Regarding Chess agents, Vanessa pointed out that while only perfect play is optimal, informally we would consider agents to have an objective that is better served by slightly better play, for example, an agent rated 2500 ELO is better than one rated 1800, which is better than one rated 1000, etc. That means that lots of “chess minds” which are non-optimal are still somewhat rational at their goal.
I think that it’s very likely that even according to this looser definition, almost all chess moves, and therefore almost all “possible” chess bots, fail to do much to accomplish the goal. We could check this informally by evaluating the set of possible moves in normal games would be classified as blunders, using a method such as the one used here to evaluate what proportion of actual moves made by players are blunders. Figure 1 there implies that in positions with many legal moves, a larger proportion are blunders—but this is looking at the empirical blunder rate by those good enough to be playing ranked chess. Another method would be to look at a bot that actually implements “pick a random legal move”—namely Brutus RND. It has an ELO of 255 when ranked against other amateur chess bots, and wins only occasionally against some of the worst bots; it seems hard to figure out from that what proportion of moves are good, but it’s evidently a fairly small proportion.
Regarding Chess agents, Vanessa pointed out that while only perfect play is optimal, informally we would consider agents to have an objective that is better served by slightly better play, for example, an agent rated 2500 ELO is better than one rated 1800, which is better than one rated 1000, etc. That means that lots of “chess minds” which are non-optimal are still somewhat rational at their goal.
I think that it’s very likely that even according to this looser definition, almost all chess moves, and therefore almost all “possible” chess bots, fail to do much to accomplish the goal.
We could check this informally by evaluating the set of possible moves in normal games would be classified as blunders, using a method such as the one used here to evaluate what proportion of actual moves made by players are blunders. Figure 1 there implies that in positions with many legal moves, a larger proportion are blunders—but this is looking at the empirical blunder rate by those good enough to be playing ranked chess. Another method would be to look at a bot that actually implements “pick a random legal move”—namely Brutus RND. It has an ELO of 255 when ranked against other amateur chess bots, and wins only occasionally against some of the worst bots; it seems hard to figure out from that what proportion of moves are good, but it’s evidently a fairly small proportion.