Unit a2 1 causality
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Unit A2.1 Causality. Kenneth D. Forbus Qualitative Reasoning Group Northwestern University. Overview. What is causality? Design choices for causality in qualitative physics Using causality Example: Self-explanatory simulators. A qualitative physics view of causation.

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Unit A2.1 Causality

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Unit a2 1 causality

Unit A2.1 Causality

Kenneth D. Forbus

Qualitative Reasoning Group

Northwestern University


Overview

Overview

  • What is causality?

  • Design choices for causality in qualitative physics

  • Using causality

    • Example: Self-explanatory simulators


A qualitative physics view of causation

A qualitative physics view of causation

  • There are several broadly used notions of causality in reasoning about the physical world

  • They can be decomposed by several factors, including

    • Ontological assumptions: Is there a class of entities that act as mechanisms in the domain?

    • Measurement scenario: What sense of change is being discussed?


Measurement scenarios affect causality

Measurement Scenarios affect causality

Incremental

Cause precedes effect

Continuous

Cause, effect coextensive

Moving soup spoon causes

the napkin to wipe your face

Heat flow causes

heat of water to rise,

which causes

temperature of water

to rise


Implications for theories of causal reasoning

Implications for theories of causal reasoning

  • Consider the following:

    • Causes must precede effects in mechanistic situations, but causes are temporally coextensive in continuous causation.

    • Ontological assumptions used by human experts vary with domain

      • cf. use of processes versus components in thermodynamics versus electronics

         No single, simple account of causality is

        sufficient.

         “Gold standard” is psychology, not physics


Causality via propagation

Causality via Propagation

  • Source of causation is a perturbation or input (de Kleer & Brown, 1984)

  • Changes propagate through constraint laws

  • Useful in domains where number of physical process instances is very large


Mythical causality

Mythical Causality

  • What a system does between quasistatic states

    • Extremely short period of time within which incremental causality operates, even in continuous systems

    • Motivation: Capture intuitive explanations of experts about causality in continuous systems, without violating philosophical ideas such as “A Cause must precede its effect”


Implications of causality as propagation

Implications of causality as propagation

  • Identifies order of causality with order of computation.

  • No input  no causality

    • Quantitative analog: Simulators like SPICE require an order of computation to drive them.


Causality in qp theory forbus 1981 1984

Pressure(G)

Pressure(F)

Q+

Q+

Level(Wg)

Level(Wf)

Q+

Q+

Amount-of(Wg)

Amount-of(Wf)

F

G

Causality in QP theory(Forbus, 1981; 1984)

  • Sole Mechanism assumption: All causal changes stem from physical processes

  • Changes propagate from quantities directly influenced by processes through causal laws to indirectly influenced quantities

  • Naturally models human reasoning in many domains (i.e., fluids, heat, motion…)

I-

I+

Liquid FlowF  G


Implications of sole mechanism assumption

Implications of Sole Mechanism assumption

  • All natural changes must be traced back to the action of some physical process

    • If not so explained, either an agent is involved, or a closed-world assumption is incorrect

      • The scenario isn’t fully or accurately known

      • The reasoner’s process vocabulary is incomplete or incorrect

  • Syntactic enforcement: Direct influences only appear in descriptions of physical processes

  • Causal direction in qualitative relations crucial for ensuring correct causal explanations


How directional are causal laws

Answer: It depends

In some domains, clear causal direction across broad variety of situations

cf. engineering thermodynamics

In some domains, causal direction varies across broad variety of situations

cf. analog electronics

How directional are causal laws?

T =f(heat, mass, …)

V = I * R


Causal ordering

Used by H. Simon in economics in 1953

Inputs

Set of equations (quantitative or qualitative)

Subset of parameters identified as exogenous

Output

Directed graph of causal relationships

Method (informal)

Exogenous parameters comprise starting set of explained parameters

Find all equations that have exactly one parameter not yet explained.

Add causal links from explained parameters to the unexplained parameter

Add unexplained parameter to set of explained parameters

Continue until exhausted

Causal Ordering


Tradeoffs in causal ordering algorithm

Advantages

Can provide causal story for any set of equations

Assuming well-formed and enough exogenous parameters

Causal story can change dynamically if what is exogenous changes

Drawbacks

Poor choice of exogenous parameters can lead to psychologically implausible causal stories

e.g., “the increase in blood sodium goes up, which causes the blood volume to go up.”

Does not specify the sign of causal effect

Tradeoffs in causal ordering algorithm


Self explanatory simulators

Self-Explanatory Simulators

  • Idea: Integrate qualitative and numerical representations to achieve

    • Precision and speed of numerical simulation

    • Explanatory power of qualitative physics

  • Imagine

    • SimEarth with explanations

    • Interactive, active illustrations in textbooks

    • Training simulators with debriefing facilities

    • Virtual museum exhibits that you can seriously play with


How self explanatory simulators are built

Compiled Simulation

SIMGEN Compiler

Runtime

How self-explanatorysimulators are built

Students

DomainModeler

Domain

Theory

IDE &

Tools

Scenario

Support

Files

Curriculum

developer,

Teacher, or

student


Unit a2 1 causality

Compiling self-explanatory

simulators

Scenario

Domain

Theory

Qualitative

Analysis

Qualitative

Model

Code

Generator

Explanation

System

Code


How the explanation system works

How the explanation system works

  • Simulator keeps track of model fragment activity in a concise history

    • <MFi <start> <end> <T,F>>…

    •  At any time tick, can recover full activation structure

  • Causal questions answered by

    • Recovering influence graph from activation structure

    • Filtering results appropriately for audience

      • e.g., thermal conductivity not mentioned in Evaporation Laboratory

      • “Can’t say, don’t tell” policy


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