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Masters Thesis Testing: Testing & Reviews : Jenn Sandercock

Welcome to my Masters thesis testing page...

My masters thesis aimed to create characters that adapted, used context and were individuals (i.e. different to the other characters even when their initial personality templates were different). In order to test these three areas I looked at the choices the characters made (i.e. what they did), how well they thought they were achieving their personal goals and I developed a quantitative measure for individuality. Eventually the theory that you can develop "different" characters using my techniques would need to be tested using human participants and asking them whether they notice differences. As a preliminary step before this, a quantitative measure determines whether their are any statistically significant differences between the characters. If the characters have no statistical differences, then it is very unlikely that humans would notice the difference. This means that the quantitative test needs to be passed prior to testing with human participants, i.e. there was no human player in the tests performed.

The quantitative measure for individuality that I developed is based on paired t-tests that examine the differences in number of times characters choose each possible plan. For more information about my masters thesis, please look at my masters thesis overview page.

Behaviours of two sample characters

Behaviours of two sample characters according to the top-level choices they can make.

Behaviours of two sample characters

Behaviours of two sample characters according to the top-level choices they can make with different starting conditions.

Learning sub-goals

Behaviours of a single character to show that it can learn specific goals such as "move towards a friend".

Learning about contexts

Characters learned based on their context which came from their perceived achievement of their personal goals. In this image we are looking at the same character in two different contexts and can see that they prefer different actions/plans in different contexts. This difference was learnt automatically by the character and not pre-programmed.