Efficiency Issues in Model-Based Approaches to On-Board Diagnosis

1. Efficiency Issues in Model-Based Approachesto On-Board Diagnosis P.Torasso, C.Picardi and L. Console Dipartimento di Informatica- Universita’ di Torino Italy ESA Workshop “On-Board Autonomy”

2. Autonomy and Diagnosis • Onboard autonomy requires several components: planning and scheduling are very crucial • Autonomy requires also the ability of detecting what is going wrong and reacting • Diagnostic reasoning is an important step • Usually several competing diagnostic hypotheses • Need of informing the human operator about diagnostic conclusions? • Need of informing other software components ESA Workshop “On-Board Autonomy”

3. Knowledge-based approaches to diagnosis • Develop a domain theory for the system to be diagnosed • Logical/qualitative models • Correct and/or faulty behavior • Usually component oriented • Reasoning mechanisms for prediction and post-diction • In-depth understanding of diagnostic reasoning (theory of diagnosis) ESA Workshop “On-Board Autonomy”

4. Challenges of On-board Diagnosis (I) Off-board diagnosis is an iterative process • Available observations are used for making hypotheses about faults • Model-based approaches to diagnosis are able to suggest probes • Revision of the diagnostic hypotheses on the basis of new measurements • Strong interaction with the human agent On-board diagnosis has different requirements ESA Workshop “On-Board Autonomy”

5. Challenges of On-board Diagnosis (II) • Observations provided just via sensors • Strict time constraints for producing diagnostic conclusions • Difficulty/impossibility of acquiring extra data • Usually a large number of potential diagnoses • Need of complex reasoning mechanisms, very expensive from a computational point of view • Sometimes, on-board computation resources are limited ESA Workshop “On-Board Autonomy”

6. Efficiency issues • Decomposing static and time-varying aspects in diagnostic problem solving [Console et al. , Ann.Math&AI, 94] [Williams&Nayak, AAAI96] [Struss et al., DX96] • Exploiting observations for decomposing diagnostic problems [e.g. Darwiche IJCAI 99] • Exploiting hierarchical models for focussing diagnostic reasoning [Mozetic, IJMMS 91], [Out et al., Ann.Math&AI 94, Renon, IJCAI01] ESA Workshop “On-Board Autonomy”

7. Towards On-board Diagnosis At Università di Torino work on on-board diagnosis in the automotive sector: • European projects VMBD and IDD for the automotive sector in the space sector • ASI sponsored basic research project on “An Intelligent System for Supervising Autonomous Space Robots.” in co-operation with other Italian universities, CNR-IP ESA Workshop “On-Board Autonomy”

8. The compilation approach to on-board diagnosis (I) • Developed inside VMBD project [Cascio et al. AI Comm 99, Console et al, IJCAI 01] • Use of quite expressive qualitative models taking into account temporal and dynamic aspects of the system to be diagnosed • Diagnostic reasoning on such models too expensive for on-board computing • Off-line qualitative simulation of interesting diagnostic cases ESA Workshop “On-Board Autonomy”

9. The compilation approach to on-board diagnosis (II) • Automatic learning of decision trees starting from diagnostic cases obtained via simulation and fault injection • Use of the decision tree for on-board diagnosis ==> fast and small, suitable for current ECU • The nodes of the decision tree contain measurements provided by sensors in different time points and leaves contain (repair) actions • Discrimination among faults done as long as different actions can be done • Discriminative power limited by lack of sensors ESA Workshop “On-Board Autonomy”

10. Exploring diagnostic problem solving: the SPIDER case study • Investigated within a ASI supported project [Portinale&Torasso ISAIRAS 99, IJCAI 99] • A logical model relating behavioral modes of components of the SPIDER arm and contextual information with qualitative manifestations (based on FMECA documents available) • Innovative diagnostic strategies based on the notion of “Variable assignment problems” • Exploiting analogies with constraint satisfaction problem for a compact representations of a set of diagnoses (“scenario”) ESA Workshop “On-Board Autonomy”

11. Diagnostic Agent • Diagnosis as explanation of observations • DT behavioral model of SPIDER (nominal and faulty modes) • COMP the set of the components in SPIDER • CXT ground atoms modeling contextual information • OBS ground atoms representing observations ESA Workshop “On-Board Autonomy”

12. Diagnostic Agent (II) For most diagnostic problems a large number of diagnoses Preference criterion: based on a measure related to MDL Preferred solutions involving OK mode for most components In many cases no sufficient information for discriminating among alternative (preferred) diagnoses ESA Workshop “On-Board Autonomy”

13. A diagnostic case where only qualitative measurements provided sensors are available ESA Workshop “On-Board Autonomy”

14. Compact representation of multiple-fault diagnoses for the diagnostic case (only sensor data) ESA Workshop “On-Board Autonomy”

15. The same diagnostic case where all qualitative measurements are available ESA Workshop “On-Board Autonomy”

16. One multiple-fault diagnosis for the previous diagnostic case (all observations are available) ESA Workshop “On-Board Autonomy”

17. Discriminability among diagnoses Goal: Formalize the notion that two diagnoses cannot be discriminated using a given type of measurements Classes of observations: sensorized vs not sensorized In SPIDER domain several classes: joint positions, temperature sensors, observations under human control ESA Workshop “On-Board Autonomy”

18. Discriminability among diagnoses (II) Single out the behavioral modes of the components which cannot be discriminated using a class of manifestations Independent on the specific diagnostic problem at hand ESA Workshop “On-Board Autonomy”

19. Finding indistinguishable behavioral modes • Injecting fault bmr (and bms ) for component Ci • Evaluate the transitive closure for all possible contexts • Check if any difference is observable with meaurements in class CLj ESA Workshop “On-Board Autonomy”

20. Automatic generation of abstract models If diagnostic problem DP involves just manifestations of class i, some faults may be indistinguishable ==> • Generation of alternative diagnoses that cannot be discriminated • Computation time is consumed • Human or artificial supervisor may be confused Proposed solution: automatic generation of abstract models where indistinguishable modes are collapsed ESA Workshop “On-Board Autonomy”

21. Automatic generation of abstract models (II) A simplied form of the algorithm for: • collapsing behavioral modes • revising domain theory ESA Workshop “On-Board Autonomy”

22. Automatic generation of abstract models (III) Off-line: generate an abstract model for each class of measurements (if there are indistinguishable modes) On-line: for each diagnostic problem DP, select (from the model library) the model which fits available measurements Benefits: • saving in computation time • reduction in the number of diagnoses (if bmr and bms for component Ci are indistinguishable instead of two diagnoses just one involving bmr+s ) • no loss of information ESA Workshop “On-Board Autonomy”

23. Abstract models and abstraction of manifestations ESA Workshop “On-Board Autonomy”

24. using the abstract model just single fault diagnoses ESA Workshop “On-Board Autonomy”

25. Without abstract model, generation of multiple faults diagnoses with very low plausibility ESA Workshop “On-Board Autonomy”

26. Conclusions (I) On-board diagnosis is an interesting and challenging problem Need of multiple approaches: • efficient diagnostic strategies • partitioning the diagnostic problem • compilation techniques • automatic generation of abstract models if modes are indistinguishable • taking recovery into consideration ESA Workshop “On-Board Autonomy”

27. Conclusions (II) Interesting results obtained in the automotive sector • compilation techniques allow to put on-board diagnostic procedures which are automatically generated starting from a model of the artifact • diagnosability is an important aspect ESA Workshop “On-Board Autonomy”

28. Conclusions (III) • Efficiency is just one aspect • How to involve the human supervisor in the loop? ==> Interactive autonomy • How to make the result of the diagnostic agent understandable to the human supervisor? Some work in: • compact graphical representation of diagnoses • preference criteria among diagnoses • informing the supervisor about the impossibility of discriminating among diagnoses ESA Workshop “On-Board Autonomy”