Marker-augmented robot-environment interaction

There has been an increasing interest in developing computational theories of autonomous robots. However, previous work has dwelled on intelligent modifications to internal computational structures of robots, ignoring modifications to external environments. We formalize the modification of an environment by externalizing internal state. We argue that externalizing internal state by addition of markers increases communication through the world and that stronger markers have a higher potential to externalize internal state. We also show that markers can be used to maintain task fulfilling capability of a robot constant even when stimuli are specialized or effects of actions are made weaker. We argue that markers can be useful in composing more complex reactive behavior and show how such markers can be synthesized. Our results apply to all kinds of autonomous agents (robots as well as softbots) that are intended to use minimal internal representations