Science:
Ran 6 external people through the study for the guard scenario.
Begin compiling formal results for that study.
Did design work on Behavior Shop to better incorporate the feedback we received.
Cleaned up my scenario 1 report.
Engineering:
Worked on pyDEACCON – saturday & sunday
- Negative spaces were able to grow into other negative or positive spaces if they had perfectly colinear edges – fixed
- Implemented better perfect quality seeding
- Converted pyDEACCON to use MVC model, and the DEACCON portion is fully encapsulated into “model”.
- Changes the view in pyDEACCON to use FI3RST.
- Loading and rendering of arbatrary geometry is now supported in FI3RST
- Continually updating and redrawing of pyDEACCON is now supported in FI3RST
- pyDEACCON is now threaded off from its rendering component – This allows the system to render faster than pyDEACCON can grow and allows detailed views of the growth procces
- Implemented a “vertical spreading” of pyDEACCON regions so it possible to clearly see where one region stops and another stops
- Implemented better growth control algorthim to ensure colinearity.
- Completely fixed the problem where growing regions would “catch” on a neighboring region and incorrectly stop growing.
Challenges:
Getting panda to redraw the updated decomposition
Some minor thread safety issues caused by threading off the decomposition
Getting people in here to finish up my study participant quota
A panda error message when attempting to draw triangle fans that was exactly wrong (“# vertices supplied >= max_support_vertices”) when in fact I was passing in less than three vertices.
Plans:
Finish writing up Behavior Shop 1.0 and Start Implementation
Develop video for GRF
Summary / Long Term Update :
Science:
The DEACCON work was voted most influencial academic game AI paper of 2008 by AIGameDev.com
Developed and implemented an algorithm to dynamically repair invalidated spatial decompositions to allow for more dynamic game worlds
Developed a series of metrics for the evaluation of spatial decomposition
Developed theory for and have started implementing 3D spatial decomposition technique. The critical part of a 3D spatial decomposition is that the decomposition be aware of the affordances provided by the environment. Stairs for example require a decomposition such that the area above each stair is a tall enough column to fit an entire agent into. A naive approach to 3D decomposition would result in regions seeding from the center of each plane and growing from there. However, such an approach will result in major problems for our stair example many of the stairs will have small unusable regions on them while a few stairs will have enormous oversize regions that extend out over open space. The fix for this is to use a gravaty based seeding that drops every new region to contact with the ground which ensures it initially begins growth by following the ground plane.
Engineering
Implemented the alpha version of BehaviorShop. After initial internal trials it was decided a more natural language approach was needed and we decided to go with a sentance based behavior structure. Users build behaviors layers by filling in the blanks of a sentance describing the behavior. This approach was found more natural and enjoyable by our users. During human trials using this method users were heard subconsciously reading off the sentances that described the behaviors they were constructing, which indicates they were quite comfortable with how behaviors were built. Human testing on behavior shop produced an interesting list of lessions learned (stored in GIG2008/Documents/BehaviorShopFirstHumanTrialInitialResults.doc) to be incorperated into BehaviorShop 1.0. The demographics and fully compiled results from this study will be stored in GIG2008/Documents/BehaviorShopFirstHumanTrialFinalData.doc.
Implemented several lighting and rendering technology upgrades for use in FI3RST.
