Conclusions

Conclusions	The Future

* Interacting with a real-world environment, some variables (ie opponent) are not under experimental control * Rating method found that uses all available information to obtain a maximum likelihood estimation * Good and bad found amongst both humans & robots * Tron Learning, as a system, shown * Human Learning shown in individual humans * Coevolving agents and humans is possible (also see TD-Leaf, Baxter et.al. 98)	* Use Statistical Rating as Fitness Function But: * Newton-Raphson involves inverting NxN matrix, a few times. So: * Glickman (1999) (Incremental) Parameter Estimation w/ Error Also: * Multi-Dimensional models? * Pareto?

The Future

* Interacting with a real-world environment, some variables (ie opponent) are not under experimental control

* Rating method found that uses all available information to obtain a maximum likelihood estimation

* Good and bad found amongst both humans & robots

* Tron Learning, as a system, shown

* Human Learning shown in individual humans

* Coevolving agents and humans is possible (also see TD-Leaf, Baxter et.al. 98)

* Use Statistical Rating as Fitness Function

But:
* Newton-Raphson involves inverting NxN matrix, a few times.
So:
* Glickman (1999) (Incremental) Parameter Estimation w/ Error

Also:

* Multi-Dimensional models?

* Pareto?

http://demo.cs.brandeis.edu/tron