Quote:
Originally Posted by nathanww
Team 1678 has done some research on the kind of "operator as a data source" model that kamocat is describing, as well as a system for autonomous/hybrid action planning(hybrid refers generically to recieiving basic game information from an operator, not "hybrid mode"). Essentially the planning system consits of a component that "abstracts" input from sensors and/or an operator, "promotors" which respond to certain abstract conditions, and "payloads" which are instruction sets attached to the promotors which actually contain the robot's response to a certain condition. By altering "weighting factors" attached to the payloads and promotors, the overall tactics of the robot can be changed without needing to change the content of payloads(i.e.A robot can be made more agressive or more defensive by changing parameters that correspond to when agressive and defensive modes activate). In simulation(and potentially on an actual robot during the build season) a genetic algorythm can also be applied to the parameters to allow for machine learning. |
Neat!
I'll probably end up with some sort of weighting system, but it might use data that's a combination of the inputs. (Say, a ratio of gamepieces to robots.)
I like the idea of the weighting jitter, and the continuous scale from aggressive to defensive.