We had two final project presentations today for CIS 730 (Artificial Intelligence). Both were among the five who worked on the Roguelike game Angband. Our goal was to look at specific behaviors and improve them.

The first student, Dave Lupo, wanted to improve the tendency of the BenBorg (by Ben Harrison) to be a shopaholic. He trained a feedforward artificial neural net (ANN) using backpropagation to compute a better "dive motivator". This lowered the ratio of time in town vs. dungeon, and he found that increasing the ratio of "time in the dungeon" to "time in town" increased survivability.

Dave plotted the "time in town vs. time in dungeon" curve for 13 characters before his improved dive function, and 14 characters after, and found that they did have higher XP-to-move ratios. He speculated that they had higher survivability as a result, though these results were inconclusive. (I suggested that he look at the slope of the line to see if ' it really improved survivability.)

Now, here's the funny part. The points were all at time of character death, because he lost most of the characters at low levels, but I was sure he didn't lose them all by level 14, so I asked him what the rightmost point was. "Oh, that's time of death after 150000 turns". I asked, "what do you mean, after 150K turns?" He replied that to impose a time limit, he didn't just end the borg run at 150K; he sets "target level = 99" so that it essentially goes: "Morgoth... I'm comin' to get you!" and commences a Rambo-esque death dive!

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Banazir

I must say, things are looking up.

( Fresh versions of AI and Database Courses )
( On the new and previous crop of students )
( On the Gates )

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Banazir