The title will be announced during or at the end of the talk

The Haunted Swamps of Heuristics

Eduard Gröller

Institute of Computer Graphics and Algorithms

Vienna University of Technology

Eduard Gröller 3

Problem Solving ↔ Path Finding

A

B

High ground of theory ↔ Haunted swamps of heuristics

The

Haunted

Swamps

of

Heuristics

negative

negative

Reviewer comments

negative

neutral,positive

„ ... only heuristics ...“

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„ ... lots of parameter tweaking ...“„ ... too many heuristic choices ...“

„ ... ad hoc parameter specification ...“

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Heuristics

HeuristicsGreek: "Εὑρίσκω", "find" or "discover“Experience-based techniques for problem solving, learning, and discoveryFinding a good enough solution

ExamplesTrial and ErrorDraw a pictureAssume a solution and work backwardAbstract problem → examine concrete exampleSolve a more general problem first

[Wikipedia, 2011]

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Objects of Desire in Science

Focus objects of scientific interest

Data

Artefacts, fossils, mummies

Algorithms

Ötzi the Iceman[Wikipedia, 2011]

Multipath CPR[Roos et al., 2007]

Dual Energy CT[Heinzl et al., 2009]

Our community is really fond of algorithms

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Objects of Desire – Algorithms

Algorithm: set of instructions + constants + variables

And then there are:

Parameters: auxiliary measures (greek)

Constraints, boundary conditions, approximations, calibrations

Whatever does not work

Parameters often specified heuristically

Problem solving: algorithm + parameters

parameters

encoded in parameters

encoded in parameters

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Heuristic Parameter Specification - Examples

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Context-Preserving Rendering (1)

[al. et Gröller, 2006]

Eduard Gröller

Context-Preserving Rendering (2)

Integrate various focus+context approaches with only few parameters

ts

11

User-Defined Parameters

0.4s

0.8s

1.5t 3.0t 4.5t 6.0t

Eduard Gröller

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Heuristic Parameter Specification

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Statistical Transfer-Function Spaces

[al. et Gröller, 2010]

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Problem Solving: Algorithm + Parameters

Parameter space analysisRobustness, stability: well established in other disciplines Increased interest in visualization

VariationsEsemblesKnowledge-assisted visualization

data imagealgorithmparameters

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Dynamical Systems – Parameter Space

Mandelbrot set: parameter space for Julia sets

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Parameter Space Analysis in Visualization

Uncertainty-Aware Exploration of Continuous Parameter SpacesSurrogate models ≈ euphemism for heuristics

[Berger, Piringer et al., 2011]

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Parameter Space Analysis in Visualization

World LinesFlood emergency assistanceTesting breach closure procedures Steer multiple, related simulation runsTest alternative decisionsAnalyze and compare multi-runs

[Waser et al., 2010]

Video

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Problem Solving: Algorithm + Parameters

ExamplesExploration of Continuous Parameter SpacesWorld Lines

Visualization algorithms?? Parameter variation for computational steering

data image

Context-Preserving Rendering[al. et Gröller, 2006][Bruckner et al., 2006]

t

s

GradMagnMod

Com

posi

ting

Stefan Bruckner, Eduard Gröller

Maximum Intensity Difference Accumulation

Blending of MIP and DVR [Bruckner et al. 2009]

DVR

MIP

MIDA

βi = 1- fi – fmaxi if fi > fmaxi

0 otherwise

Ai = Ai-1 + (1 - Ai-1)αi

Ci = Ci-1 + (1 - Ai-1)αici

Ai = βiAi-1 + (1 - βiAi-1)αi

Ci = βiCi-1 + (1 - βiAi-1)αici

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Problem Solving: Algorithm + Parameters

algorithm + parameters „solution cloud“

algo

par

data image

Algorithms and parameters closely intertwinedParameters deserve much more attentionHeuristics ok, but do sensitivity analysis

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Algo., Parms., Heuristics – Quo Vadis? (1)

Problem solving in visualizationAlgorithmic centric → data/image centricImperative → declarative approachesFrameless rendering → algorithmless renderingProgram verification → image verification→ Algorithms on demand→ Each pixel/voxel gets its own algorithm

Integrated views/interaction

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Algo., Parms., Heuristics – Quo Vadis? (2)

Problem solving in visualizationInteraction sensitivityComparative visualizationTopological analysis of parameter spacesInterval arithmetics → distribution arithmetics in visualization (uncertainty visualization)Publishing in visualization

More stability/robustness analyses in future?Executable Paper Grand Challenge

Semantic Layers for Illustrative Volume Rendering

[al. et Gröller, 2007]

„... the work is a significant step backwards ...“

[anonymous reviewer]

Curvature Based Selective Style Application

Semantic Layers for Illustrative Volume Rendering

Mapping volumetric attributes to visual stylesUse natural language of domain expert (rules)Rules evaluated with fuzzy logic arithmetics

[Rautek et al., 2007]

Fuzzy Logic as a Black Box

attribute semanticsa …a

style semanticss …s

rule base

fuzzylogic

1 n

evaluate attributes a …a per voxel

1 n

1 m

parameters for styles s …s

1 m

Semantics Driven Illustrative Rendering

Video

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Problem Solving ↔ Path Finding

A

B

High ground of theory ↔ Haunted swamps of heuristics

Heuristics are great, BUT, Handle with care

Doubt is not a pleasant condition, but certainty is absurd. [Voltaire]

The title has been announced at the beginning of the talk

Thanks for your attentionQuestions ?