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Research map / Multi-agent systems and games / Human-AI and goal-conditioned

Information Shaping for Enhanced Goal Recognition of Partially-Informed Agents

AAAI 2020 · Multi-agent systems and games › Human-AI and goal-conditioned · approach: Imitation and skill learning · 12 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
add CG-Pruning to BFS over sensor extensions (ablation)information shaping search (FD) · WUMPUS-KEYfraction completed: 0.13 -> 0.26Helped
add CG-Pruning to BFS over sensor extensions (ablation)information shaping search (FD) · LOGISTICS-Ifraction completed: 0.4 -> 0.7Helped
add CG-Pruning (ablation)information shaping search (FF) · LOGISTICS-Ifraction completed: 0.43 -> 0.92Helped
add CG-Pruning (ablation)information shaping search (FD) · UNIXaverage time (s): 179.6 -> 49.2Helped
add CG-Pruning (ablation)information shaping search (FD) · WUMPUSaverage time (s): 1184.32 -> 854.62Helped
add CG-Pruning (ablation)information shaping search (FF) · TRAILaverage time (s): 70.65 -> 107.66Hurt
add CG-Pruning (ablation)information shaping search (FF) · COLOR-BALLSaverage time (s): 29.32 -> 55.23Hurt
add CG-Pruning (ablation)information shaping search · all domainsWCD reductionNo clear effect
apply information shaping (sensor extensions)goal recognition design with partially informed actor · LOGISTICS-I (FD)WCD reduction: 3.91Helped
reveal more information to actor (non-monotonic)GRD-APK · Wumpus exampleWCD: 0 -> 4Mixed

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