Abstract
Decision making is something with which we are all familiar. Each day
we find ourselves in situations where we have more than one
alternative to choose from. Most of our everyday decisions require
little or no reflection. Yet, other decisions are more complex and
may require thorough analysis. The field of decision analysis studies
the application of decision theory to actual decision problems.
Decision theory is the mathematical foundation for decision making,
which combines probability theory and utility theory in order to
describe what constitutes a rational decision. Today there are
numerous tools for decision analysis that run on ordinary PCs. These
make it easier for human decision makers to structure a decision
problem, and help visualize the effects of possible outcomes.
However, not only humans are decision makers. Intelligent agents are
also faced with decision problems. This thesis investigates if any of
the computerized tools for decision analysis can be used by
intelligent agents. Special attention is given to real-time domains,
where data that decisions depend on vary rapidly. These domains
require quick responses from the tools. Otherwise the result of a
decision analysis will be based on data that is no longer accurate.
The objective of the thesis is to provide results indicating if any of
the available tools for decision analysis are fast enough for use in
real-time domains.
Full paper: PDF, PS (51 pages, 25 references)
Presentation: PDF (11 slides)
Citings
Eva Jereb, Uros Rajkovic, and Vladislav Rajkovic. 2005. A hierarchical multi-attribute system approach to personnel selection. International Journal of Selection and Assessment 13(3):198–205.
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