Human-Centric Goal Reasoning with Ripple-Down Rules
<|reference_start|>Human-Centric Goal Reasoning with Ripple-Down Rules: 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.<|reference_end|>
@article{brameld2024human-centric,
    title={Human-Centric Goal Reasoning with Ripple-Down Rules},
    author={Kenji Brameld, Germ'an Castro, Claude Sammut, Mark Roberts, David W.
  Aha},
    journal={arXiv preprint arXiv:2402.10224},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.10224},
    primaryClass={cs.RO cs.AI cs.MA}
}