## Flexible Policy Construction by Information Refinement

### Michael C. Horsch

**Dissertation Abstract**
Decision making under uncertainty addresses the problem of deciding
what actions to take in the world, when there is uncertainty about the
state of the world, and uncertainty as to the outcome of these
actions. A rational approach to making good choices is the principle
of maximum expected utility: the decision maker should act so as
to maximize the expected benefits of the possible outcomes.

The ``textbook'' approaches to decision analysis typically make the
assumption that the computational costs involved in are negligible.
This assumption is not always appropriate. When computational costs
cannot be ignored, a decision maker must be able to choose a trade-off
between computational costs and object value.

This thesis proposes an approach to decision making called information
refinement. It is an iterative, heuristic process which a decision
maker can use to build a policy. We present three algorithms which use
information refinement to construct policies for decision problems
expressed as influence diagrams. The algorithms are intended for
situations in which computational costs are not negligible, and are
designed to give the decision maker control of the trade-off involved
in the decision making process.

The first algorithm is an anytime algorithm for single stage decision
problems. It constructs a policy by increasing the use of information
available to the decision maker. The second algorithm applies the
single stage algorithm to multi-stage decision problems using a fixed
allocation of computational resources. The third algorithm is an
anytime algorithm for multi-stage decision problems.

We show empirically that these algorithms are able to make decisions
with high expected value with small computational costs. We provide
empirically evidence for our claims, by applying our algorithms to a
large number of large decision problems.

My
dissertation is available in compressed PostScript.

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