Robot hackers have long realized that human observers tend to over-attribute intelligence, or at least intentionality, to robots, at least provided that they’re moving.  (Dave) Miller’s law states that the perceived intelligence of a robot is directly proportional to its velocity (Dave didn’t name this Miller’s law, but he said it once at a workshop and I’m fond of quoting it).

The image above is a screenshot of what appears to be two child characters playing with one another while being watched by an adult.  However, in actuality, what I’ve been implementing is attachment behavior, which is the response of children to stress by seeking out their caregiver (more on this another day).  To implement that, I need to have something to stress the kids out.  The right way to do it is to implement a real social engagement system with wariness and coy behaviors, play, turn-taking, etc.  However, the first step in that is simply to make a second child and then hack the children’s appraisal systems to assign negative valence to strangers (i.e. to each other).  All that does is make the kids watch one another and keep their distance from one another.  For example, one won’t approach the ball if the other is too close to it.  There’s no real sociality going on there.

The interesting thing is that it’s enough to make them look like they’re playing.  They both run to the ball, but then when one gets to close to it, the other backs off.  The first one will kick it until it happens to kick it toward the other one (which is pretty frequent since I haven’t implemented aiming).  At that point, the first one stays away from the ball and the second one plays with it.  This continues until they get far enough from the parent to engage the attachment system, at which point the attached child runs to the parent and hugs him/her, then runs back to play.

The point of this isn’t that this is a good simulation of anything, just that surprisingly simple behavior can appear engaging and intelligent, provided that whatever behavior you do have is relatively fluent.