The Tron system was intended to function as one intelligent, learning opponent
to challenge humanity. The strategy of this virtual agent is generated by the
random mixture of Tron robots in the evolving population; 18% of the games
being played by new, untested agents, exploring new strategy space. The remaining
games are played by those agents considered the best so far -- survivors from
previous generations, exploiting previous knowledge. In terms of traditional
AI, the idea is to utilize the dynamics of evolution by selection of the fittest
as a way to create a mixture of experts that create one increasingly robust
Solving equation (3.9) for all of the computer's games put together yields the performance history of the whole system considered as a single playing entity. Fig. 3.12 shows that the system has been learning throughout the experiment, at the beginning performing at a RS rate below -2.0, and at the end around 0.
But the RS is an arbitrary scale: what does it mean in human terms? The next
graph re-scales the RS values in terms of the percent of humans below each value.
Beginning as a player in the lower 30 percent, as compared to humans,
the Tron system has improved dramatically: by the end of the period it is a
top 5% player (fig. 3.13).