Edited by author 04-18-2002 06:13 AM
This was an interesting paper. I like the way the authors simplified the problem by just wanting to keep the driving speed constant for each driver and by wanting to minimize the number of lane changes. Although I think the author correctly points out at the end that wanting constant speeds is a big assumption, given that people may want to alter their speeds, especially younger drivers. Also practically it does seem that poor traffic usually happens the most when encountering on and off ramps, and not because of drivers having differing desired speeds.

In the beginnning it also seems like this problem is formulated as an optimization problem (read: Figure 1) and not a learning problem. By that I mean early he phrases the problem as though there is 'one' best arrangment of cars and lanes that all the drivers should strive for.

I think there is an absence of details in the paper about the learning algorithms. Each is described at a high level but detail is omitted, which is bad. Also it seemed as though the system was hacked together, employing many different learning strategies and there really wasn't a central idea other than that ai can be applied to this problem. Is he using reinforcement learning, ANN's, local search, intelligent heuristics? It seems like a hodgepodge of learning algorithms.

However he does get good performance, which is promising. Also though this paper seems a bit dated, with no reference being later than 1997. I know this problem is currently being looked at in EE/ECE depts, but I don't think ML people have touched it in a while. Why is that?