| Summary: | A key aspect of how we understand the world revolves around an ability to
manipulate our surroundings to experiment. From the scientific method through
theories of child development, the ability to experiment is deemed critical; however,
few studies have been performed to understand the strengths and weaknesses of
different experimental strategies.
This dissertation investigated the effectiveness of several different experimental
strategies when learning about an unknown system. An empirical study was
performed using binary functions as hypotheses and using computer programs to
model several different experimental strategies. These strategies were derived from
our definition of a normative experiment selector, which described how an idealized
experimenter should select experiments.
A detailed program of study was performed on these computer programs to
determine the strengths and weaknesses of the experimental strategies they implemented.
The number of experiments needed to determine a target theory from an
initial set of hypotheses was measured. Two key discoveries were made.
First, we discovered that simple experimental strategies were the most effective.
For example, the most effective strategy we discovered was experimental
relevance selecting any experiment guaranteeing elimination of at least a single
hypothesis from the set being considered. Complex strategies to determine the
optimal experiment to perform were very costly for a slight performance gain.
Second, we discovered that only two factors had any major effect on performance:
the number of experimental outcomes and the number of initial hypotheses
considered. The number of experiments available to the experiment selector had
little or no effect. Our best situations were where: (a) only a small number of
hypotheses were possible, (b) each experiment had a large number of outcomes,
and (c) relevant experiments were easy to determine and perform. === Graduation date: 1991
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