In this paper we describe a novel approach to team formation based on the value of inter-agent interactions. Specifically, we propose a model of teamwork that considers outcomes from chains of interactions between agents. Based on our model, we devise a number of network metrics to capture the contribution of interactions between agents. This is then used to learn the value of teamwork from historical team performance data. We apply our model to predict team performance and validate our approach using real-world team performance data from the 2018 FIFA World Cup. Our model is shown to better predict the real-world performance of teams by up to 46% compared to models that ignore inter-agent interactions.

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Coalition Structure Generation (CSG) is an NP-complete problem that remains difficult to solve on account of its complexity. In this paper, we propose an efficient hybrid algorithm for optimal coalition structure generation called ODSS. ODSS is a hybrid version of two previously established algorithms IDP (Rahwan and Jennings 2008) and IP (Rahwan et al. 2009). ODSS minimizes the overlapping between IDP and IP by dividing the whole search space of CSG into two disjoint sets of subspaces and proposes a novel subspace shrinking technique to reduce the size of the subspace searched by IP with the help of IDP. When compared to the state-of-the-art against a wide variety of value distributions, ODSS is shown to perform better by up to 54.15% on benchmark inputs.

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In this paper, we focus on the Coalition Structure Generation (CSG) problem, which involves finding exhaustive and disjoint partitions of agents such that the efficiency of the entire system is optimized. We propose an efficient hybrid algorithm for optimal coalition structure generation called BOSS. When compared to the state-of-the-art, BOSS is shown to perform better by up to 33.63% on benchmark inputs. The maximum time gain by BOSS is 3392 seconds for 27 agents.