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Model-based Technology George M. Coghill. Introduction Description of the Field Motivations for development Applications of MBT Overview Models Background.

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Presentation on theme: "Model-based Technology George M. Coghill. Introduction Description of the Field Motivations for development Applications of MBT Overview Models Background."— Presentation transcript:

1 Model-based Technology George M. Coghill

2 Introduction Description of the Field Motivations for development Applications of MBT Overview Models Background Text which may be consulted: Qualitative Reasoning Ben Kuipers, MIT Press 1994

3 What happens next?

4 … and in this case? V qiqi qoqo

5 Model-based Reasoning Qualitative Reasoning –Symbolic, using no numbers –Structural though incomplete –Synonyms: Naive physics, Qualitative modelling, Qualitative simulation, Commonsense reasoning, Deep knowledge. Developments –Use of any models in the domain reasoning process –Numerical, Interval, Semi-quantitative, Fuzzy, Qualitative, Rule-based, Procedural

6 Systems Natural Systems –Physical: Fluid behaviour, Chemical reactions –Biological: Drug uptake, Cardiac performance, Renal operation, Photosynthesis –Ecological Artificial Systems –Physical: Electrical circuits, Mechanical systems, Chemical plant –Economic: Housing markets, Organisations

7 Forward Chaining B F A G C D E H I R1: If A then B R2: If B and C then D R3: If B and E then F R4: If D then G R5: If F then G R6: If G and H then I R7: If G then J J and or

8 Motivations Problems with RBS –Reasoning from First Principles –Dangers with nearest approximation Second Generation Expert Systems –Use deep knowledge –Provide explanations of reasoning process Commonsense reasoning –Capture how humans reason –Enable use of appropriate causality Model reuse –Improved ease of ES maintenance

9 Applications of MBT Domains of Application –Modelling of ecological systems –Diagnosis of industrial plant –Training of process operators –Control of process plant Industrial Investment –Number of large collaborative projects involving industry (e.g. Unilever, Siemens, BG) and academia Eye to the future –Industrial rollout –Focus on the essence of Modelling

10 Applications of MBT (2) Development methods –KADS - Expert Systems development –ARTIST, PRIDE - Model-based Diagnosis Communication infrastructure –European - Monet –National - R&R (UK), MQD (France) Commercialisation –Tiger - Diagnosis of Gas Turbines –FLAME (Autosteve) - FMEA and Diagnosis of car electrics

11 Overview Background and Basics –Ontologies, Quantity spaces, Qualitative arithmetic, Operations, Causality. Major methods of QR –Devices, Processes and Constraints –Focus on constraint based and developments Reasoning Domains –Explanation, Diagnosis, Training, Prediction, Spatial reasoning, Kinematics, Learning Modelling Methodologies –Teleological, Behavioural, Multimodelling, Multiple models

12 Assume knowledge Youve all come across them. Declarative Structure Representation –Executable but distinct from inference mechanism. Prediction: –What value will it have? Explanation –Why did it happen that way? –Facilitates understanding of system What is a Model? Inference Engine Input Data BehaviourModel

13 Basic Principles of QR Terminology and Concepts –new(ish) field: proliferation of terms –underlying concepts basis for all QR Symbollically represents the important (qualitative) distinctions in a system –increasing, steady, decreasing –high, medium, low Scales of Measurement –nominal, ordinal, interval, ratio Qualitative versus Quantitative?

14 Qualitative Reasoning Components of a Qualitative Model –Ontology (a way of looking at the world) –Variables (things that change) –Quantity space (values variables take) –Relations (what variables do to each other) Quantity Spaces l1 l2

15 Qualitative Relations Behavioural Abstraction

16 Qualitative Relations (2) Incompleteness –Not the same as Uncertainty but is related to Precision –Known model structure (assumed) –Imprecise knowledge of system functional relations Operators –ADD, MULT, DERIV, Monotonic functions

17 Precision and Uncertainty T ( o C) 100 T ( o C) 100 Precise, Certain, Correct T ( o C) Precise, Certain, Incorrect T ( o C) Imprecise, Certain, Correct T ( o C) Imprecise, Certain, Incorrect T ( o C) Precise, Uncertain, Correct Imprecise, Uncertain, Correct

18 Arithmetic Operations Sign Algebra _ _ MULT DIV + + _ _ _ _ + + _ _ 0 0 X X X

19 Aritmetic Operations (2) _ _ _ _ _ + _ 0 + ? ? _ _ ? ? + + _ _ ADD SUB

20 Arithmetic Operations (3) A = B - C where B & C both have value [+], A will be undefined Disambiguation –may be possible from other information –A = [+] if B > C –A = [0] if B = C –A = [-] if B < C Functional Relations –Y = M+(X) –Y = M-(X)

21 Qualitative Vectors Convenient representation of state and behaviour Consists of Magnitude and first n derivatives of a variable: x -> d 0 (zeroth derivative) x -> d 1 (first derivative) x -> d 2 (second derivative)... [x] = (d 0, d 1, d 2...) Usually need at least two elements in a vector (three is better because curve shapes can be seen).

22 Qualitative Vectors (2) _ _ d1d1 d2d2

23 Qualitative Calculus [x] = [d 0, d 1, d 2 ] Intg(x) = [d 0 I, d 1 I, d 2 I ] D(x) = [d 0 D, d 1 D, d 2 D ] For Integration: d 0 I = d 0 + d 1 = d 1 I + d 2 I (by Taylors Theorem) For Differentiation: d 2 D : depends on what is known of the original function (or system in which it appears)

24 Model Types Static (Equilibrium) –algebraic equations only [A] = [B] + [C] [X] = M+([Y]) M = U * V Dynamic –contains derivatives, –requires integration x = k.x y = xdt –may also have algebraic parts NB: Dynamic is not the same as time varying!!!

25 Model Types (2) Continuous –no gaps in quantity space –no jumps allowed –focus of QR (mainly) Discontinuous/Discrete –finite number of gaps in quantity space –jumps can occur

26 Behaviour Types Results of Simulation/Inference are known as ENVISIONMENTS TOTAL ENVISIONMENT –All possible behaviours for all possible inputs COMPLETE ENVISIONMENT –All possible behaviours for a specific input ATTAINABLE ENVISIONMENT –All behaviours from a specified initial value and input ~ with a fixed quantity space PARTIAL ENVISIONMENT –All behaviours from a specified initial value and input ~ with landmark generation

27 Behaviour Types (2) Envisionments are represented as a graph or a tree BEHAVIOUR –Single path through an envisionment graph or behaviour tree HISTORY –Behaviour of a single variable removed from its envisioned context.

28 Ontology A way of representing what there is in the world (closed) Two (main) perspectives: –Functional: focuses on purpose (design) –Behavioural: focuses on operation Three Behavioural Ontologies: –Devices (Components): pipes, tanks valves –Processes: heating, reacting, decomposing –Constraints: relations between variables

29 Heated Tanks Heater Tank A Tank B

30 Component Representation ADD(D1 D2 Q) MULT(Fab T D1) M+(D2 T_dash) DERIV(T T_dash) M+(La Fab) MINUS(Fba Fab) DERIV(La Fba) M+(Fab Lb) DERIV(Lb Fab) Heater Tank A Tank B

31 Process Representation ADD(D1 D2 Q) MULT(Fab T D1) M+(D2 T_dash) DERIV(T T_dash) M+(La Fab) MINUS(Fba Fab) DERIV(La Fba) M+(Fab Lb) DERIV(Lb Fab) Heating Flow Containment

32 Constraint Model M+(Fab Lb) M+(La Fab) M+(D2 T_dash) ADD(D1 D2 Q) MINUS(Fba Fab) MULT(Fab T D1) DERIV(La Fba) DERIV(Lb Fab) DERIV(T T_dash)


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