1 / 32

Model-based Technology

Model-based Technology. George M. Coghill. 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. What happens next?. … and in this case?.

page
Download Presentation

Model-based Technology

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

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? qi V qo

  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. B F A C D J E G H Forward Chaining • 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 or and I

  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. Input Data Inference Engine Model Behaviour What is a Model? • Assume knowledge • You’ve 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

  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. 0 - + 0 l2 l1 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

  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. T (oC) 100 Precise, Certain, Correct T (oC) 100 105 Precise, Certain, Incorrect T (oC) 95 105 100 Imprecise, Certain, Correct 115 T (oC) 105 100 Imprecise, Certain, Incorrect 105 T (oC) 95 100 Precise, Uncertain, Correct T (oC) 100 Precision and Uncertainty Imprecise, Uncertain, Correct

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

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

  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 -> d0 (zeroth derivative) x’ -> d1 (first derivative) x” -> d2 (second derivative) . . . [x] = (d0, d1, d2 . . .) • Usually need at least two elements in a vector (three is better because curve shapes can be seen).

  22. _ • d2 + 0 • d1 + 0 _ Qualitative Vectors (2)

  23. Qualitative Calculus [x] = [d0, d1, d2 ] Intg(x) = [d0I, d1I, d2I] D(x) = [d0D, d1D, d2D] For Integration: d0I = d0 + d1 = d1I + d2I (by Taylor’s Theorem) For Differentiation: d2D: 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. Heater Tank A Tank B Heated Tanks

  30. 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 Component Representation

  31. 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 Process Representation

  32. 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) Constraint Model

More Related