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The Haunted Swamps of Heuristics: Uncertainty in Problem Solving

Artem Amirkhanov, Stefan Bruckner, Christoph Heinzl, M. Eduard Gröller

BOOK CHAPTER: In Scientific Visualization: Uncertainty, Multifield, Biomedical, and Scalable Visualization, pp. 51–60, 2014. DOI: 10.1007/978-1-4471-6497-5_5

Abstract

In scientific visualization the key task of research is the provision of insight into a problem. Finding the solution to a problem may be seen as finding a path through some rugged terrain which contains mountains, chasms, swamps, and few flatlands. This path - an algorithm discovered by the researcher - helps users to easily move around this unknown area. If this way is a wide road paved with stones it will be used for a long time by many travelers. However, a narrow footpath leading through deep forests and deadly swamps will attract only a few adventure seekers. There are many different paths with different levels of comfort, length, and stability, which are uncertain during the research process. Finding a systematic way to deal with this uncertainty can greatly assist the search for a safe path which is in our case the development of a suitable visualization algorithm for a specific problem. In this work we will analyze the sources of uncertainty in heuristically solving visualization problems and will propose directions to handle these uncertainties.

Published

Scientific Visualization: Uncertainty, Multifield, Biomedical, and Scalable Visualization

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BibTeX

@INCOLLECTION{Amirkhanov-2014-HSH,
  author = {Artem Amirkhanov and Stefan Bruckner and Christoph Heinzl and M.
	Eduard Gr{\"o}ller},
  title = {The Haunted Swamps of Heuristics: Uncertainty in Problem Solving},
  booktitle = {Scientific Visualization: Uncertainty, Multifield, Biomedical, and
	Scalable Visualization},
  publisher = {Springer},
  year = {2014},
  editor = {Min Chen and Hans Hagen and Charles D. Hansen and Christopher R.
	Johnson and Arie E. Kaufman},
  series = {Mathematics and Visualization},
  chapter = {5},
  pages = {51--60},
  month = sep,
  abstract = {In scientific visualization the key task of research is the provision
	of insight into a problem. Finding the solution to a problem may
	be seen as finding a path through some rugged terrain which contains
	mountains, chasms, swamps, and few flatlands. This path - an algorithm
	discovered by the researcher - helps users to easily move around
	this unknown area. If this way is a wide road paved with stones it
	will be used for a long time by many travelers. However, a narrow
	footpath leading through deep forests and deadly swamps will attract
	only a few adventure seekers. There are many different paths with
	different levels of comfort, length, and stability, which are uncertain
	during the research process. Finding a systematic way to deal with
	this uncertainty can greatly assist the search for a safe path which
	is in our case the development of a suitable visualization algorithm
	for a specific problem. In this work we will analyze the sources
	of uncertainty in heuristically solving visualization problems and
	will propose directions to handle these uncertainties.},
  doi = {10.1007/978-1-4471-6497-5_5},
  keywords = {uncertainty, heuristics, problem solving},
  owner = {bruckner},
  timestamp = {2014.12.30},
  url = {http://www.springer.com/mathematics/computational+science+%26+engineering/book/978-1-4471-6496-8}
}






 Last Modified: Stefan Bruckner, 2017-10-13