The paper describes Pietro Torasso’s contribution to the area of diagnostic problem solving, starting from the early 80’s and recalling the work carried on by Pietro and by the research group he started at the Department of Computer Science of the University of Torino.
AnselmaL., MagroD. and TorassoP., Automatically decomposing configuration problems, In LNAI 2829, 2003, pp. 39–52. Springer.
2.
BrusoniV., ConsoleL., TerenzianiP. and Theseider DupréD., A spectrum of definitions for temporal model-based diagnosis, Artif Intell102(1) (1998), 39–79.
3.
ConsoleL., Theseider DupréD. and TorassoP., A theory of diagnosis for incomplete causal models. In Proceedings of the 11th International Joint Conference on Artificial Intelligence, Detroit, MI, USA, August 1989, 1989, pp. 1311–1317.
4.
ConsoleL., Theseider DupréD. and TorassoP., On the relationship between abduction and deduction, J Log Comput1(5) (1991), 661–690.
5.
ConsoleL., PicardiC. and RibaudoM., Process algebras for systems diagnosis, Artif Intell142(1) (2002), 19–51.
6.
ConsoleL., PortinaleL., Theseider DupréD. and TorassoP., Diagnosing time-varying misbehavior: An approach based on model decomposition, Ann Math Artif Intell11(1-4) (1994), 381–398.
7.
ConsoleL., RivolinA.J. and TorassoP., Fuzzy temporal reasoning on causal models, International Journal of Intelligent Systems6(2) (1991), 107–133.
8.
ConsoleL., TerenzianiP. and TheseiderD., Dupré, Local reasoning and knowledge compilation for efficient temporal abduction, IEEE Trans Knowl Data Eng14(6) (2002), 1230–1248.
9.
ConsoleL. and TorassoP., Diagnostic Problem Solving: Combining Heuristic, Approximate and Casual Reasoning, ISBN:0442237987. Wiley, 1990.
10.
ConsoleL. and TorassoP., Hypothetical reasoning in causal models, International Journal of Intelligent Systems5(1) (1990), 83–124.
11.
ConsoleL. and TorassoP., Integrating models of the correct behavior into abductive diagnosis, In ECAI, 1990, pp. 160–166.
12.
ConsoleL. and TorassoP., On the co-operation between abductive and temporal reasoning in medical diagnosis, Artificial Intelligence in Medicine3(6) (1991), 291–311.
13.
ConsoleL. and TorassoP., A spectrum of logical definitions of model-based diagnosis, Computational Intelligence7 (1991), 133–141.
14.
ConsoleL. and TorassoP., An approach to the compilation of operational knowledge from casual models, IEEE Trans Systems Man and Cybernetics22(4) (1992), 772–789.
15.
CravettoC., LesmoL., MolinoG. and TorassoP., Lito2: A framebased expert system for medical diagnosis in hepatology, Artificial intelligence in medicine, 1985, pp. 107–119.
16.
FoxM., LongD. and 1:Pddl2., Pddl2. 1: An extension to pddl for expressing temporal planning domains, Journal of Artificial Intelligence Research (2003).
17.
GhallabM., NauD. and TraversoPaolo, Automated Planning: Theory and practice, Elsevier, 2004.
18.
HamscherW., ConsoleL., de KleerJ., editors. Readings in Model-based Diagnosis. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 1992.
19.
LesmoL., MarzuoliM., MolinoG. and TorassoP., An expert system for the evaluation of liverfunctional assessment, Journal of Medical Systems8(1) (1984), 87–101.
20.
MagroD., Coconf: Conceptual language-based configuration, AI Communications23(1) (2010), 1–46.
21.
MagroD. and TorassoP., Interactive configuration capability in a sale support system: Laziness and focusing mechanisms, In Proc. IJCAI-01 Configuration WS, 2001, pp. 57–63.
22.
MagroD. and TorassoP., Supporting product configuration in a virtual store, LNAI2175 (2001), 176–188.
23.
MagroD. and TorassoP., Decomposition strategies for configuration problems, AIEDAM, Special Issue on Configuration17(1) (2003), 51–73.
24.
MicalizioR., ScalaE. and TorassoP., Intelligent supervision for robust plan execution, AI* IA: Artificial Intelligence Around Man and Beyond, 2011, pp. 151–163.
25.
MicalizioR. and TorassoP., On-line monitoring of plan execution: A distributed approach, Knowledge-Based Systems20(2) (2007), 134–142.
26.
MicalizioR. and TorassoP., Team cooperation for plan recovery in multi-agent systems. In Multiagent System Technologies, 5th German Conference, MATES 2007, Leipzig, Germany, September 24-26, 2007, Proceedings, 2007, pp. 170–181.
27.
MicalizioR. and TorassoP., Monitoring the execution of a multi-agent plan: Dealing with partial observability. In ECAI 2008 - 18th European Conference on Artificial Intelligence, Patras, Greece, July 21-25, 2008, Proceedings, 2008, pp. 408–412.
28.
MicalizioR. and TorassoP., Agent cooperation for monitoring and diagnosing a MAP. In Multi agent System Technologies,7th German Conference, MATES 2009, Hamburg, Germany, September 9-11, 2009. Proceedings, 2009, pp. 66–78.
29.
MicalizioR. and TorassoP., Cooperative monitoring to diagnose multi agent plans, Journal of Artificial Intelligence Research51 (2014), 1–70.
30.
MicalizioR., TorassoP. and TortaG., Online monitoring and diagnosis of multi-agent systems A model based approach. In Proceedings of the 16th Eureopean Conference on Artificial Intelligence, ECAI’ 2004, Valencia, Spain, August 22-27, 2004, 2004, pp. 848–852.
31.
MicalizioR., TorassoP. and TortaG., Intelligent supervision of plan execution in multi-agent systems, International Transactions on Systems Science and Applications(ITSSA)1(3) (2006), 259–268.
32.
MicalizioR., TorassoP. and TortaG., Online monitoring and diagnosis of a team of service robots Amodel-based approach, AI Commun19(4) (2006), 313–340.
33.
MozeticI., Hierarchical model-based diagnosis, International Journal of Man-Machine Studies35(3) (1991), 329–362.
34.
NauD., CaoY., LotemA. and Munoz-AvilaH., Shop: Simple hierarchical ordered planner. In Proceedings of the 16th international joint conference on Artificial intelligence-Volume 2, pp. 968–973. Morgan Kaufmann Publishers Inc.
35.
ScalaE., HaslumP., MagazzeniD. and ThiébauxS., Landmarks for numeric planning problems. In Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI 2017, Melbourne, Australia, August19-25, 2007, 2017, pp. 4384–4390.
36.
ScalaE., MicalizioR. and TorassoP., Robustplan execution via reconfiguration and replanning, AI Communications28(3) (2015), 479–509.
37.
ScalaE. and TorassoP., Proactive and reactive reconfiguration for the robust execution of multi modality plans. In Proceedings of the Twenty-first European Conference on Artificial Intelligence, 2014, pp. 783–788. IOS Press.
38.
ScalaE. and TorassoP., Deordering and numeric macro actions for plan repair. In Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI 2015, Buenos Aires, Argentina, July 25-31, 2015, 2015, pp. 1673–1681.
39.
TerenzianiP. and TorassoP., Towards an integration of time and causation in a hybrid knowledge representation formalism, Int J Intell Syst9(3) (1994), 303–338.
40.
TerenzianiP. and TorassoP., Time, action-types, and causation: An integrated analysis, Computational Intelligence11(3) (1995), 529–552.
41.
TorassoP. and ConsoleL., Approximate reasoning and prototypical knowledge, Int J Approx Reasoning3(2) (1989), 157–177.
42.
TorassoP. and TortaG., Computing minimum cardinality diagnoses using OBDDs, In KI 2003: Advances in Artificial Intelligence, 26th Annual German Conference on AI, KI 2003, Hamburg, Germany, September 15-18, 2003, Proceedings, 2003, pp. 224–238.
43.
TorassoP. and TortaG., Automatic abstraction of time-varying system models for model based diagnosis. In KI2005: Advances in Artificial Intelligence, 28th Annual German Conference on AI, KI 2005, Koblenz, Germany, September 11-14, 2005, Proceedings, 2005, pp. 176–190.
44.
TorassoP. and TortaG., Model-based diagnosis through OBDD compilation: A complexity analysis. In Reasoning, Action and Interaction in AI Theories and Systems, Essays Dedicated to Luigia Carlucci Aiello, 2006, pp. 287–305.
45.
TortaG. and TorassoP., Automatic abstraction in component-based diagnosis driven by system observability. In IJCAI-03, Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence, Acapulco, Mexico, August 9-15, 2003, 2003, pp. 394–402.
46.
TortaG. and TorassoP., On the use of OBDDs in model-based diagnosis: An approach based on the partition of the model, Knowl.-Based Syst19(5) (2006), 316–323.
47.
TortaG. and TorassoP., An on-line approach to the computation and presentation of preferred diagnoses for dynamic systems, AI Commun20(2) (2007), 93–116.
48.
TortaG. and TorassoP., On the role of modeling causal independence for system model compilation with obdds, AI Commun20(1) (2007), 17–26.
49.
TortaG. and TorassoP., Parametric abstraction of behavioral modes for model-based diagnosis, AI Commun22(2) (2009), 73–96.
50.
TortaG. and TorassoP., Automatic component abstractionfor model-based diagnosis on relational models, AICommun26(2) (2013), 179–209.
