Advertisement

Atlas: A Knowledge-Based Collaborative Framework for Handling Logistics Procedures

  • T. Tamisier
  • Y. Didry
  • F. Feltz
Part of the Communications in Computer and Information Science book series (CCIS, volume 194)

Abstract

Owing to their ability to easily organize and update heterogeneous knowledge, Decision-Support Systems form a promising approach for the optimization of logistics businesses. The management and visualization of the knowledge base of these systems are in this regard crucial to ensure a proper functioning and to keep an intuitive view of their expected behavior. This paper introduces Atlas, a customizable automated tool for assisting / improving the supply chain with respect to miscellaneous aspects such as secure collaboration, traceability, or multimodality. The operational knowledge of Atlas is accessed through 2 different views. In an analytical view, the knowledge is modeled on elementary if-then rules, which are processed by a resolution engine written in the Soar architecture. A synthetic view offers a pictorial representation of all the knowledge, and in particular, shows the inter-dependence of the rules and their procedural references. In addition to allowing an efficient processing, the system checks the coherence of the knowledge and produces a justification of the decision with respect to relevant operational procedures.

Keywords

Supply-chain optimization decision-support knowledge management information retrieval 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Gordon, T.: From Jhering to Alexy - Using Artificial Intelligence Models in Jurisprudence. In: Jurix Conference (1994)Google Scholar
  2. 2.
    Fehling, M.R.: Unified Theories of Cognition: Modeling Cognitive Competences. Artificial Intelligence 59(1-2) (1993)Google Scholar
  3. 3.
    The Softlaw Expert-System, http://expert.Softlaw.com.au/fdss
  4. 4.
    O’Callaghan, T.A., et al.: Building and Testing the Shyster-Mycin Hybrid Legal Expert System, http://cs.anu.edu.au/software/shyster
  5. 5.
    Eijlander, P.: The Possibilities and Limitations of Using Intelligent Tools for Drafting Legislation. In: Jurix Conference (1994)Google Scholar
  6. 6.
    Forgy, C.L.: Rete: a Fast Algorithm for the Many Pattern Many Object Pattern Match Problem. Artificial Intelligence 19 (1982)Google Scholar
  7. 7.
    Laird, J., et al.: SOAR: an Architecture for General Intelligence. Artificial Intelligence 33(1) (1987)Google Scholar
  8. 8.
  9. 9.
  10. 10.
    Di Battista, G., Eades, P., Tamassia, R., Tollis, I.: Graph Drawing: Algorithms for the Visualisation of Graphs. Prentice Hall, Englewood Cliffs (1999)zbMATHGoogle Scholar
  11. 11.
    Sugiyama, K., Tagawa, S., Toda, M.: Methods for Visual Understandings of Hierarchical System Structures. IEEE Trans. in Systems Man & Cybernetics 11(2) (1981)Google Scholar
  12. 12.
    Radwan, A., Girgis, M., Ghanem, A.: A Study of Barycentre Algorithm for Hierarchical Graph Crossing Minimization. International Journal on Intelligent Cooperative Information Systems 2(2) (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • T. Tamisier
    • 1
  • Y. Didry
    • 1
  • F. Feltz
    • 1
  1. 1.Department Informatics, Systems, Collaboration (ISC)Centre de Rercherche Public - Gabriel LippmannBelvauxGrand Duchy of Luxembourg

Personalised recommendations