Let's take a walking stat system for example-
For a player what is needed is:
Acceleration- determines how quickly directions are changed and speed is gained.
Speed- is translation speed.
Overall Balance- determines the coordination in successful movement.
Current Location- is the player's coordinates.
For A.I. what is needed is:
Positive Result Ranking List- is a que of positive results. Appended based on rumors. Also appended based on experience.
Positive Result- is the goal of the A.I.
New Goal- is the next goal in the que.
Alternative Location List- is a list of locations to cycle through.
Current Location Attraction Level- determines how much the current goal is worth. The A.I. will follow the best path.
New Location Attraction Level- is the factor that determines if an alternate location will override current location.
Undesired Effects List- is a list of negative things an A.I. associates a path with.
Desired Effect List- is a list of positive things an A.I. associates a path with.
Desired Location- is the place where A.I. thinks goals complete.
Desired Location Value- is the chance of success in the mind of A.I.
Negative Path’s Rumor List- is a list of paths likely to fail. Based on third party information.
Positive Path’s Rumor List- is a list of paths likely to fail. Based on third party information.
Path Has Association- is a comparison of positive effects list subtracted by negative effects list. Each item as a value of one or zero.
Reaction Speed- is how quickly paths are processed.
Complete Path Recognition List- determines the chance of A.I. mistaking a path they discover. Some paths are assigned low mistaking chance value. This would be the same thing as a skilled player.
Half Path Recognition List- determines if an A.I. recognizes a learned path. Some paths are assigned a 45% mistaking chance value.
Unrecognizable Path List- determines if a path is new to an A.I.. A path is assigned to this list after A.I. hears or sees it and decides not to follow the path.
This is all that is required to simulate walking in a game.