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Part of the book series: Simulation Foundations, Methods and Applications ((SFMA))

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Abstract

The purpose of system dynamics modelling is to develop understanding and then the improvement of systems. The first stage in this process is the construction of a logical model (influence diagram) to describe a system.

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References

  1. Pearl J (1998) Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann Publishers Inc., San Francisco

    MATH  Google Scholar 

  2. Trovati M, Bessis N (2015) An influence assessment method based on co-occurrence for topologically reduced sets. Soft Comput 20(5):2021–2030

    Google Scholar 

  3. Sanchez-Graillet O, Poesio M (2004) Acquiring from text. LREC

    Google Scholar 

  4. Kuipers BJ (1984) Causal reasoning in medicine: analysis of a protocol. Cogn Sci 8:363–385

    Article  Google Scholar 

  5. Trovati M (2016) An overview of some theoretical topological aspects of big data, Big-Data analytics and cloud computing, theory, algorithms and applications, computer communications and networks, Springer

    Google Scholar 

  6. Liddy ED (2001) A robust risk minimization based named entity recognition system. In: Encyclopedia of library and information science. Marcel Decker, Inc., New York

    Google Scholar 

  7. Laporte E (2005) Symbolic natural language processing. In: Lothaire (ed) Applied combinatorics on words, pp 164–209

    Google Scholar 

  8. Manning CD, Schutze H (1999) Foundations of statistical natural language processing. MIT Press, Cambridge

    Google Scholar 

  9. Troussov A, Levner E, Bogdan C, Judge J, Botvich D (2010) Spreading activation methods. In: Dynamic and advanced data mining for progressing technological development: innovations and systemic approaches, pp 136–167

    Google Scholar 

  10. Dale R, Moisl H, Somers HL (2000) Handbook of natural language processing. Marcel Dekker, Inc., New York

    Google Scholar 

  11. Korhonen AYK (2006) A large subcategorisation lexicon for natural language processing applications. In: Proceedings of LREC

    Google Scholar 

  12. Stumme G (1998) Efficient data mining based on formal concept analysis. Lecture Notes in Computer. Springer, New York

    Google Scholar 

  13. Wilks Y, Stevenson M (1998) The grammar of sense: using part-of-speech tags as a first step in semantic disambiguation. Nat Lang Eng 4:135–143

    Article  Google Scholar 

  14. Erman LD, Hayes-Roth F, Lesser VR, Reddy DR (1980) The Hearsay-II speech-understanding system: integrating knowledge to resolve uncertainty. ACM Comput Surv 12(2):213–253

    Article  Google Scholar 

  15. Nii HP, Feigenbaum EA, Anton JJ, Rockmore AJ (1982) Signal-to-symbol transformation: HASP/SIAP case study. AI Mag 3:23–35

    Google Scholar 

  16. Fox CW, Evans MH, Pearson MJ, Prescott TJ (2012) Towards hierarchical blackboard mapping on a whiskered robot. Robot Auton Syst 60(11):1356–1366

    Article  Google Scholar 

  17. Sutton C, Morrison C, Cohen PR, Moody J, Adibi J (2004) A Bayesian blackboard for information fusion. In: Svensson P, Schubert J (eds) Proceedings of the seventh international conference on information fusion, pp 1111–1116

    Google Scholar 

  18. Millington I, Funge J (2009) Artificial intelligence for games, 2nd edn. CRC Press, Boca Raton, pp 459–466

    Google Scholar 

  19. Corkill DD (1991) Blackboard systems. AI Expert 6(9):40–47

    Google Scholar 

  20. Carver N, Lesser V (1994) Evolution of blackboard control architectures. Expert Syst Appl 7:1–30

    Article  Google Scholar 

  21. Pang GK-H (2009) Blackboard architecture for intelligent control. In: Unbehauen H (ed) Control systems, robotics and automation: and intelligent control systems, vol 17. EOLSS, Oxford, pp 303–316

    Google Scholar 

  22. Hayes-Roth B, Johnson V, Garvey A, Hewett M (1986) Application of the BB1 blackboard control architecture to arrangement-assembly tasks. Artif Intell 1(2):85–94

    Google Scholar 

  23. Newell A, Simon HA (1972) Human problem solving. Prentice-Hall, Englewood Cliffs

    Google Scholar 

  24. Russell S, Norvig P (1995) Artificial intelligence: a modern approach. Prentice-Hall, New Jersey

    MATH  Google Scholar 

  25. Russell S, Norvig P (2016) Artificial intelligence: a modern approach, 3rd edn. Pearson, Harlow

    MATH  Google Scholar 

  26. Carver N (1997) A revisionist view of blackboard systems, In: Proceedings of the 1997 midwest artificial intelligence and cognitive science society conference. The AAAI Press, Dayton, Ohio

    Google Scholar 

  27. Gelenter D (1983) Generative communication in Linda. ACM Trans Program Lang Syst 7(1):80–112

    Article  Google Scholar 

  28. Engelmore RS, Morgan AJ, Nii HP (1988a) Introduction. In: Engelmore R, Morgan T (eds) Blackboard systems. Addison-Wesley, Boston, pp 1–22

    Google Scholar 

  29. Craig ID (1995) Blackboard systems. Ablex Publishing Corporation, Norwood, NJ

    Google Scholar 

  30. Jones J, Millington M, Ross P (1986) A blackboard shell in PROLOG. In: Proceedings ECAI-86, pp 428–436

    Google Scholar 

  31. Dodhiawala RT, Sridharam N, Pickering C (1989) A real-time blackboard architecture. In: Jagannathan V, Dodhiawala R, Baum L (eds) Blackboard architectures and applications. Academic Press Inc., San Diego

    Google Scholar 

  32. Balzar R, Erman L, London P, Williams C (1980) HEARSAY-III: a domain-independent framework for expert systems. In: Proceedings of the first annual conference on artificial intelligence, pp 108–110

    Google Scholar 

  33. Hayes-Roth F, Waterman DA, Lenat DB (1983) Building expert systems. Addison-Wesley, Reading

    Google Scholar 

  34. Corkill DD, Gallagher KQ, Murray KE (1986) GBB: a generic blackboard development system. In: Proceedings of AAAI-86, pp 1008–1014

    Google Scholar 

  35. Orkin J (2003) Applying blackboard systems to first person shooters. [online] slidepayer.com. Available at: web.media.mit.edu/~jorkin/utgameAI03-Orkin.ppt and http://slideplayer.com/slide/6102412/. Accessed 16 Apr 2016

  36. Newell A (1962) Some problems of the basic organization in problem-solving programs. In: Yovits M, Jacobi G, Goldstein G (eds) Proceedings of the second conference of self-organising systems, pp 393–423

    Google Scholar 

  37. Hayes-Roth B, Hayes-Roth F, Rosenschein S, Cammarata S (1979) Modelling planning as an incremental, oppotunistic process. In: Proceedings IJCAI-79, pp 375–383

    Google Scholar 

  38. Zanconato (1988) BLOBS—an object-oriented blackboard system framework for reasoning in time. In: Engelmore R, Morgan T (eds) Blackboard systems. Addison-Wesley. Reading, pp 335–345

    Google Scholar 

  39. Reynolds D (1988) MUSE: A toolkit for embedded, real-time AI. In: Engelmore R, Morgan T (eds) Blackboard systems. Addison-Wesley, Reading, pp 519–532

    Google Scholar 

  40. Lesser VR, Corkill DD (1983) The distributed vehicle monitoring testbed: a tool for investigating distributed problem solving networks. AI Mag 4(3):15–33

    Google Scholar 

  41. Nii HP (1986b) CAGE and POLIGON: two frameworks for blackboard-based concurrent problem solving. Technical Report KSL-86-41. Stanford University, Stanford

    Google Scholar 

  42. Ensor JR, Gabbe JD (1986) Transactional blackboards. Int J Artif Intell Eng 1(2):80–84

    Article  Google Scholar 

  43. Selfridge O (1959) Pandemonium: a paradigm for learning. In: Proceedings of symposium on the mechanisation of thought processes, pp 511–529

    Google Scholar 

  44. Nii HP (1986) Blackboard systems: the blackboard model of problem solving and the evolution of blackboard architectures. AI Mag 7(2):38

    Google Scholar 

  45. Simon HA (1977) Scientific discovery and the psychology of problem solving. In: Models of discovery, Reidel, Boston

    Google Scholar 

  46. Reddy DR, Erman LD, Neely RB (1973) A model and a system for machine recognition of speech. IEEE Trans Audio Electro Acoust AU-21:229–238

    Google Scholar 

  47. Engelmore RS, Morgan AJ, Nii HP (1988) Hearsay-II. In: Engelmore R, Morgan T (eds) Blackboard systems. Addison-Wesley, Reading, pp 25–29

    Google Scholar 

  48. Lowerre BT, Reddy R (1980) The HARPY speech understanding system. In: Lea W (ed) Trends in speech recognition. Prentice-Hall, Englewood Cliffs

    Google Scholar 

  49. Terry A (1988) Using explicit strategic knowledge to control expert systems. In: Engelmore R, Morgan T (eds) Blackboard systems. Addison-Wesley, Reading, pp 159–188

    Google Scholar 

  50. Englemore RS, Terry A (1979) Structure and function of the CRYSALIS system. In: Proceedings of IJCAI-79, pp 250–256

    Google Scholar 

  51. Carver N (1990) Sophisticated control for interpretation: planning to resolve sources of uncertainty. Ph.D. Thesis. University of Massachusetts, Computer and Information Science Department, Amherst

    Google Scholar 

  52. Carver N, Lesser V (1990) Control for interpretation: planning to resolve sources of uncertainty. Technical Report No. 90-53. University of Massachusetts, Computer and Information Science Department, Amherst, MA

    Google Scholar 

  53. Feigenbaum EA, Nii HP (1978) Rule-based understanding of signals. In: Waterman D, Hayes-Roth F (eds) Pattern-directed inference systems. Academic Press, New York

    Google Scholar 

  54. Craig ID (1991) Formal specification of advanced AI architectures. Ellis Horwood, Chichester

    Google Scholar 

  55. Velthuijsen H (1992) The nature and applicability of the blackboard architecture. Ph. D. Thesis. Faculty of General Science, Limburg University, Maastricht

    Google Scholar 

  56. Craig ID (1993) Formal techniques in the development of blackboard systems. Int J Pattern Recogn Artif Intell 7(2):197–219

    Article  Google Scholar 

  57. Whitehair R (1996) A framework for the analysis of sophisticated control. Ph. D. Thesis. University of Massachusetts. Computer Science Department

    Google Scholar 

  58. Culliton P (2003) Implementing a blackboard-like system for squad-level combat AI Part I. [online] GameDev.net. Available at: http://www.gamedev.net/page/resources/_/technical/artificial-intelligence/implementing-a-blackboard-like-system-for-squad-r1931. Accessed 15 Sept 2016

  59. Dill K (2014) Structural architecture—common tricks of the trade. In: Rabin S (ed) Game AI PRO: collected wisdom of game AI professionals. CRC Press, Boca Raton

    Google Scholar 

  60. Champandard AJ (2007) Using a static blackboard to store world knowledge. [online] aigamedev.com. Available at: http://aigamedev.com/open/article/static-blackboard/. Accessed 16 Apr 2016

  61. Mark D (2010) Damián Isla Interview on Blackboard Arch. [online] intrinsicalgorithm.com. Available at: http://intrinsicalgorithm.com/IAonAI/2010/02/damian-isla-interview-on-blackboard-arch/. Accessed 19 Sept 2016

  62. Khosravi H, Kabir E (2009) A blackboard approach towards an integrated Farsi OCR system. IJDAR 12(1):21–32

    Article  Google Scholar 

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Correspondence to Stuart Berry .

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Lowndes (Retired), V., Berry, S., Trovati, M., Whitbrook, A. (2017). Model Building. In: Berry, S., Lowndes, V., Trovati, M. (eds) Guide to Computational Modelling for Decision Processes. Simulation Foundations, Methods and Applications. Springer, Cham. https://doi.org/10.1007/978-3-319-55417-4_1

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  • DOI: https://doi.org/10.1007/978-3-319-55417-4_1

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