Developed or found in the wxpython demo  of the custom controls required for Behavior Shop 1.0
Logging Windows
Custom Panels with drop down boxes and linked help buttons
Better GDI+ drawing and box moving systems
Tree Controls with a triangle graphic rather than the plus/minus graphic

Implemented 3D space filling volumes coding the adaptive part tonight and tomorrow night – I am amazed at how compact a python implementation of ASFV is turning out to be compared to a full C# or C++ implementation.

Gave Keith a hand on some math problems with the level editor

Sci :

Read an interesting paper on 3D motion planning located here

Polished my design of ASFV 3D and starting formulating my introduction for the AIIDE paper and gathering related work.

Thought up another advantage that using ASFV over more traditional triangle/vertex based decomposition metrics.  DEACCON and tools derived from it should be better able to adapt to future 3D simulation and game environments than  vertex based approaches.  This occurs because DEACCON run times are controlled by the sizes of the worlds to be decomposed which is somewhat constant, while Vertex based approaches are run time limited by how complex the geometry present in the world is, which is growing exponentailly more complex as graphics hardware advances.

-Produced final version of poster for AIIDE talk.

-Upgraded study site to include progress bar.

-Massive Python Coding

- Cleared bugs and polished the interface for the agent builder.
- Successfully generated agent profiles using builder and succesfully passed them into agent core for execution.
- Set up base code for insertion of more behaviors.
- Implemented “Goto” behavior

DEACCON work
Hooked up rendering code to interface/decomposition
Implemented adding of positive space via clicking
Discovered massive bugs with the vector class I was using, I was forced to re-implement half of it to correct the bugs (if using vector class from gig2008/hale repo I recommend grabbing the new one I just placed in there. Many functions did not work as expected.
Fixed collision over run bugs on negative space regions.
Fixed improper collision detection with the positive space regions
Debugged many small problems with python
Fixed a nasty pass by value reference bug in generating negative space regions
Tracked down why the normals weren’t generating correctly(bad vector class)
Rewrote my old edge intersection algorithm with a better one I cribbed from computational geometry book.
Still working on issues with 2d/3d growth in python

Science

-Generated basic schema to define the output precisely from the agent builder
-Extended UML schema to allow for the exporting of additional knowledge from the Agent Builder – Implemented extension
Information from AIIDE that needs a followup
-Tactical agent personality inter agent behavior simulation
-Offline planning and hierarchial tasks networks in vido games
- Lightweight procedualr animation with believable physics – Ian Horswell
- Intellegent trading agents in Massive Games
- Agent Learning in RPG’s
- Knowledge Based Software Engineering Conference – my ontology building study is very like the automated generation of software requirements.

Random Stuff that comsumed some portion of time

-Demo’ed for Explore UNCC saturday
-Presented DEACCON on wednesday to playground
- Spent an entire day traveling + prep work

I found the use case they presented to be very interesting, they use the dynamic interface generation to create interfaces that have been adapted to specific users motor skill impairments.  The algorithm works almost exactly the same as in normal inteface generation except that instead of testing various inteface configurations to see which ones the user prefers it is looking at which set of interfaces the user has the easiest time interacting with.  This allows it to build a custom set of interface tools that optimize on the level of motor control the user possess.  Both there statistical tests and there ancedotal evaluations presented very good results.

Bib:

Decision-Theoretic User Interface Generation
Krzysztof Z. Gajos and Daniel S.Weld and Jacob O.Wobbrock
Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence (2008)

1986 IEEE

Brooks presented the initial research on the subsumption architecture here. Subsumption was originally designed to provide several benifits to the programers of mobile robots. It is supposed to allow the user to design modular reusable components, handle multiple sensor input, and deal with possibly inaccurate sensor results. The utility of subsumption was demonstraighted with the construction of both a physical agent capable of navigating a dynamic enviroment as well as a software simulation. The initial robot was only programed for navigation but additional tasks were planned to further show off the capabilities of subsumption. Subsumption was also shown to be very reusable and able to overcome noisy sensor data.

Link: A Robust Layered Control System for a Mobile Robot – PDF

Bibtex:

@article{basicSubsump,

Author = {Rodney Brooks},

Date-Modified = {2008-04-07 17:18:04 -0400},

Journal = {IEEE JOURNAL OF ROBOTICS AND AUTOMATION},

Number = {1},

Pages = {14–23},

Publisher = {IEEE},

Title = {A Robust Layered Control System for a Mobile Robot},

Year = {1986}}

Summary This paper presents a method of developing a landmark based navigation method for robotic agents. The agents are programmed with a subsumption architecture capable of encoding information about things they have seen with there sensors to form a self improving navigation map. This development of a navigation map is preformed in real time using just the robots sensor systems. This was the first subsumption architecture to allow for spacial planning while remaining fully reactive and one of the first to implement the behavior controls.

*** I am not sure if this is the paper that presents the behavior based system fully. They seem to gloss over the implementation of the behavior system.

*** The agents generated by this system seem to have something of sense of place which might make them something interesting to look into for the DEACCON work.

Link:Integration of Representation Into Goal-Driven Behavior-Based Robots – PDF

Bibtex:

@article{bControl,

Author = {Maja J Mataric},

Date-Modified = {2008-04-07 17:18:04 -0400},

Journal = {IEEE Transactions on Robotics and Automation},

Number = {3},

Pages = {304–312},

Publisher = {IEEE},

Title = {Integration of Representation Into Goal-Driven Behavior-Based Robots},

Year = {1992}}

This report is a well written and extremely thought out examination of the ethics and responsibilities of researchers who want to do work with human subjects and what standards they should be held to. The basic principles can primarily be condensed into 3 concepts: First being that there needs to be respect for the human test subjects. They need to be clear and true volunteers who have as full an understanding of the research they will be participating in as possible. Next the beneficence of the work needs to be examined. The risks presented to the study participants need to be minimized down as far as possible and the benefits to the participant or society need to be maximized. Finally the there is the question of Justice. The application of the research should be spread as far as possible and not limited to small groups and the actual performance of human trials should not be restricted to any small groups unless so dictated by the research itself.  Personally I found the first two principles to be kinda obvious however the third principle was moderately thought provoking.

Link: Official Site   Want to convert to pdf before making local copy

Bibtex: Coming