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6:33pm on Tuesday, 13th November, 2018:
It's MSc project-selection time. All members of staff have to come up with half a dozen project ideas, from which students select the one they want to do for their dissertation. They approach several members of staff, find out what's involved, then if they like the idea and the member of staff is OK with them doing it, they're signed up. Members of staff almost always like the idea that the student takes on the project, because the alternative is to have to supervise students doing someone else's projects. If they don't accept someone, it's generally because they've taken on their quota of students already (although I also reject students I've supervised for their final-year project, on the grounds that they need a different perspective this time round).
These are the projects I proposed this year. Six students so far have approached me, but they've only chosen two of the projects. See if you can guess which two projects. Bonus: see if you can guess which project I've proposed for the past five years that no-one has ever wanted to do.
Random Monster Generation
This is a games-based AI project. Tabletop role-playing games typically come with monster manuals, describing the various adversaries that players can encounter. This project involves using as many of these descriptions as you can find (at least a hundred, ideally more) and using them as data for generating new monsters. The descriptions should make semantic sense, if not gameplay sense. Note: you'll have to obtain digital copies of monster descriptions yourself, they're not provided.
Player Types Test
Some 850,000 people had taken the famous-but-flawed "Bartle Test" when the web site it was on closed down. The objective of this project is to create a new Player Types test that better fits the theory. So, that's basically a web interface to a database plus a knowledge of Player Types. I can supply the latter...
This project concerns the refinement and development of a fashion mechanic for use in massively-multiplayer games. In general, items subject to fashion (such as clothes) can be described in terms of a number of independent dimensions (material, length, colour, pattern, cut, ...). Each dimension takes its possible values from a fixed set (colour: red, green, blue, black, white, ...). These values can be ordered by current fashionability. The more items that exhibit a particular value, the more fashionable they are and the more they will sell. Exception: the most popular value is not fashionable at all. Consumers (whom the game would simulate) select items based on the sum of the fashionability of their components. Producers (players) don't know the individual fashionability values, but they do know how
combinations currently translate into sales. Players should aim to create items using values that are becoming popular without being so popular as to have lost fashionability. To ascertain the viability of your implementation, it must be tested by simulating at least 10,000 players over at least 20 iterations.
This is a big data project. The idea is to select papers from a field with which your supervisor is familiar (computer games, AI, in my case) and collect as many journal papers, conference papers and book chapters as possible (at least a hundred, preferably more) then run a program that you write to extract the references from these papers. For each reference, you determine whether the first author is male, female or indeterminate. You do the same for each paper. You then compare the percentage of papers written by a particular gender (say, women) with the percentage of citations of articles written by people of the same gender. Is it roughly the same, or do researchers tend to cite papers disproportionately by author's gender. Do this right and there's a press release in it..!
This project has wide scope because it offers the opportunity for students with definite ideas of their own regarding what they want to do to pitch them to me. I'm willing to consider anything involving the making of a game with the addition of an AI technique that in some way improves the player experience. It is advised that you discuss your idea with me before you select this option, so you can switch to something else if I don't think your grand scheme is as good as you do.
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