Outline 1 The topic 2 Decision support systems 3 Modeling 3.3 - - PowerPoint PPT Presentation

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

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1 The topic 2 Decision support systems 3 Modeling 3.3 Advanced modeling

Outline

 Compositional modeling: requirements  Conceptual modeling: Why? How?  Qualitative modeling: Why? Limitations?  Automated model composition

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

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Ecological Modeling and Decision Support Systems Requirements

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

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Model Structure (Townsend et al. 08)

Adult vultures in year t, Nt Survival Maturation and survival Rate at which carcasses are eaten, F Probability of a carcass containing diclofenac, C Effect of diclofenac Baseline survival, S Rate at which carcasses are eaten, F Probability of a carcass containing diclofenac, C Effect of diclofenac Baseline survival, S Adult vultures in year t-1, Nt-1 Vulture births in year t-5

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

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Re-arranged Structure of a Model of Vulture Population

Adult vultures in year t, Nt Survival Maturation and survival Adult vultures in year t-1, Nt-1 Vulture births in year t-5 Baseline survival, S Probability of carcasses with diclofenac, C Effect of diclofenac Rate at which carcasses are eaten, F

 Why re-arranged?  Reuse of model elements

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

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Requirements

 Compositional modeling – Complex model: aggregation of elementary model fragments – Requires conceptual modeling  Conceptual modeling – Represent objects, relationships, interactions explicitly  Qualitative modeling – Models capturing partial knowledge and information

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

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1 The topic 2 Decision support systems 3 Modeling 3.3 Advanced modeling 3.3.1 Conceptual modeling

Outline

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

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Ecological Modeling and Decision Support Systems Processes - Motivating Examples

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

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Process-oriented Modeling - Part 1

 Identify and model elementary, independent phenomena/interactions: “processes”

Population size Reproduction Death Immigration Emigration

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

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Process-oriented Modeling – Part 2

 Identify preconditions for the process to happen – objects – object relations – quantity conditions

DiclCarcasses Prob.: C>0 VulturePop N>0 SameLocation (VulturePop, DiclCarsasses) Dicl Poisoning

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

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Process-oriented Modeling – Part 2 Cont’d

 Identify preconditions for the process to happen – objects – object relations – quantity conditions

Resources amount>0 Population density > d0 Accessible (Population, Resources) Reproduction

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

WS 14/15 EMDS 3 - 80

Process-oriented Modeling – Part 3

Dicl Carcasses Vulture Pop DiclCarcasses Prob.: C>0 VulturePop N>0 SameLocation (VulturePop, DiclCarsasses) Dicl Poisoning

 Identify and describe impact on objects/relations

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

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Process-oriented Modeling – Part 3 Cont’d

Resources Population Resources amount>0 Population density > d0 Accessible (Population, Resources) Reproduction

 Identify and describe impact on objects/relations

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

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Process-oriented Modeling – Part 4

 Describe effects on quantities

Resources Population Resources amount>0 Population density > d0 Accessible (Population, Resources) Reproduction

“ dN/dt = r·N ”

?

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

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• 83

Ecological Modeling and Decision Support Systems Processes – More Formally

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

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Process-Oriented Modeling

Model Fragment (Process)  Conditions  Structure (objects,

• bject relations)

 Quantities  Effects  Structure  Quantities

STRUCT-CONDS

 QUANT-CONDS 

STRUCT-EFFECTS

 QUANT-EFFECTS

„Model“ in the „classical“ sense

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

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Formal Basis for Reasoning about Models

 Process: a logical formula   deduction   consistency check   model composition   model-based diagnosis   model-based therapy  …

STRUCT-CONDS

 QUANT-CONDS 

STRUCT-EFFECTS

 QUANT-EFFECTS

 Conceptual layer   explanation   education

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

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• 86

Ecological Modeling and Decision Support Systems More Examples and Demonstration

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

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Example: Chloramin Reactions

 Water treatment: protocol of knowledge acquisition from experts  Preconditions – Structural (substances) – Quantity  Effects – Structural (new substances) – Quantity

CHLORAMINE REACTION NH4

+ + HClO  NH2Cl + H2O + H+

\monochloramines (1) NH2Cl + HClO  NHCl2 + H2O \dichloramines (2) NHCl2 + HClO  NCl3 + H2O \trichloramines (3) The pH determines the stability of chloramine compounds. So in excess chlorine concentration, it’s possible to say that monochloramine stability go down, and it decomposed to: 2NH2Cl + HClO  N2 + 3HCl + H2O (4) If the pH is favorable to dichloramines existence, it is decomposed as: NH2Cl  N2 + 2HCl + Cl2 (5) Cl2 + H2O  HClO + HCl (6) If the pH is favorable to dichloramines and monochloramines existence, then (4) will be the predominant reaction. So the water with this conditions won’t have trichloramine (NCl3), because they will be reduced to dichloramine form, as: NCl3 + H2O  NHCl2 + HClO (7) Finally, to simplifying the reaction it can be said that amonium is totally destroyed by chlorine as: 2NH3 + 3Cl2  6HCl + N2 (8)

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

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Demonstration: SIMGEN for Tutoring

 Runs a simulation  answers queries  about the simulation

• „WHAT happens?“: values, active processes

 about the domain theory

• „WHY does it happen?“: preconditions

 Example: different cups containing a liquid under different conditions

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

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Ecological Modeling and Decision Support Systems Requirement: Context Independence

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

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Context-independent Models

 Compositional modeling   re-usable model fragments  re-usable in different contexts   context-independent model fragments   refer to local variables only  attributes of involved objects   state all preconditions  Otherwise: applied in wrong context   specify effect locally  “dN/dt = r·N” ???

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

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Effects Cannot be Stated by Derivatives

dN/dt = r*N

Population size Reproduction Death Immigration Emigration

?

Solution  ch. 3.3.3

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

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Modeling Assumptions

 Context-independent models  In the ideal sense: impossible  Complete preconditions?  Unstated negative preconditions:  “… and X must not be present”

Resources amount>0 Population density > d0 Accessible (Population, Resources) Reproduction Resources Population