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1. Using Human-Agent Teams to Purposefully Design Multi-Agent Systems

   https://doi.org/10.1177/1071181319631238  

   Flathmann, Christopher; McNeese, Nathan; Canonico, Lorenzo Barberis (November 2019, Proceedings of the Human Factors and Ergonomics Society Annual Meeting)

   With multi-agent teams becoming more of a reality every day, it is important to create a common design model for multi-agent teams. These teams need to be able to function in dynamic environments and still communicate with any humans that may need a problem solved. Existing human-agent research can be used to purposefully create multi-agent teams that are interdependent but can still interact with humans. Rather than creating dynamic agents, the most effective way to overcome the dynamic nature of modern workloads is to create a dynamic team configuration, rather than individual member-agents that can change their roles. Multi-agent teams will require a variety of agents to be designed to cover a diverse subset of problems that need to be solved in the modern workforce. A model based on existing multi-agent teams that satisfies the needs of human-agent teams has been created to serve as a baseline for human-interactive multi-agent teams. 

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2. Search Algorithms for Multi-Agent Teamwise Cooperative Path Finding

   https://doi.org/10.1109/ICRA48891.2023.10160864  

   Ren, Zhongqiang; Zhang, Chaoran; Rathinam, Sivakumar; Choset, Howie (May 2023, IEEE)

   Multi-Agent Path Finding (MA-PF) computes a set of collision-free paths for multiple agents from their respective starting locations to destinations. This paper considers a generalization of MA-PF called Multi-Agent Teamwise Cooperative Path Finding (MA-TC-PF), where agents are grouped as multiple teams and each team has its own objective to be minimized. For example, an objective can be the sum or max of individual arrival times of the agents. In general, there is more than one team, and MA-TC-PF is thus a multi-objective planning problem with the goal of finding the entire Paretooptimal front that represents all possible trade-offs among the objectives of the teams. To solve MA-TC-PF, we propose two algorithms TC-CBS and TC-M*, which leverage the existing CBS and M* for conventional MA-PF. We discuss the conditions under which the proposed algorithms are complete and are guaranteed to find the Pareto-optimal front. We present numerical results for several types of MA-TC-PF problems. 

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3. Ad Hoc Teaming Through Evolution

   Cook, Joshua; Tumer, Kagan (July 2021, Genetic and evolutionary computation)

   null (Ed.)

   Cooperative Co-evolutionary Algorithms effectively train policies in multiagent systems with a single, statically defined team. However, many real-world problems, such as search and rescue, require agents to operate in multiple teams. When the structure of the team changes, these policies show reduced performance as they were trained to cooperate with only one team. In this work, we solve the cooperation problem by training agents to fill the needs of an arbitrary team, thereby gaining the ability to support a large variety of teams. We introduce Ad hoc Teaming Through Evolution (ATTE) which evolves a limited number of policy types using fitness aggregation across multiple teams. ATTE leverages agent types to reduce the dimensionality of the interaction search space, while fitness aggregation across teams selects for more adaptive policies. In a simulated multi-robot exploration task, ATTE is able to learn policies that are effective in a variety of teaming schemes, improving the performance of CCEA by a factor of up to five times. 

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4. Team Assignment for Heterogeneous Multi-Robot Sensor Coverage through Graph Representation Learning

   https://doi.org/10.1109/ICRA48506.2021.9561082  

   Reily, Brian; Zhang, Hao (January 2021, International Conference on Robotics and Automation)

   Sensor coverage is the critical multi-robot problem of maximizing the detection of events in an environment through the deployment of multiple robots. Large multi-robot systems are often composed of simple robots that are typically not equipped with a complete set of sensors, so teams with comprehensive sensing abilities are required to properly cover an area. Robots also exhibit multiple forms of relationships (e.g., communication connections or spatial distribution) that need to be considered when assigning robot teams for sensor coverage. To address this problem, in this paper we introduce a novel formulation of sensor coverage by multi-robot systems with heterogeneous relationships as a graph representation learning problem. We propose a principled approach based on the mathematical framework of regularized optimization to learn a unified representation of the multi-robot system from the graphs describing the heterogeneous relationships and to identify the learned representation’s underlying structure in order to assign the robots to teams. To evaluate the proposed approach, we conduct extensive experiments on simulated multi-robot systems and a physical multi-robot system as a case study, demonstrating that our approach is able to effectively assign teams for heterogeneous multi-robot sensor coverage. 

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5. Adaptive Agent Architecture for Real-time Human-Agent Teaming

   Ni, T. (January 2021, AAAI Workshop on Plan, Activity, and Intent Recognition)

   Teamwork is a set of interrelated reasoning, actions and behaviors of team members that facilitate common objectives. Teamwork theory and experiments have resulted in a set of states and processes for team effectiveness in both human-human and agent-agent teams. However, human-agent teaming is less well studied because it is so new and involves asymmetry in policy and intent not present in human teams. To optimize team performance in human-agent teaming, it is critical that agents infer human intent and adapt their polices for smooth coordination. Most literature in human-agent teaming builds agents referencing a learned human model. Though these agents are guaranteed to perform well with the learned model, they lay heavy assumptions on human policy such as optimality and consistency, which is unlikely in many real-world scenarios. In this paper, we propose a novel adaptive agent architecture in human-model-free setting on a two-player cooperative game, namely Team Space Fortress (TSF). Previous human-human team research have shown complementary policies in TSF game and diversity in human players’ skill, which encourages us to relax the assumptions on human policy. Therefore, we discard learning human models from human data, and instead use an adaptation strategy on a pre-trained library of exemplar policies composed of RL algorithms or rule-based methods with minimal assumptions of human behavior. The adaptation strategy relies on a novel similarity metric to infer human policy and then selects the most complementary policy in our library to maximize the team performance. The adaptive agent architecture can be deployed in real-time and generalize to any off-the-shelf static agents. We conducted human-agent experiments to evaluate the proposed adaptive agent framework, and demonstrated the suboptimality, diversity, and adaptability of human policies in human-agent teams. 

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