Causality analysis and fault ascription in component-based systems

This article introduces a general framework for fault ascription, which consists in identifying, within a multi-component system, the components whose faulty behavior has caused the failure of said system. Our framework uses configuration structures as a general semantical model to handle truly concurrent executions, partial and distributed observations in a uniform way. We define a set of expected properties for counterfactual analysis, and present a refined analysis that conforms to our requirements. This contrasts with current practice of evaluating definitions of counterfactual causality a posteriori on a set of toy examples. As an early study of the behavior of our analysis under abstraction we establish its monotony under refinement.

[1]  Sjur K. Dyrkolbotn.  On Preemption and Overdetermination in Formal Theories of Causality , 2017, CREST@ETAPS.

[2]  Mark Hopkins,et al.  Causality and Counterfactuals in the Situation Calculus , 2007, J. Log. Comput..

[3]  Florian Leitner-Fischer,et al.  Symbolic Causality Checking Using Bounded Model Checking , 2015, SPIN.

[4]  Judea Pearl,et al.  Theoretical Impediments to Machine Learning With Seven Sparks from the Causal Revolution , 2018, WSDM.

[5]  Inseok Hwang,et al.  A Survey of Fault Detection, Isolation, and Reconfiguration Methods , 2010, IEEE Transactions on Control Systems Technology.

[6]  Joseph Y. Halpern,et al.  Causes and Explanations: A Structural-Model Approach. Part I: Causes , 2000, The British Journal for the Philosophy of Science.

[7]  Andrew C. Myers,et al.  Observational determinism for concurrent program security , 2003, 16th IEEE Computer Security Foundations Workshop, 2003. Proceedings..

[8]  Joseph Y. Halpern.  A Modification of the Halpern-Pearl Definition of Causality , 2015, IJCAI.

[9]  Bart Jacobs,et al.  Causal Inference by String Diagram Surgery , 2018, FoSSaCS.

[10]  Stefan Haar,et al.  Diagnosis with Petri Net Unfoldings , 2013, Control of Discrete-Event Systems.

[11]  Oleg Sokolsky,et al.  Counterfactual Causality from First Principles? , 2017, CREST@ETAPS.

[12]  Jaco Geldenhuys,et al.  Model Checking Software , 2015, Lecture Notes in Computer Science.

[13]  Javier Esparza,et al.  An Unfolding Algorithm for Synchronous Products of Transition Systems , 1999, CONCUR.

[14]  Christos G. Cassandras,et al.  Introduction to Discrete Event Systems , 1999, The Kluwer International Series on Discrete Event Dynamic Systems.

[15]  Albert Benveniste,et al.  Distributed Monitoring of Concurrent and Asynchronous Systems , 2003, CONCUR.

[16]  Stéphane Lafortune,et al.  Failure diagnosis using discrete-event models , 1996, IEEE Trans. Control. Syst. Technol..

[17]  J. McCarthy.  Situations, Actions, and Causal Laws , 1963 .

[18]  Samantha Kleinberg,et al.  Causality, Probability, and Time , 2012 .

[19]  Gordon D. Plotkin,et al.  Configuration structures, event structures and Petri nets , 2009, Theor. Comput. Sci..

[20]  Choh Man Teng,et al.  Actual causation: a stone soup essay , 2009, Synthese.

[21]  Stéphane Lafortune,et al.  Overview of fault diagnosis methods for Discrete Event Systems , 2013, Annu. Rev. Control..

[22]  Judea Pearl,et al.  Probabilistic Evaluation of Counterfactual Queries , 1994, AAAI.

[23]  Walter Fontana,et al.  Counterfactual Resimulation for Causal Analysis of Rule-Based Models , 2018, IJCAI.

[24]  Louise Travé-Massuyès,et al.  Bridging control and artificial intelligence theories for diagnosis: A survey , 2014, Eng. Appl. Artif. Intell..

[25]  Paolo Baldan,et al.  Unfolding-Based Diagnosis of Systems with an Evolving Topology , 2008, CONCUR.

[26]  C. Granger.  Testing for causality: a personal viewpoint , 1980 .

[27]  Ilan Beer,et al.  Explaining counterexamples using causality , 2009, Formal Methods in System Design.

[28]  Matthias Kuntz,et al.  From Probabilistic Counterexamples via Causality to Fault Trees , 2011, SAFECOMP.

[29]  Mogens Nielsen,et al.  Models for Concurrency , 1992 .

[30]  Alex Simpson,et al.  Foundations of Software Science and Computation Structures 22nd International Conference, FOSSACS 2019, Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2019, Prague, Czech Republic, April 6–11, 2019, Proceedings , 2019 .

[31]  Florian Leitner-Fischer,et al.  Probabilistic fault tree synthesis using causality computation , 2013, Int. J. Crit. Comput. Based Syst..

[32]  Raymond Reiter,et al.  A Theory of Diagnosis from First Principles , 1986, Artif. Intell..

[33]  Robert Stalnaker.  A Theory of Conditionals , 2019, Knowledge and Conditionals.

[34]  Dilsun Kirli Kaynar,et al.  Program Actions as Actual Causes: A Building Block for Accountability , 2015, 2015 IEEE 28th Computer Security Foundations Symposium.

[35]  Gregor Gößler,et al.  A general framework for blaming in component-based systems , 2015, Sci. Comput. Program..

[36]  J. Pearl.  Causality: Models, Reasoning and Inference , 2000 .

[37]  Joseph Y. Halpern,et al.  Graded Causation and Defaults , 2013, The British Journal for the Philosophy of Science.

[38]  Gregor Gößler,et al.  Fault Ascription in Concurrent Systems , 2015, TGC.

[39]  Lacramioara Astefanoaei,et al.  Blaming in component-based real-time systems , 2014, 2014 International Conference on Embedded Software (EMSOFT).