Difference between revisions of "Thread:Talk:DrussGT/Head-to-head/reply (14)"
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But it's also the case that a piece of data that's 100x older but 10x closer to the current situation (wrt the rest of the attributes) is a better estimate of where the enemy is firing in that situation. The ratio of those values is going to depend on how granular the enemy's gun is. So I think considering the situations sorted by time at a bunch of different granularities is a pretty good bet, and I think it's similar to what our VCS systems do with great success. | But it's also the case that a piece of data that's 100x older but 10x closer to the current situation (wrt the rest of the attributes) is a better estimate of where the enemy is firing in that situation. The ratio of those values is going to depend on how granular the enemy's gun is. So I think considering the situations sorted by time at a bunch of different granularities is a pretty good bet, and I think it's similar to what our VCS systems do with great success. | ||
| − | You have got me thinking about super-lightweight flatteners against weaker learning guns though. =) It does bother me that they're learning all the time and I'm not! | + | You have got me thinking again about super-lightweight flatteners against weaker learning guns though. =) It does bother me that they're learning all the time and I'm not! |
Revision as of 15:39, 13 September 2011
But it's also the case that a piece of data that's 100x older but 10x closer to the current situation (wrt the rest of the attributes) is a better estimate of where the enemy is firing in that situation. The ratio of those values is going to depend on how granular the enemy's gun is. So I think considering the situations sorted by time at a bunch of different granularities is a pretty good bet, and I think it's similar to what our VCS systems do with great success.
You have got me thinking again about super-lightweight flatteners against weaker learning guns though. =) It does bother me that they're learning all the time and I'm not!
