Resource-Aware Adaptation of Heterogeneous Strategies for Coalition Formation
Anusha Srikanthan, Harish Ravichandar

TL;DR
This paper introduces a framework for inferring and adaptively selecting heterogeneous coalition formation strategies from expert demonstrations, improving requirement satisfaction and resource use in multi-agent tasks.
Contribution
It presents a novel method to infer implicit, heterogeneous strategies from demonstrations and adaptively select the best strategy for a given team without retraining.
Findings
Outperforms baselines in requirement satisfaction
Achieves higher resource utilization
Increases task success rates
Abstract
Existing approaches to coalition formation often assume that requirements associated with tasks are precisely specified by the human operator. However, prior work has demonstrated that humans, while extremely adept at solving complex problems, struggle to explicitly state their solution strategy. Further, existing approaches often ignore the fact that experts may utilize different, but equally-valid, solutions (i.e., heterogeneous strategies) to the same problem. In this work, we propose a two-part framework to address these challenges. First, we tackle the challenge of inferring implicit strategies directly from expert demonstrations of coalition formation. To this end, we model and infer such heterogeneous strategies as capability-based requirements associated with each task. Next, we propose a method capable of adaptively selecting one of the inferred strategies that best suits the…
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Taxonomy
TopicsReinforcement Learning in Robotics · Evacuation and Crowd Dynamics · Military Defense Systems Analysis
