Human-Centric Goal Reasoning with Ripple-Down Rules

Kenji Brameld, Germán Castro, Claude Sammut, Mark Roberts, David W. Aha
1/30/2024
cs.ROcs.AIcs.MA

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Abstract

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.

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Cite this paper

@article{brameld2024humancentric,
  title  = {Human-Centric Goal Reasoning with Ripple-Down Rules},
  author = {Kenji Brameld and Germán Castro and Claude Sammut and Mark Roberts and David W. Aha},
  year   = {2024},
  eprint = {2402.10224},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2402.10224v1}
}

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