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Overview About what?
MBR, MBD, QR History and state of art Examples: give you first feel
What is the content? Content of this part How to study this part? Reading papers,
reference books, assignments
I reference Luca Console’s slides in the first part. If most of the content of a slide is from his original one, I label his name on the slide.
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My Expectation in this Course Formal methods Only a few main important topics
Consistent-based diagnosis Multiple fault modes Select observation points ATM Qualitative modeling and reasoning
Examples More thinking, more pleasure, less
pressure
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Begin with an example
Mult1
Mult2
Mult3
Add2
2
3
23
23
Add1 A
B
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Observation vs. Prediction
Mult1
Mult2
Mult3
Add2
2
3
23
23
Add1 A
B(12)
(12)10
12
6
What’s wrong
Mult1
Mult2
Mult3
Add2
2
3
23
23
Add1 A
B(12)
(12)10
12
Diagnoses
{Add1}, {Mult1}, {Mult2, Add2}, {Mult2, Mult2}
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Model
Behavioral model of each type of component:Adder(X) not AB(X) out(X) = inp1(X) + inp2(X)Multiplier(X) not AB(X) out(X) = inp1(X) * inp2(X)...
Structural model:
Multiplier(Mult1), Multiplier(Mult2),
Multiplier(Mult3), Adder(Add1),
Adder(Add2)
out(Mult1) = inp1(Add1)
out(Mult2) = inp2(Add1) = inp1(Add2)
out(Mult3) = inp2(Add2)
Mult1
Mult2
Mult3
Add2
23
23
23
Add1 A
B
8
Mult1
Mult2
Mult3
Add2
23
23
23
Add1 A
B
Prediction: A=12, B=12
12
12Observation A=10, B=12
10
12
A=10 generates two conflicts:{Add1, Mult1, Mult2}{Add1, Mult1, Mult3, Add2}
conflict = set of components involved in the discrepancy; they cannot be all working properly
diagnosis = (minimal) hitting set of the conflicts;intersection between the conflicts provides single-fault diagnoses
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Diagnosis on models of structure and function
actualdevice
observedbehaviour
modelof the device
predicted behaviour
diagnosis
design textbook first principles ....
model of the structureof the device and of the (nominal) behaviourof each type ofcomponent
diagnosis = removing discrepanciesbetween the nominal predicted behaviourand the observed one
From Luca Console
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A Little Bit on History The ‘70s: heuristic approaches to
diagnosis The ‘80s
critique to the heuristic approach model-based diagnosis: the beginnings and the
basic techniques The ‘90s: state of the art The ‘00: opportunities for the future
From Luca Console
11
The ‘70s: heuristic diagnosis
the ‘70s: the expert systems agediagnosis is one of the main experimentation areas for expert systems a well-defined problem with definite boundaries
specific domain knowledge to be represented specific reasoning and problem solving strategies
Basic assumptions: diagnosis = heuristic process experts rely on associational knowledge of the form
symptomsfaults (diseases) knowledge derives from experience knowledge can be elicited from domain experts and
represented using suitable KR languages
From Luca Console
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Diagnostic expert systems: conceptual view
domain expert (and knowledge engineer)
K.B
K.A. interface
control K.B.
final user
user interface
work. mem.
inferenceengine
From Luca Console
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Case study 1: Mycin [Stanford Univ. 72-79]
Diagnosis and therapy of bacterial infections Knowledge base: production rules (original proposal)
if (1) the stain of the organism is gram-negative
(2) the morphology of the organisms is coccus(3) the growth configuration of the organism is chains
then there is a suggestive evidence (0.7)that the identity of the organisms is streptococcus
Inference strategy: backward chaining Approximate reasoning: ad-hoc heuristic approach Explanations: HOW, WHY, WHY-NOT ... Meta-rules for control from Mycin to Emycin and many other applications
From Luca Console
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Critiques to the heuristic approach
Some serious problems difficulties in acquiring and maintaining the
knowledge base experience knowledge
it is not easy to find experts who are usually not available
subjective knowledge dependent on the specific expert
it is impossible to deal with cases not considered a-priori
it is difficult to deal with multiple faults it is impossible to reuse knowledge in similar
devices or even in new versions of the same device
limited explanation capabilities
From Luca Console
15
The ‘80s: model-based reasoning
New tendency (late 70s - beginning of the 80’s)experience heuristic knowledge model of the system to be diagnosed
“objective” model, not specific for diagnosis (task independent)
New approach to knowledge-based systems based on “deep knowledge” based on “first principles” second-generation expert systems “model-based”
From Luca Console
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What is model-based reasoning (MBR) an inferring process using models
abstracted from the reality of a system. MBR is the symbolic processing of an explicit representation of the internal working of a system in order to predict, simulate and explain the resultant behaviour of the system from the structure, causality, functional and behaviour of its components
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Model-based Diagnosis (MBD) A main application task for MBR MBD = finding malfunctions (faults,
diseases ...) in a system starting from a set of observations (measurements, tests, symptoms, examinations ...) and system description.
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Model-based diagnosis
Different approaches different types of models different definitions of diagnosis
predictedbehaviour
actualsystem
observedbehaviour
diagnosis
modelof the system
design textbook first principles....
• “knowledge level” view:
From Luca Console
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Two Different Evolutions
Diagnosis on causal models “process centered” approach born in medical domains, then application to
diagnosis of industrial processes and devices model: causal description of the behavior of the
system, in normal and/or faulty conditions Diagnosis on models of structure and function
“component centered” approach born in technical domains, then other
applications model: description of the structure of a device
(components and their connections) and of the function of each type of component
From Luca Console
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Diagnosis on causal models
actualsystem
observedbehaviour
modelof the system
predicted behaviour
diagnosis
design textbook first principles ....
causal modelsof the behaviour(correct and/orfaulty)
diagnosis = covering (explanation)of the observations via causal chainsoriginated by the faulty behaviour
From Luca Console
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Example
Obs1 = {engine_temp(high)}Two minimal candidate explanations E1 = { oil_cup(holed) } E2 = {radiator(holed)}
oil_cup
normal holed
oil_level
normal low
oil_loss
oil_below_car
oil_gauge
normal red
radiatornormal holed
water_levelnormal low
water_tempnormal high
engine_tempnormal high
engine_on
From Luca Console
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Example
Obs2 = {oil_gauge(red), engine_temp(high)}
One minimal candidate explanation E1 = { oil_cup(holed) }
oil_cup
normal holed
oil_level
normal low
oil_loss
oil_below_car
oil_gauge
normal red
radiatornormal holed
water_levelnormal low
water_tempnormal high
engine_tempnormal high
engine_on
From Luca Console
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Defining and computing diagnoses
Diagnosis: Given a set of observations determine a (minimal) set of faults whose
consequences cover the observations “Knowledge level”: diagnosis = abductive process
determine an explanation of the observations using the model as the domain theory
Abduction: Given a theory T and a set Obs of observations to
be explained Determine a set E such that
T E |= Obs T E consistent
Diagnosis as set covering
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Comparing abduction, deduction and induction
Deduction: major premise: All balls in the box are black minor premise: These balls are from the box conclusion: These balls are blackAbduction: rule: All balls in the box are black observation: These balls are black explanation: These balls are from the boxInduction: case: These balls are from the box observation: These balls are black hypothesized rule: All ball in the box are black
Induction: from specific cases to general rules Abduction and deduction: both from part of a specific case to other part of the case using general rules (in different ways)
A=>BA--------B
A=>B B--------Possibly A
Whenever A then B but not vice versa---------PossiblyA=>B
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Abduction-based diagnosis Originally, diagnostic systems were
abductive ones and relied on fault models AB(c) => symptom B.
They are able to give explanations of symptoms from the diagnosis: if diagnosis AB(c) is inferred by abduction, it explains (in the sense implies or entails) symptom B.
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Diagnosis on models of structure and function
Models of structure and function Generating prediction starting from the
model of the correct behaviour (and the inputs)
Analysis of the discrepancies between predicted and observed behaviour; for each predicted value that differs from the observed one: conflict = set of components involved in the
discrepancy; they cannot be all working properly generating all conflicts (actually only the minimal
ones) at least one component in each conflict must be
faulty diagnosis = (minimal) hitting set of the conflicts
From Luca Console
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Diagnosis on models of structure and function(II)
actualdevice
observedbehaviour
modelof the device
predicted behaviour
diagnosis
design textbook first principles ....
model of the structureof the device and of the (nominal) behaviourof each type ofcomponent
diagnosis = removing discrepanciesbetween the nominal predicted behaviourand the observed one
From Luca Console
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Model
Behavioral model of each type of component:Adder(X) not AB(X) out(X) = inp1(X) + inp2(X)Multiplier(X) not AB(X) out(X) = inp1(X) * inp2(X)...
Structural model:
Multiplier(Mult1), Multiplier(Mult2),
Multiplier(Mult3), Adder(Add1),
Adder(Add2)
out(Mult1) = inp1(Add1)
out(Mult2) = inp2(Add1) = inp1(Add2)
out(Mult3) = inp2(Add2)
Mult1
Mult2
Mult3
Add2
23
23
23
Add1 A
B
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Modeling Modeling is the critical aspect of model-based diagnosis Each model is an abstraction of the actual physical system
different choices and assumption in modeling different dimensions (aspects) are captured by different types
of models choosing the models depend on many factors
which pieces of information are available which are the goals of diagnosis which observations (and in which form) can be available which repair and test action can be made temporal constraints on the behavior of the device and on
the diagnostic process ....
Different dimensions in modeling
From Luca Console
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Dimensions in modeling (not exhaustive !)
component orientedprocess oriented
causal models process modelsstructuralmodels
functionalmodels
behaviouralmodels
teleologicalmodels
correctbehaviour
faultmodels
static dynamic time-varying
quantitative qualitative
discrete state change derivatives
intensional extensional
landmarksintervals
orders ofmagnitude
...
....
... ...
... ...
crisp probabilistic
(similar to comp. oriented.)
hierarchicalflat
From Luca Console
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Qualitative Model Quantitative (numeric) models: based on
mathematical equations in some cases they are derived from physical laws but usually
they are difficult to use they are not what people use to solve problems
Qualitative models abstract models they need a “new mathematics”, that is “common-
sense” forms of reasoning to solve qualitative equations closer than numeric models to the way we reason problem: being more abstract they are less accurate and
can be ambiguous
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One Example on Qualitative Model Exampel: qualitative sum
f = f1 + f2 (notice that there are ambiguous cases)
+ neg 0 posneg neg neg ??0 neg 0 pospos ?? pos pos
* neg 0 posneg pos 0 neg0 0 0 0pos neg 0 pos
Similar to define qualitative multiple
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Qualitative Modeling Capture the fundamental aspects of a
system or mechanism Suppress much of the detail Abstraction and Approximation Simulating (process)
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Qualitative Reasoning An inferring technique using qualitative
model to derive new knowledge of, and gain insight into a system
An approach used by human being’s thinking and reasoning
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Limitation of QR Ambiguous Less accuracy More compact or more volume?
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Research topics in MBD and QR Modeling:
New modeling methods Model reuse Model checking, verification
Diagnosis methods on: Hybrid system Dynamic system On-board system
New application areas: Biometrics Ecology E-business
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The ’00: Who in this area Crucial Missions: satellites, nuclear plants,
chemistry plants, space shuttle, Rockets E.g. NASA, Thales, Ontario power
Manufacturing: automobile E.g. all car makers, Bosch
Technique suppliers for the above two Academic and research institutes Other areas:
Biomedical Education Business
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Application: Business system modeling and performance analysis Causal-effect relation modeling and
reasoning Explanation on the reasons of one
performance indicator Hybrid system: both qualitative and
quantitative relation
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Application: distributed business application modeling and diagnosis B to B applications:
supply chain management; vendor matching on marketplaces; cooperative project; virtual organizations/enterprises
B to C applications: through web services distributed business process modeling:
Workflow network Business description language Communication protocol and semantic management Knowledge management (inside/outside)
Features: Dynamic environment; Distributed environment
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Workflow network
Pic from F.Laymann and D.Roller, “Workflow-based Applications”, IBM Systems Journal, Vol 36, No. 1, 1997
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Workflow-based Application
WFMS
Execution
WFMS
Execution
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Research issues How to model: design phase Features of the model
Deadlock Loop Resources sharing
Diagnosis: executing phase If the performance is abnormal, where is the
fault? Inside/outside? Recovery actions? Distributed diagnosis?
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Application: Logic in NBON Initial: logic expression to describe vendor Review: manually check false positive, false
negative Adjust: more accurate logic expression
Not a typical diagnostic problem The constraints are independent No constraint propagation, no deduction No cause-effect relation if don’t exploit business pattern Need to modify the “model”, not deduce from the model Can solve with simple algorithm
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Prepare yourself to read the proofs Deduction theorem, which is known to the
ancient GreekFor any sentences and , |= if and only if the
sentence ( =>) is valid
Reductio ad absurdum (proof by refutation, proof by contradiction)
|= if and only if the sentence (¬) is unsatisfiable
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