scholar 

  30. Symonds AJ (1986) Introduction to IBM’s knowledge-systems products. IBM Syst J 25(2):134–146

    Article  Google Scholar 

  31. Ören–AISim. Publications, presentations and other activities of Dr. Tuncer Ören on: synergies of artificial intelligence, cybernetics, and simulation. https://www.site.uottawa.ca/~oren/pubsList/AISim.pdf

  32. Ören-agents. Publications, Presentations and other activities of Dr. Tuncer Ören on: software agents and agent-directed simulation. https://www.site.uottawa.ca/~oren/pubsList/agents.pdf

  33. Ören T (1985) Intelligence in simulation—editorial. Simuletter—a quarterly publication of SIGSIM, The Special Interest Group on Simulation of the ACM. vol 16, number 1, January, p 3

    Google Scholar 

  34. Newell A, Simon HA (1961) The simulation of human thought. In: Current trends in psychological theory. University of Pittsburgh Press

    Google Scholar 

  35. Williams RD (ed) (1992).Two approaches to machine intelligence. IEEE Comp 25:78–81

    Google Scholar 

  36. Gonzales AJ, Dankel DD (1993) The engineering of knowledge-based systems: theory and practice. Prentice-Hall, Englewood Cliffs, NJ

    Google Scholar 

  37. Ören T (1994) Artificial intelligence in simulation. Ann Oper Res 53:287–319. https://link.springer.com/article/10.1007/BF02136832

  38. Ochoa A, Hernández A, Cruz L, Ponce J, Montes F, Li L, Janacek L (2010) Artificial societies and social simulation using ant colony, particle swarm optimization and cultural algorithms. In: New achievements in evolutionary computation. IntechOpen

    Google Scholar 

  39. Bertelle C, Duchamp GH, Kadri-Dahmani H (eds) (2008) Complex systems and self-organization modelling. Springer Science & Business Media

    Google Scholar 

  40. Dorigo M, Birattari M, Stutzle T (2006) Ant colony optimization. IEEE Comput Intell Mag 1(4):28–39

    Article  Google Scholar 

  41. Calabrò G, Inturri G, Le Pira M, Pluchino A, Ignaccolo M (2020) Bridging the gap between weak-demand areas and public transport using an ant-colony simulation-based optimization. Transp Res Procedia 45:234–241

    Article  Google Scholar 

  42. Said O (2017) Analysis, design and simulation of Internet of Things routing algorithm based on ant colony optimization. Int J Commun Syst 30(8):e3174

    Article  Google Scholar 

  43. Ahmed TH (2005, April) Simulation of mobility and routing in ad hoc networks using ant colony algorithms. In: International conference on information technology: coding and computing (ITCC’05), vol 2. IEEE, pp 698–703

    Google Scholar 

  44. Payton D, Daily M, Estowski R, Howard M, Lee C (2001) Pheromone robotics. Auton Robot 11(3):319–324

    Article  MATH  Google Scholar 

  45. Parunak HV, Purcell M, O’Connell R (2002) Digital pheromones for autonomous coordination of swarming UAV’s. In: 1st UAV conference, p 3446

    Google Scholar 

  46. Van Dyke Parunak, H., Brueckner, S., & Sauter, J. (2002, July). Digital pheromone mechanisms for coordination of unmanned vehicles. In: Proceedings of the first international joint conference on autonomous agents and multiagent systems: part 1, pp 449–450

    Google Scholar 

  47. Bradshow J (ed) (1997) Software agents. AAAI Press

    Google Scholar 

  48. Weiss G (ed) (1999) Multi-agent systems: a modern approach to distributed artificial intelligence. MIT Press, Cambridge, MA

    Google Scholar 

  49. Ören T (2000) Agent-directed simulation—challenges to meet defense requirements. In: Ören T, Numrich SK, Uhrmacher AM, Wilson LF, Gelenbe E (2000—Invited Paper). Joines JA et al (eds) Agent-directed simulation: challenges to meet defense and civilian requirements. Proceedings of the 2000 winter simulation conference, December 10–13, 2000, Orlando, Florida, pp 1757–1762. https://informs-sim.org/wsc00papers/241.PDF

  50. Yilmaz L, Ören T (2009) Agent-directed simulation, Chap. 4, pp 111–143 of (2009-All chapters by invited contributors). In: Yilmaz L, Ören TI (eds) Agent-directed simulation and systems engineering. Wiley Series in Systems Engineering and Management, Wiley-Berlin, Germany, 520 p

    Google Scholar 

  51. ABM researchers. http://www.agent-based-models.com/blog/researchers/

  52. ABM resources. http://www.agent-based-models.com/blog/resources/

  53. Ören TI (2001) Advances in computer and information sciences: from abacus to holonic agents. Special issue on artificial intelligence of Elektrik (Turkish J Electr Eng Comput Sci—Published by TUBITAK—Turkish Science and Technical Council) 9(1):63–70. https://dergipark.org.tr/en/pub/tbtkelektrik/issue/12103/144616

  54. Ören T, Yilmaz L (2017) The age of the connected world of intelligent computational entities: reliability issues including ethics, autonomy and cooperation of agents. (Invited ebook chapter). In: Nassiri Mofakham F (ed) Frontiers in artificial intelligence—intelligent computational systems. Bentham Science Publishers, pp 184–213

    Google Scholar 

  55. TBD-dic. Turkish informatics society-English-Turkish dictionary. http://bilisimde.ozenliturkce.org.tr/onerilen-tum-terimler-ingilizce-turkce/

  56. Jávor A (1990) Demons in simulation: a novel approach, systems analysis, modeling. SIMULATION 7(1990):331–338

    Google Scholar 

  57. Jávor A (1992) Demon controlled simulation, mathematics and computers in simulation vol 34, pp 283–296

    Google Scholar 

  58. Jávor A, Szűcs G (1998) Intelligent demons with hill climbing strategy for optimizing simulation models. In: Summer computer simulation conference, Reno, Neveda, July 19–22, 1998, pp 99–104

    Google Scholar 

  59. Hogeweg P, Hesper B (1979) Heterarchical selfstructuring simulation systems: concepts and applications in biology. In: Zeigler BP, Elzas MS, Klir GJ, Ören TI (eds) Methodology in systems modelling and simulation. North Holland, pp 221–2312

    Google Scholar 

  60. Ören TI, Yilmaz L, Ghasem-Aghaee N (2014) A systematic view of agent supported simulation: past, present, and promising future. Proceedings of the 4th international conference on simulation and modeling methodologies, technologies and applications (SIMULTECH’14), Vienna, Austria, 28–30 August, pp 497–506. (paper nr: 150). Printed in Portugal. ISBN 978-989-758-038-3

    Google Scholar 

  61. Russell SJ, Norvig P (2016) Artificial intelligence: a modern approach. Pearson Education Limited, Malaysia

    MATH  Google Scholar 

  62. Zeigler BP (1972) Toward a formal theory of modeling and simulation: structure preserving morphisms. J ACM 19(4):742–764

    Google Scholar 

  63. Niazi M, Hussain A (2011) Agent-based computing from multi-agent systems to agent-based models: a visual survey. Scientometrics 89(2):479

    Article  Google Scholar 

  64. Camus B, Bourjot C, Chevrier V (2015) Combining DEVS with multi-agent concepts to design and simulate multi-models of complex systems (WIP). In: Proceedings of the symposium on theory of modeling & simulation: DEVS Integrative M&S symposium. Society for computer simulation international, pp 85–90

    Google Scholar 

  65. Ören TI, Yilmaz L (2012) Agent-monitored anticipatory multisimulation: a systems engineering approach for threat-management training. In: Breitenecker F, Bruzzone A, Jimenez E, Longo F, Merkuryev Y, Sokolov B (eds) Proceedings of EMSS’12—24th European modeling and simulation symposium, September 19–21, 2012, Vienna, Austria, pp 277–282. ISBN 978-88-97999-01-0 (Paperback). ISBN 978-88-97999-09-6 (PDF)

    Google Scholar 

  66. Ören TI (2014–Invited review paper) Coupling concepts for simulation: a systematic and comprehensive view and advantages with declarative models. Int J Model Simul Sci Computi (IJMSSC) 5(2):1430001–14300017 (article ID: 1430001). https://doi.org/10.1142/S17939623143000015

  67. Ören T, Mittal S, Durak U (2018) Induced emergence in social system engineering: multimodels and dynamic couplings as methodological bases, Chap. 9. In: Mittal S, Diallo S, Tolk A (eds) (2018) Emergent Behavior in complex systems engineering: a modeling and simulation approach, Wiley. Hoboken, NJ

    Google Scholar 

  68. Ören TI, Yilmaz L (2015, Invited article) Awareness-based couplings of intelligent agents and other advanced coupling concepts for M&S. In: Proceedings of the 5th International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH’15), Colmar, France, July 21–23, 2015, pp 3–12

    Google Scholar 

  69. Ören TI (1983) Quality assurance of system design and model management for complex problems. In: Wedde H (ed) Adequate modelling of systems. Springer, Heidelberg, pp 205–219. https://doi.org/10.1007/978-3-642-69208-6_31

  70. Ören TI (2001) Software agents for experimental design in advanced simulation environment. In: Ermakov SM, Kashtanov YN, Melas V (eds) Proceedings of the 4th St. Petersburg workshop on simulation, June 18–23, 2001, pp 89–95

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jean François Santucci .

Editor information

Editors and Affiliations

Appendices

Appendix 1: Types of agents (adapted from Ören [53] and TBD-dic [55])

 

Adaptive agent

Competent agent

Dispatched agent

Advertising cookie

Competitive agent

Dispatched mobile agent

Agent

Complete agent

Distant agent

Agent-based holon

Computational agent

Distinguished agent

Animated agent

Computer interface agent

Domain-specific agent

Antagonistic agent

Computer-controlled bot

Emotional agent

Anticipatory agent

Contractee agent

Endomorphic agent

Application agent

Contractor agent

Essential cookie

Artificial moral agent

Conventional agent

Ethical agent

Authorized agent

Conventional software agent

Fixed agent

Autistic agent

Conversational agent

Functional cookie

Autodidactic agent

Cookie

Global agent

Autonomous agent

Cooperating agent

Goal-directed agent

Autoprogrammable agent

Cooperation agent

Goal-oriented agent

Believable agent

Coordination agent

Holonic agent

Bot

Coordinator agent

Independent agent

Broker

Coupled multiagents

Individual agent

Broker agent

Deceptive agent

Information agent

Client agent

Deleted cookie

Information disseminating agent

Cognitive agent

Deliberative agent

Information filtering agent

Co-located agent

Diagnosis agent

Information gathering agent

Co-located agent

Digital agent

Information spider

Communication agent

Disabled cookie

 

Intelligent agent

Persistent cookie

Stationary agent

Inter-agent

Personal agent

Subagent

Interface agent

Personal digital agent

Subordinate agent

Intermediate agent

Personal software agent

System latency agent

Internet agent

Proactive agent

Task-specific agent

Itinerant agent

Purposeful agent

Teachable agent

Knowledge-based agent

Rational agent

Temporary cookie

Learning agent

Reactive agent

Tightly coupled multiagent

Local agent

Reliable agent

Tracking cookie

Long-lived agent

Remote agent

Transient agent

Loosely coupled multiagent

Resident agent

Transportable Information agent

Mail agent

Retrieval agent

Trusted agent

Marketing cookie

Root agent

Trustworthy agent

Message transfer agent

Rule-based agent

Unauthorized agent

Messaging agent

Scriptable agent

Understanding agent

Mobile agent

Search agent

Uniform resource agent

Model-based agent

Self-motivated agent

User agent

Multiple mobile agent

Self-replicating agent

User interface agent

Network agent

Semantic agent

User-programmed agent

Neural net agent

Semi-autonomous agent

Virtual agent

Notification agent

Service agent

Vivid agent

Pedagogical agent

Session cookie

Wanderer

Permanent cookie

Sociable agent

Web search agent

Permanent cookie

Software agent

Web site agent

Persistent cookie

Spider

Web-oriented agent

Appendix 2: Agent-related concepts (adapted from Ören [53] and TBD-dic [55])

 

Agency

Agent development platform

Agent software

Agent architecture

Agent efficiency

Agent system

Agent autonomy

Agent framework

Agent understanding

Agent behavior

Agent implementation

Agent user

Agent class

Agent interactivity

Agent-assisted workflow support

Agent code

Agent language

Agent-based

Agent communication language

Agent model

Agent-based adaptive mechanism

Agent communication protocol

Agent security

Agent-based adaptive system

 

Agent service

 

Agent-based assistant

Agentive

Multiagent learning system

Agent-based cloud computing

Agent-monitored

Multiagent learning technique

Agent-based cognitive architecture

Agent-oriented

Multiagent software

Agent-based complex system

Agent-oriented methodology

Multiagent system

Agent-based complex system development

Agent-oriented modeling

Multiagent understanding

Agent-based design

Agent-oriented problem solving

Multiagent understanding system

Agent-based fault-tolerant system

Agent-oriented programming

Ontology-based agent service

Agent-based interaction protocol

Agent-oriented requirements engineering

Privacy in agent-based systems

Agent-based interface

Agent-oriented tool

Safety in agent-based systems

Agent-based knowledge discovery

Agentry

Security in agent-based systems

Agent-based marketplace

Agent-supported

Self-adaptation in multiagent systems

Agent-based model

Animated agent technology

Self-adaptation via multiagent systems

Agent-based modeling

Autonomous agent-based technique

Semantic agent system

Agent-based modeling-as-a-service

Cookie policy

Service-oriented agent-based architecture

Agent-based social simulation

Cookie preference

Service-oriented agent-based protocol

Agent-based software

Ethics for agents

Subagency

Agent-based software engineering

Holonic agent simulation

Task execution in multiagent systems

Agent-based software provider

Intelligent agent modeling

Task planning in multiagent systems

Agent-based system

Intelligent agent system

Task-oriented agent-based system

Agent-based system application

Intelligent agent technology

 

Agent-based technique

Inter-agent communication

 

Agent-based trust model for cooperation

Inter-agent communication language

 

Agent-based ubiquitous service

Inter-agent knowledge processing

 

Agent-based ubiquitous system

Learning via multiagent system

 

Agent-based virtual enterprise

Lifetime of cookies

 

Agent-directed

Mobile agent paradigm

 

Agented

Multiagent architecture

 

Agent-enabled

Multiagent design-system

 

Agent-enabled feature

Multiagent intelligent system

 

Agential

  

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Santucci, J.F., Capocchi, L., Ören, T., Szabo, C., Graciano Neto, V.V. (2023). Synergies of Soft Computing and M&S. In: Ören, T., Zeigler, B.P., Tolk, A. (eds) Body of Knowledge for Modeling and Simulation. Simulation Foundations, Methods and Applications. Springer, Cham. https://doi.org/10.1007/978-3-031-11085-6_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-11085-6_12

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-11084-9

  • Online ISBN: 978-3-031-11085-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics