How Rival Teams Teach Robots to Team Up Under Attack

When a swarm of birds scatters to confuse a predator, or a basketball team rotates to defend a fast break, something remarkable happens: individual agents stop acting independently and start behaving as a team. A new mini review published in Frontiers in Control Engineering argues that these adversarial moments—when a group of agents must contend with a rival group—are precisely where the most interesting and effective team behaviors emerge. Written by Daigo Shishika of George Mason University, Michael Dorothy of the DEVCOM Army Research Laboratory, and Douglas G. Macharet of the Federal University of Minas Gerais, the review maps out how the field of multiagent systems studies group control when opponents enter the picture, and it organizes the sprawling literature around a deceptively simple question: what exactly is being assigned, and how does the assignment change when an adversary is watching?

The authors deliberately narrow the term adversarial. Rather than treating any risky environment as adversarial, they focus on scenarios involving competing teams of agents that must physically move through an environment to achieve their objectives. This framing captures everything from natural swarms evolving against predators to military and civilian security applications such as patrolling, target tracking, and interception. The review’s central insight is that many multiagent coordination problems can be understood as versions of task allocation, but adversarial scenarios stress two aspects of allocation that benign settings rarely do: the level of coupling between tasks, and the dynamic, strategic nature of the assignments themselves.

The coupling issue is fundamental. A common way of deploying a multiagent system is the divide-and-conquer approach, which allows spatially distributed tasks to be executed efficiently. In coverage control for mobile sensing networks, for example, each agent handles its own patch, and the success of one agent does not affect the success of its teammates. But when the overall group must work as a cohesive team to compete against another team, the task may not decompose into independent subtasks. Instead, agents may have to take specialized roles that augment each other, where the failure of one member can significantly affect the overall success of the team. The review distinguishes these two regimes with the terms coordination, for loosely coupled tasks whose contributions to team performance are linear or sublinear, and cooperation, for tightly coupled tasks where outcomes depend on joint action.

The second stress point is strategic. When the adversary is modeled as another decision-making entity, the problem is often formulated as a non-cooperative dynamic game. In such a formulation, the adversary can take advantage of static or open-loop assignment strategies, so the assignment must consider the evolution of system states as well as possible adversarial actions. This is one step beyond an assignment policy based on static analysis performed repeatedly over time. The authors categorize dynamic assignments as either passive, meaning re-assignments based on instantaneous optimality as the situation changes, or strategic, meaning assignments determined by anticipating the adversary’s decisions and how the engagement will unfold over time.

The review first examines the workhorse problems of patrolling, routing, and tracking. Patrolling involves systematically visiting designated areas to uphold security and monitor changes, and prevailing approaches often rely on pre-determined cyclic or partition strategies, sometimes extended to non-deterministic behaviors. Persistent surveillance has also been modeled as a vehicle routing problem. Yet successful patrolling teams demand adaptive decision-making, dynamically adjusting strategies based on unexpected events or threats. A specific research category centers on adversarial patrolling, where explicit attacker models and adversary detection are taken into account, allowing the development and analysis of defense mechanisms and countermeasure strategies that significantly enhance resilience against a wide array of potential attack scenarios.

Routing problems, where the objective is to determine time- or length-optimized paths to deliver information, goods, or services, undergo distinct formulations when posed adversarially. Researchers have considered the presence of hazardous zones in orienteering-style problems, and perimeter defense has emerged as an important task where the goal is to protect a region from targets attempting to enter or escape. Dynamic vehicle routing formulations have been proposed for a single agent capturing linearly translating targets, with the objective of intercepting the maximum number of targets en route before they reach the perimeter. The same concept has been extended to the multiagent case using adaptive partition strategies and network flows formulations for heterogeneous teams. In target tracking, which underlies surveillance, search and rescue, environmental monitoring, and military operations, a fundamental adversarial approach involves reconfiguring team displacement to restore impacted capabilities, and robust frameworks have been designed to withstand sensing and communication attacks or failures.

The heart of the review is differential games, where direct engagement between a red team and a blue team is modeled mathematically. A central example is the reach-avoid game, a variant of pursuit-evasion games in which a group of evaders, the attackers, seeks to reach a target area while a group of pursuers, the defenders, tries to minimize the number of breaches by intercepting them. The dominant solution approach is based on agent-agent assignments: the team-versus-team problem is approximated as a combination of one-versus-one problems solvable with traditional differential game techniques, and the pairwise outcomes are encoded into a bipartite graph. Classic algorithms such as the Hungarian algorithm, which solves maximum matching or minimum linear sum assignment problems in polynomial time, then determine which pursuer should go after which evader. Many variants of multiagent reach-avoid games use this decomposition, with changes in game geometry producing different low-level control strategies while the high-level team strategies all reduce coordination to pairwise assignments.

Progress on deeper teaming has come from breaking this pairwise mold. Researchers have considered how a coalition of pursuers can team up against a single evader, generalizing the assignment approach. In perimeter defense games, where defending agents are constrained to move along the perimeter of the target, a two-versus-one pincer movement was proposed, and this result was further used to construct bounded regions within which n-versus-m local games were defined. While most multiagent reach-avoid games assume individually greedy attacker strategies, this n-versus-m formulation showed how a subteam of attackers can concentrate force on a single point so that, when there are more attackers than defenders, a calculated number survive and score. The authors categorize this as force concentration, which has tighter coupling than force distribution because the success of a subteam relies on the cooperation of its members rather than on the sum of individual performances. A distinct type of teaming appears in the turret-runner-penetrator differential game, where a turret-like defender neutralizes attackers by turning and pointing at them. Under certain initial conditions, it is optimal for the attackers to split into two roles: the runner acts as a decoy to waste as much time as possible before the turret pursues the penetrator, who seeks to breach the perimeter. Such a sacrificial role is absent from the homogeneous task assignment seen in most reach-avoid games, hinting at more sophisticated teaming beyond divide and conquer.

At a higher abstraction level, the review considers Colonel Blotto games, in which two colonels each allocate a fixed budget of resources across a set of battlefields, and a battlefield is won by whoever commits more resources to it. One type of teaming that arises here is coalition formation: in three-player coalition Blotto games, two players each fight a common opponent while remaining selfish, yet each has an incentive to leverage the other’s actions to implicitly cooperate and beat the stronger player together. The dynamic attacker-defender Blotto game bridges motion control and resource allocation, played over multiple stages with sequential actions and a fundamental asymmetry: the attacker wins by achieving numerical advantage at any single node, whereas the defender must maintain advantage at all nodes simultaneously. Strategies that control platoons of defender resources require coordination to ensure no gap in defended nodes, a behavior the authors label formation maintenance.

Perhaps the most striking source of evidence comes from human team sports. Humans work together under limited information, mixed motives, and nonstationarity, and while human strategies may not always be optimal in the language of traditional game theory due to cognitive biases, bounded rationality, or the compromises of coalition-building, they work remarkably well. Recent advances in motion tracking technology now enable the collection of large quantities of player trajectory data, and machine learning techniques are being used to learn player-specific action templates and to quantify high-level concepts such as space-creating and space-exploiting behavior in professional soccer. The authors view team sports as representing the full diversity of behavior and complexity that robotic systems should attain in adversarial environments. The review concludes by identifying the frontiers where the challenge includes scaling game-theoretic strategies to large groups, enhancing teaming through specialized roles, and incorporating adversarial decision making into decentralized multiagent systems—steps toward robots that, like a well-drilled team, can anticipate an opponent and adapt together in real time.

Subject of Research: Multiagent systems and teaming behavior in adversarial scenarios

Article Title: Teaming behavior in adversarial scenarios

Article References: Shishika, D., Dorothy, M., & Macharet, D. G. (2023). Teaming behavior in adversarial scenarios. Frontiers in Control Engineering, 4, Article 1279454. https://doi.org/10.3389/fcteg.2023.1279454

Image Credits: AI Generated

DOI: 10.3389/fcteg.2023.1279454

Keywords: multiagent systems, adversarial scenarios, dynamic games, task assignment, pursuit evasion, team sports analytics, Teaming, behavior, adversarial, scenarios, scientific research