Resource-Aware Adaptation of Heterogeneous Strategies for Coalition Formation

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 target team without requiring additional training. Specifically, we formulate and solve a constrained optimization problem that simultaneously selects the most appropriate strategy given the target team's capabilities, and allocates its constituents into appropriate coalitions. We evaluate our approach against several baselines, including some that resemble existing approaches, using detailed numerical simulations, StarCraft II battles, and a multi-robot emergency-response scenario. Our results indicate that our framework consistently outperforms all baselines in terms of requirement satisfaction, resource utilization, and task success rates.
Comment: 10 pages, 6 figures, to be published as extended abstract in AAMAS 2022