Why would humans want to continue playing Tron? One mechanism we devised for
attracting ``web surfers'' and enticing them to keep coming back is our ``Hall
of Fame'' (fig. 3.14).
Figure 3.15 shows that we have consistently attracted groups of veteran players along with a steady stream of new participants. The presence of seasoned humans helps us get an accurate evaluation of agents, but novices were necessary, at least in the beginning, to discriminate between agents who could have lost all games against an expert. Figure 3.6 shows horizontal lines which represent some of the veterans, coming back again and again over long time spans. The more such players are present, the more accurate is the computation of RS indexes.
Is the human species getting better as well? No. Redoing the same exercise of
figure 3.12, but now tracing the strength level of all human players
considered as one entity, we obtain a wavy line that does not seem to be going
up nor down (fig. 3.16). This shows that, although individual humans
improve, new novices keep arising, and the overall performance of the species
has not changed over the period that Tron has been on-line.
An altogether different image emerges when we consider humans on an individual
basis. Although a large number of games are needed to observe significant learning,
there is an important group of users who have played 400 games or more. On average,
these humans raise from a performance of -2.4 on their first game, to -0.8
on their 400th game, improving approximately 1.5 points over 400 games (fig.
3.17). The learning rate is dramatically faster for humans, compared
to the approximately 100,000 games (against people) that our system needed to
achieve the same feat (fig. 3.12).
On fig. 3.18 we have plotted the learning curves of the 12 most
frequent players. Many of them keep learning after 1000 games and more, but
some plateau or become worse after some time.