This is going to be a short log, as I haven’t done much related to the thesis that is actually visible, most of it’s been discussions regarding implementation, and the rest of my time has been spent on other projects. I have made some progress on the game, though. It’s a very simple game, but still has enough complexity to serve as a boiled down simulation environment.
The game, shown on the left, is named “Dead End” and is written in Python. In it, the player controls a cat that needs to reach the exit (which, as I notice now, is not depicted in the picture, but it’s supposed to be an area in the top of the screen) to win. The opponents are a pack of dogs, which attempt to capture the cat, at which point the game ends. This, of course, is just a prototype intended to demonstrate how the game would work if it were really played, it is not intended to be fully robust or playable, but the aim is to make it “good enough” so that one can test it out with some real users at the end as a final dataset.
The game needs to exist both as a playable game and a simulation in the ADATE system, preferrably without having to rewrite any code. This would require a game “core” written in Standard ML, which is then called from the python code that handles the graphics and input system for the playable game. This would, ideally, make the game logic completely consistent between game and simulation, as anything related to how the characters move and behave is controlled by the shared Standard ML code. To me, this seems like the best way to go, unless it turns out that the game logic is sufficiently simple that writing the code to interface between python and Standard ML would actually end up being more work than maintaining two copies.
For the next week, my focus is going to be finishing up the game completely in Python, and start work on translating the core to Standard ML for the simulation.
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