  ActorSim is a goal reasoning framework developed at the Naval Research
Laboratory. Originally, all goal reasoning rules were hand-crafted. This work
extends ActorSim with the capability of learning by demonstration, that is,
when a human trainer disagrees with a decision made by the system, the trainer
can take over and show the system the correct decision. The learning component
uses Ripple-Down Rules (RDR) to build new decision rules to correctly handle
similar cases in the future. The system is demonstrated using the RoboCup
Rescue Agent Simulation, which simulates a city-wide disaster, requiring
emergency services, including fire, ambulance and police, to be dispatched to
different sites to evacuate civilians from dangerous situations. The RDRs are
implemented in a scripting language, FrameScript, which is used to mediate
between ActorSim and the agent simulator. Using Ripple-Down Rules, ActorSim can
scale to an order of magnitude more goals than the previous version.
