Skip to main content

Part of the book series: Studies in Computational Intelligence ((SCI,volume 64))

  • 743 Accesses

Summary. Many connections have been established in recent years between Chemistry and Computer Science, and very accurate systems, based on mathematical and physical models, have been suggested for the analysis of chemical substances. However, such a systems suffer from the difficulties of processing large amount of data, and their computational cost grows largely with the chemical and physical complexity of the investigated chemical substances. This prevent such kind of systems from their practical use in many applicative domain, where complex chemical compound are involved. In this paper we proposed a formal model, based on qualitative chemical knowledge, whose aim is to overcome such computational difficulties. The model is aimed at integrating ontological and causal knowledge about chemical compounds and compound transformations. The model allowed the design and the implementation of a system, that is based on the well known Heuristic Search paradigm, devoted to the automatically resolution of chemical formulation problems in the industrial domain of rubber compounds.

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 169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Grant, G., Richards, W.: Computational Chemistry. Volume 29 of Oxford Chem-istry Primers. Oxford University Press (1995)

    Google Scholar 

  2. Cohen-Tannoudji, C., Diu, B., Laloe, F.: Quantum Mechanics Volume I & II. John Wiley & Sons (1977)

    Google Scholar 

  3. MacQuarrie, D.: Quantum Chemistry. Prentice Hall (1983)

    Google Scholar 

  4. Hammond, B., Lester, W., Reynolds, P.: Monte Carlo Methods in Ab Initio Quantum Chemistry. World Scientific (1994)

    Google Scholar 

  5. Parr, R., Yang, W.: Density Functional Theory of Atoms and Molecules. Oxford University Press (1989)

    Google Scholar 

  6. Hoffmann, W.: Rubber Technology Handbook. Oxford University Press, New York (1989)

    Google Scholar 

  7. Burkert, U., Allinger, N.: Molecular Mechanics. American Chemical Society (1982)

    Google Scholar 

  8. Schlick, T.: Molecular Modeling and Simulation. Springer Verlag (2002)

    Google Scholar 

  9. Young, D.: Computational Chemistry : A Practical Guide for Applying Tech-niques to Real World Problems. Wiley-Interscience (2001)

    Google Scholar 

  10. Mittal, S., Frayman, F.: Towards a generic model of configuration tasks. In: Proc. of the 11th IJCAI, Detroit, MI (1989) 1395-1401

    Google Scholar 

  11. Fikes, R., Nilsson, N.J.: Strips: A new approach to the application of theorem proving to problem solving. Artificial Intelligence 2 (1971) 189-208

    Article  MATH  Google Scholar 

  12. Newell, A., Simon, H.A.: Gps, a program that simulates human thought. In Feigenbaum, E.A., Feldman, J., eds.: Computers and Thought. McGraw-Hill (1963)279-293

    Google Scholar 

  13. Green, C.C.: Theorem proving by resolution as a basis for question-answering systems. In Meltzer, Michie, eds.: Machine Intelligence 4. Edinburgh University Press, Edinburgh (1969)

    Google Scholar 

  14. Green, C.C.: Application of theorem proving to problem solving. In: IJCAI1. (1969)219-239

    Google Scholar 

  15. McCarthy, J., Hayes, P.J.: Some philosophical problems from the standpoint of artificial intelligence. In Meltzer, B., Michie, D., eds.: Machine Intelligence 4. Edinburgh University Press (1969) 463-502

    Google Scholar 

  16. Fox, M., Long, D.: The automatic inference of state invariants in tim. Journal of AI Research 9 (1998) 367-421

    MATH  Google Scholar 

  17. Gerevini, A., Schubert, L.: Inferring state constraints for domain-independent planning. In: AAAI ’98/IAAI ’98: Proceedings of the Fifteenth National/Tenth Conference on Artificial Intelligence/Innovative Applications of Artificial intel-ligence, Menlo Park, CA, USA, American Association for Artificial Intelligence (1998)905-912

    Google Scholar 

  18. Borrett, J.E., Tsang, E.P.K.: A context for constraint satisfaction problem formulation selection. Constraints 6 (2001) 299-327

    Article  MATH  MathSciNet  Google Scholar 

  19. Westfold, S., Smith, D.: Synthesis of efficient constraint satisfaction programs (1998)

    Google Scholar 

  20. Kautz, H., Selman, B.: Planning as satisfiability. In: ECAI ’92: Proceedings of the 10th European conference on Artificial intelligence, New York, NY, USA, John Wiley & Sons, Inc. (1992) 359-363

    Google Scholar 

  21. Lifschitz, V.: Answer set programming and plan generation. Artificial Intelli-gence 138 (2002) 39-54

    Article  MATH  MathSciNet  Google Scholar 

  22. Lifschitz, V.: Answer set planning. In: ICLP. (1999) 23-37

    Google Scholar 

  23. Lifschitz, V., Turner, H.: Representing transition systems by logic programs. In: LPNMR. (1999) 92-106

    Google Scholar 

  24. Subrahmanian, V.S., Zaniolo, C.: Relating stable models and ai planning domains. In: ICLP. (1995) 233-247

    Google Scholar 

  25. Giunchiglia, F., Traverso, P.: Planning as model checking. In: ECP ’99: Proceed-ings of the 5th European Conference on Planning, London, UK, Springer-Verlag (2000)1-20

    Google Scholar 

  26. Spalazzi, L., Traverso, P.: A dynamic logic for acting, sensing, and planning. Journal of Logic Computation 10 (2000) 787-821

    Article  MATH  MathSciNet  Google Scholar 

  27. Eiter, T., Faber, W., Leone, N., Pfeifer, G., Polleres, A.: A logic programming approach to knowledge-state planning, ii: the dlvk system. Artificial Intelligence 144 (2003) 157-211

    Article  MATH  MathSciNet  Google Scholar 

  28. Mosca, A.: A theoretical and computational inquiry into the Compounding Problem. Ph.D. thesis, Department of Computer Science, Systems, and Com-munication - University of Milano-Bicocca, Italy (2005)

    Google Scholar 

  29. Himmelblau, D.M., Riggs, J.B.: Basic Principles and Calculations in Chemical Engineering. 7 edn. Prentice Hall Professional Technical Reference (2003)

    Google Scholar 

  30. Duncan, T.M., Reimer, J.A.: Chemical Engineering Design and Analysis, An introduction. Cambridge Series in Chemical Engineering. Cambridge University Press (1998)

    Google Scholar 

  31. Fine, K.: Compounds and aggregates. Nous 28 (1992) 137-158

    Google Scholar 

  32. Husserl, E.: Logische Untersuchungen. Zweiter Band. Untersuchungen zur Phnomenologie und Theorie der Erkenntnis. Halle: Niemeyer (1900/1901) [2nd ed. 1913; Eng. trans. by J. N. Findlay: Logical Investigations, Volume Two, London: Routledge & Kegan Paul (1970)

    Google Scholar 

  33. Rescher, N.: Axioms for the part relation. Philosophical Studies 6 (1955) 8-11

    Article  Google Scholar 

  34. Montague, R.: On the nature of certain philosophical entities. The Monist 53 (1969) 159-194

    Google Scholar 

  35. Simons, P., Dement, C.: Aspects of the mereology of artifacts. In Poli, R., Simons, P., eds.: Computers and Thought. Kluwer Academic Publishers (1996) 255-276

    Google Scholar 

  36. Sattler, U.: Description logics for the representation of aggregated objects. In W. Horn, ed.: Proceedings of the 14th European Conference on Artificial Intelligence, IOS Press, Amsterdam (2000)

    Google Scholar 

  37. Gent, A.E.: Engineering with rubber, how to design rubber components. Hanser Publisher, New York (1992)

    Google Scholar 

  38. Simons, P.: Parts: A Study In Ontology. Clarendon Press, Oxford (1987)

    Google Scholar 

  39. White, J.L.: Rubber processing, Technology - Materials - Principles. Hanser Publisher, Munich Vienna New York (1995)

    Google Scholar 

  40. Roberts, A.D., ed.: Natural rubber science and technology. Oxford University Press, Ney York (1988) s. 161.

    Google Scholar 

  41. Newell, A., Simon, H.A.: Computer science as empirical inquiry: symbols and search. Commun. ACM 19 (1976) 113-126

    Article  MathSciNet  Google Scholar 

  42. Korf, R.E.: Artificial intelligence search algorithms. In: Algorithms and Theory of Computation Handbook, CRC Press, 1999. CRC Press (1999)

    Google Scholar 

  43. Korf, R.E.: Search: A survey of recent results for Artificial Intelligence. In Shrobe, H.E., A.A., eds.: Exploring Artificial Intelligence: Survey Talks from the National Conferences on Artificial Intelligence, San Mateo, CA, Kaufmann (1988)197-237

    Google Scholar 

  44. Bandini, S., Manzoni, S., Sartori, F.: Knowledge maintenance and sharing in the KM context: the case of P-Truck. In Cappelli, A., Turini, F., eds.: AI*IA 2003: Advances in Artificial Intelligence, Proceedings of 8th Congress of the Italian Association for Artificial Intelligence, Pisa (I), September 2003. Volume 2829 of Lecture Notes in Artificial Intelligence, Berlin, Heidelberg, Springer-Verlag (2003)499-510

    Google Scholar 

  45. Bandini, S., Mosca, A., Vanneschi, L.: Towards the use of genetic algorithms for the chemical formulation problem. In Manzoni, S., Palmonari, M., Sartori, F., eds.: Proceedings of the 9th Congress of the Italian Association for Artificial Intelligence (AI*IA 2005), Workshop on Evolutionary Computation (GSICE 2005), Milano, Centro Copie Bicocca (2005) ISBN 88-900910-0-2.

    Google Scholar 

  46. Holland, J.H.: Adaptation in Natural and Artificial Systems. The University of Michigan Press, Ann Arbor, Michigan (1975)

    Google Scholar 

  47. Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley (1989)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Bandini, S., Mosca, A., Palmonari, M. (2007). Model-Based Chemical Compound Formulation. In: Magnani, L., Li, P. (eds) Model-Based Reasoning in Science, Technology, and Medicine. Studies in Computational Intelligence, vol 64. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71986-1_24

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-71986-1_24

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-71985-4

  • Online ISBN: 978-3-540-71986-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics