Week5 (Due 10/4)

CS 372 (Artificial Intelligence) Fall 2004

Week5 (Due 10/4) Reactions & Thoughts

Ioana Butoi

Andrew Cantino

The problem of robotically building maps is interesting, as is the task of using the maps once they are built. I'm fascinated by the use of probability and statistical techniques to generate maps. I would be very interested to find out more about this.

One technique that I learned about in another class was the kalman filter, which allowed the integration of actual data readings from sensors with a probabilistic model of the sensors' errors and of the world. Is this technique or similar techniques used on robots commonly?

I have a question about our brief discussion of configuration spaces: is it a general property of path searches like that to stick to the edges of objects, or was that just a property of the particular algorithm that we saw in class. The in-class demonstration found the borders of regions in the world where the robot could or could not fit, and then made paths based on connected nodes on these borders. This approach didn't seem to make use of all of the open, available space in the world (which would be presumably safer to traverse without bumping into anything.)

Audrey Flattes

Christina Florio

Kathleen Maffei

Sara McCullough

Ben Root

I was impressed with the flexibility and power of the search algorithms that were implemented in the search.py and searchqueues.py and eightpuzzlestate.py. The idea which underlies all of it is that you can isolate your solution to be a path between nodes on a graph. All we need is a representation now of our problem which can be fed into applyoperators() and then we can find a solution, even an optimal one.

A friend of mine is always betting on football and basketball games. I am hoping to use either a neural net or a neural net produced by a G.A. to beat him in picking winners. He does pretty well as it is, so if I can win a higher percentage than him I would have a lucrative program on my hands!

Sandeep Singh

A few weeks ago, Andrew and I were briefly talking about how a site like Mapquest is able to find a driving route from point A to point B. After this week’s discussions and demonstrations about searches, it has become more evident that tools like Mapquest most likely use some form of search tree. Seeing as how the directions provided by these services tend to be roundabout and often indirect, I think it’s safe to say that they implement a blind search in order to retrieve driving directions. Regardless of what search they may use, these services are very fast and providing directions and driving routes. I could be completely wrong about this assumption. It could be that they use some other technique that we have not yet discussed. Either way, I think such a program could be created using the search methods we have learned.

I also found the robot demonstration in which Ben and I were the hands, Andrew the eyes and Ioana the brain, to be very helpful in understanding the point that the programs we are to write should—and can not—be exact in their execution. In the lab exercise from the previous week, I had commented in my lab by saying that I had wanted to somehow use the angles of reflection to determine the degree by which the robot should turn. After this informative—yet comical—example, it has become clearer to me that using such methods is not the goal when it comes to artificial intelligence and programming intelligent agents.