An important research problem in artificial intelligence is how to organize multiple agents, and coordinate them, so that they can work together to solve problems. Coordinating agents in a multi-agent system can significantly affect the system’s performance—the agents can, in many instances, be organized so that they can solve tasks more efficiently, and consequently benefit collectively and individually. Central to this endeavor is coalition formation—the process by which heterogeneous agents organize and form disjoint groups (coalitions). Coalition formation often involves finding a coalition structure (an exhaustive set of disjoint coalitions) that maximizes the system’s potential performance (e.g., social welfare) through coalition structure generation. However, coalition structure generation typically has no notion of goals. In cooperative settings, where coordination of multiple coalitions is important, this may generate suboptimal teams for achieving and accomplishing the tasks and goals at hand. With this in mind, we consider simultaneously generating coalitions of agents and assigning the coalitions to independent alternatives (e.g., tasks/goals), and present an anytime algorithm for the simultaneous coalition structure generation and assignment problem. This combinatorial optimization problem has many real-world applications, including forming goal-oriented teams. To evaluate the presented algorithm’s performance, we present five methods for synthetic problem set generation, and benchmark the algorithm against the industry-grade solver CPLEX using randomized data sets of varying distribution and complexity. To test its anytime-performance, we compare the quality of its interim solutions against those generated by a greedy algorithm and pure random search. Finally, we also apply the algorithm to solve the problem of assigning agents to regions in a major commercial strategy game, and show that it can be used in game-playing to coordinate smaller sets of agents in real-time.

中文翻译：

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用于同时优化联盟结构生成和分配的随时算法

人工智能中一个重要的研究问题是如何组织多个智能体并进行协调，以使它们可以共同解决问题。多代理系统中的协调代理会显着影响系统的性能-在许多情况下，可以对代理进行组织，以便他们可以更有效地解决任务，并因此集体和个人受益。这项工作的核心是联盟的形成，即异质代理组织并形成不相交的群体（联盟）的过程。联盟形成通常涉及找到一个联盟结构（一组详尽的脱节联盟），该结构通过产生联盟结构来最大化系统的潜在性能（例如，社会福利）。但是，联盟结构生成通常没有目标的概念。在需要多个联盟协调的合作环境中，这可能会产生次优团队来实现和完成当前的任务和目标。考虑到这一点，我们考虑同时生成代理联盟并将联盟分配给独立的备选方案（例如，任务/目标），并提出了一种用于同时联盟结构生成和分配问题的随时算法。这个组合优化问题有许多实际应用，包括组成面向目标的团队。为了评估所提出算法的性能，我们提出了五种用于综合问题集生成的方法，并使用分布和复杂性各不相同的随机数据集，针对该工业级求解器CPLEX对该算法进行了基准测试。为了测试其随时性能，我们将其临时解决方案的质量与由贪婪算法和纯随机搜索生成的临时解决方案的质量进行了比较。最后，我们还将该算法用于解决大型商业策略游戏中将代理分配给区域的问题，并表明该算法可用于游戏中以实时协调较小的代理组。