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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. Problem Solving ↔ Path Finding. A. B. - PowerPoint PPT Presentation
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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 ...“
Eduard Gröller 4
„ ... lots of parameter tweaking ...“„ ... too many heuristic choices ...“
„ ... ad hoc parameter specification ...“
Eduard Gröller 5
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]
Eduard Gröller 6
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
Eduard Gröller 7
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
Eduard Gröller 8
Heuristic Parameter Specification - Examples
Eduard Gröller 9
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
Eduard Gröller 12
Heuristic Parameter Specification
Eduard Gröller 13
Statistical Transfer-Function Spaces
[al. et Gröller, 2010]
Eduard Gröller 14
Problem Solving: Algorithm + Parameters
Parameter space analysisRobustness, stability: well established in other disciplines Increased interest in visualization
VariationsEsemblesKnowledge-assisted visualization
data imagealgorithmparameters
Eduard Gröller 15
Dynamical Systems – Parameter Space
Mandelbrot set: parameter space for Julia sets
Eduard Gröller 16
Parameter Space Analysis in Visualization
Uncertainty-Aware Exploration of Continuous Parameter SpacesSurrogate models ≈ euphemism for heuristics
[Berger, Piringer et al., 2011]
Eduard Gröller 17
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
Eduard Gröller 18
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
Eduard Gröller 21
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
Eduard Gröller 22
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
Eduard Gröller 23
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
Eduard Gröller 29
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 ?