Process algebras for systems diagnosis

Artificial Intelligence

Volumn 142, Issue 1, 2002, Pages 19-51

  Console, Luca ^a   Picardi, Claudia ^a   Ribaudo, Marina ^a  

^a UNIVERSITY OF TURIN   (Italy)

Author keywords

Characterization of diagnosis; Component centered qualitative models; Model based reasoning; Process algebras

Indexed keywords

ALGEBRA; ALGORITHMS; COMPUTER SCIENCE; PROBLEM SOLVING;

PROCESS ALGEBRAS;

ARTIFICIAL INTELLIGENCE;

EID: 0036838120     PISSN: 00043702     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0004-3702(02)00292-8     Document Type: Article

References (61)

1. D. Bobrow (Ed.), Special Issue on Qualitative Reasoning about Physical Systems Artificial Intelligence. 24:(1-3):1984.
2. J. de Kleer, B. Williams (Eds.), Special Issue on Qualitative Reasoning about Physical Systems II Artificial Intelligence. 51:(1-3):1991.
3. F. Wotawa (Ed.), Special Issue on Model-based Reasoning AI Communications. 12:(1-2):1999.
4. Ajmone Marsan M., Balbo G., Conte G., Donatelli S., Franceschinis G. Modelling with Generalized Stochastic Petri Nets. 1995;John Wiley, New York.
5. Baral C., McIlraith S.A., Cao Son T. Formulating diagnostic problem solving using an action language with narratives and sensing. Proc. 7th Internat. Conf. Principles of Knowledge Representation and Reasoning (KR-2000), Breckenridge, CO. 2000;311-322.
6. Baroni P., Lamperti G., Pogliano P., Zanella M. Diagnosis of large active systems. Artificial Intelligence. 110:(1):1999;135-183.
7. Bergstra J.A., Ponse A., Smolka S.A. Handbook of Process Algebra. 2001;Elsevier, Amsterdam.
8. Bernardo M., Cleaveland W.R., Sims S.T., Stewart W.J. TwoTowers: A Tool Integrating Functional and Performance Analysis of Concurrent Systems. 1998.
9. Bernardo M., Gorrieri R. A tutorial on EMPA: A theory of concurrent processes with nondeterminism, priorities, probabilities and time. Theoretical Computer Science. 201:July 1998;1-54.
10. Besnard P., Cordier M.-O. Explanatory diagnoses and their characterization by circumscription. Annals of Mathematics and Artificial Intelligence. 11:(1-4):1994;75-96.
11. Brusoni V., Console L., Terenziani P., Theseider Dupré D. A spectrum of definitions for temporal model-based diagnosis. Artificial Intelligence. 102:(1):1998;39-79.
12. Buchholz P. Markovian process algebra composition and equivalence. Proc. Second Workshop on Process Algebras and Performance Modelling. 1994;Erlangen, Germany.
13. Bylander T., Allemang D., Tanner M., Josephson J. The computational complexity of abduction. Artificial Intelligence. 49:(1-3):1991;25-60.
14. Cascio F., Console L., Guagliumi M., Osella M., Panati A., Sottano S., Theseider Dupré D. Generating on-board diagnostics of dynamic automotive systems based on qualitative deviations. AI Communications. 12:(1):1999;33-44. Also in Proc. 10th Internat. Workshop on Principles of Diagnosis, Loch Awe, Scotland.
15. Clarke E.M., Grumberg O., Peled D. Model Checking. 1999;MIT Press, Cambridge, MA.
16. Cleaveland R., Parrow J., Steffen B. The Concurrency Workbench: A semantics-based tool for the verification of concurrent systems. ACM Trans. Programming Languages and Syst. 15:January 1993;36-72.
17. Console L., Dressler O. Model-based diagnosis in the real world: lessons learned and challenges remaining. Proc. IJCAI-99, Stockholm, Sweden. 1999;1393-1400.
18. Console L., Portinale L., Theseider Dupré D., Torasso P. Diagnosing time-varying misbehavior: An approach based on model decomposition. Annals of Mathematics and Artificial Intelligence. 11:(1-4):1994;381-398.
19. Console L., Theseider Dupré D., Torasso P. A theory of diagnosis for incomplete causal models. Proc. IJCAI-89, Detroit, MI. 1989;1311-1317.
20. Console L., Torasso P. A spectrum of logical definitions of model-based diagnosis. Computational Intelligence. 7:(3):1991;133-141.
21. Cordier M.-O. When abductive diagnosis fails to explain too precise observations: An extended spectrum of definitions of MBD based on abstracting observations. Proc. 9th Internat. Workshop on Principles of Diagnosis. 1998;24-31.
22. Davis R. Diagnostic reasoning based on structure and behavior. Artificial Intelligence. 24:(1-3):1984;347-410.
23. de Kleer J., Mackworth A., Reiter R. Characterizing diagnoses and systems. Artificial Intelligence. 56:(2-3):1992;197-222.
24. de Kleer J., Williams B.C. Diagnosis with behavioral modes. Proc. IJCAI-89, Detroit, MI. 1989;1324-1330.
25. Geffner H., Pearl J. Distributed diagnosis of systems with multiple faults. Proc. Third IEEE Conf. on AI Application, Orlando, FL. 1987;156-162.
26. Gilmore S., Hillston J. The PEPA workbench: A tool to support a Process Algebra based approach to performance modelling. Proc. Seventh International Conference on Modelling Techniques and Tools for Computer Performance Evaluation, Vienna. 1994.
27. Hamscher W., Console L., de Kleer J., Readings in Model-Based Diagnosis. 1992;Morgan Kaufmann, San Mateo, CA.
28. Hillston J. A Compositional Approach to Performance Modelling. 1996;Cambridge University Press, Cambridge.
29. Hoare C.A.R. Communicating Sequential Processes. 1985;Prentice-Hall, Englewood Cliffs, NJ.
30. Leitch R., Freitag H., Shen Q., Struss P., Tornielli G. Artist: A methodological approach to specifying model-based diagnostic systems. Guida G., Stefanini A., Industrial Applications of Knowledge-Based Diagnosis. 1992;183-208 Elsevier, Amsterdam.
31. Lucas P. Analysis of notions of diagnosis. Artificial Intelligence. 105:(1-2):1998;295-343.
32. Malhotra M., Trivedi K. Dependability modeling using Petri nets. IEEE Trans. Reliability. 44:1995;428-440.
33. Malik A., Struss P. Diagnosis of dynamic systems does not necessarily require simulation. Proc. 7th Internat. Workshop on Principles of Diagnosis. 1996;147-156.
34. Milne R., Applications Compendium, Monet, The European network of excellence in model-based systems and qualitative reasoning. 2000; http://monet.aber.ac.uk/members_docs/members_pages/databases.htm.
35. Milner R. Communication and Concurrency. 1989;Prentice-Hall, Englewood Cliffs, NJ.
36. Mosterman P.J., Biswas G. Monitoring, prediction and fault isolation in dynamic physical systems. Proc. AAAI-97, Providence, RI. 1997;100-105.
37. Mosterman P.J., Biswas G. Diagnosis of continuous valued systems in transient operating regions. IEEE Trans. Systems, Man Cybernetics. 29:(6):1999;554-565.
38. Pearl J. Probabilistic Reasoning in Intelligent Systems. 1989;Morgan Kaufmann, San Mateo, CA.
39. Pencolé Y., Cordier M.-O., Rozé L. Incremental decentralized diagnosis approach for the supervision of a telecommunication network. Proc. 12th Internat. Workshop on Principles of Diagnosis (DX01). 2001;151-158.
40. Peng Y., Reggia J. A probabilistic causal model for diagnostic problem solving - Part I: Integrating symbolic causal inference with numeric probabilistic inference. IEEE Trans. Systems, Man Cybernetics. 17:(2):1987;146-162.
41. Peng Y., Reggia J. Abductive Inference Models for Diagnostic Problem Solving. 1991;Springer-Verlag, Berlin.
42. Peterson J.L. Petri Net Theory and the Modeling of Systems. 1981;Prentice-Hall, Englewood Cliffs, NJ.
43. Plotkin G.D. An operational semantics for CSP, Technical Report CSR-114-82. May 1982;The University of Edinburgh.
44. Poole D. Normality and faults in logic-based diagnosis. Proc. IJCAI-89, Detroit, MI. 1989;1304-1310.
45. Poole D., Goebel R., Aleliunas R. THEORIST: A logical reasoning system for defaults and diagnosis. Cercone N., Mc Calla G., The Knowledge Frontier. 1987;331-352 Springer-Verlag, Berlin.
46. Preist C., Eshghi K., Bertolino B. Consistency-based and abductive diagnoses as generalized stable models. Annals of Mathematics and Artificial Intelligence. 11:(1-4):1994;51-74.
47. Raiman O., de Kleer J., Saraswat V., Shirley M. Characterizing non-intermittent faults. Proc. AAAI-91, Anaheim, CA. 1991;849-854.
48. Reggia J.A., Nau D.S., Wang P.Y. Diagnostic expert systems based on a set covering model. Internat. J. Man-Machine Studies. 19:(5):1983;437-460.
49. Reiter R. A theory of diagnosis from first principles. Artificial Intelligence. 32:(1):1987;57-96.
50. Riese M. Diagnosis of communicating systems: Dealing with incompleteness and uncertainty. Proc. IJCAI-93, Chambery, France. 1993;1480-1485.
51. Rozé L. Supervision de Réseaux de Télécommunication: Une approche à base de modèles, PhD thesis. 1997;Univ. de Rennes I.
52. Sachenbacher M., Malik A., Struss P. From electrics to emissions: Experiences in applying model-based diagnosis to real problems in real cars. Proc. 9th Internat. Workshop on Principles of Diagnosis. 1998;246-253.
53. Sampath M., Sengupta R., Lafortune S., Sinnamohideen K., Teneketzis D. A discrete event systems approach to failure diagnosis. Proc. 5th Internat. Workshop on Principles of Diagnosis. 1994;269-277.
54. Sampath M., Sengupta R., Lafortune S., Sinnamohideen K., Teneketzis D. Diagnosability of discrete event systems. IEEE Trans. Automatic Control. 40:(9):1995;1555-1575.
55. Sampath M., Sengupta R., Lafortune S., Sinnamohideen K., Teneketzis D. Failure diagnosis using discrete event models. IEEE Trans. Control Syst. Technol. 4:(2):1996;105-124.
56. Scarl E. Sensor placement for diagnosability. Annals of Mathematics and Artificial Intelligence. 11:(1-4):1994;493-510.
57. Selman B., Levesque H. Abductive and default reasoning: A computational core. Proc. AAAI-90, Boston, MA. 1990;343-348.
58. Struss P. Knowledge-based diagnosis: an important challenge and touchstone for AI. Proc. ECAI-92, Vienna. 1992;863-874.
59. ten Teije A., van Harmelen F. An extended spectrum of logical definitions of diagnosis. Proc. 5th Internat. Workshop on Principles of Diagnosis. 1994;334-342.
60. Travé Massuyès L., Dague P., Guerrin F. Le Raisonnement Qualitatif. 1997;Hermes, Paris.
61. Williams B., Nayak P. A model-based approach to reactive self-configuring systems. Proc. AAAI-96, Portland, OR. 1996;971-978.