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Mingkai Tang, Yuanhang Li, Hongji Liu, Yingbing Chen, Ming Liu, Lujia Wang

With the expansion of the scale of robotics applications, the multi-goal multi-agent pathfinding (MG-MAPF) problem began to gain widespread attention. This problem requires each agent to visit pre-assigned multiple goal points at least once without conflict. Some previous methods have been proposed to solve the MG-MAPF problem based on Decoupling the goal Vertex visiting order search and the Single-agent pathfinding (DVS). However, this paper demonstrates that the methods based on DVS cannot always obtain the optimal solution. To obtain the optimal result, we propose the Multi-Goal Conflict-Based Search (MGCBS), which is based on Decoupling the goal Safe interval visiting order search and the Single-agent pathfinding (DSS). Additionally, we present the Time-Interval-Space Forest (TIS Forest) to enhance the efficiency of MGCBS by maintaining the shortest paths from any start point at any start time step to each safe interval at the goal points. The experiment demonstrates that our method can consistently obtain optimal results and execute up to 7 times faster than the state-of-the-art method in our evaluation.

Redha Taguelmimt, Samir Aknine, Djamila Boukredera, Narayan Changder, Tuomas Sandholm

Coalition formation is a key capability in multi-agent systems. An important problem in coalition formation is coalition structure generation: partitioning agents into coalitions to optimize the social welfare. This is a challenging problem that has been the subject of active research for the past three decades. In this paper, we present a novel algorithm, SMART, for the problem based on a hybridization of three innovative techniques. Two of these techniques are based on dynamic programming, where we show a powerful connection between the coalitions selected for evaluation and the performance of the algorithms. These algorithms use offline phases to optimize the choice of coalitions to evaluate. The third one uses branch-and-bound and integer partition graph search to explore the solution space. Our techniques bring a new way of approaching the problem and a new level of precision to the field. In experiments over several common value distributions, we show that the hybridization of these techniques in SMART is faster than the fastest prior algorithms (ODP-IP, BOSS) in generating optimal solutions across all the value distributions.

Oliver Sourbut, Lewis Hammond, Harriet Wood

Many settings of interest involving humans and machines – from virtual personal assistants to autonomous vehicles – can naturally be modelled as principals (humans) delegating to agents (machines), which then interact with each other on their principals’ behalf. We refer to these multi-principal, multi-agent scenarios as delegation games. In such games, there are two important failure modes: problems of control (where an agent fails to act in line their principal’s preferences) and problems of cooperation (where the agents fail to work well together). In this paper we formalise and analyse these problems, further breaking them down into issues of alignment (do the players have similar preferences?) and capabilities (how competent are the players at satisfying those preferences?). We show – theoretically and empirically – how these measures determine the principals’ welfare, how they can be estimated using limited observations, and thus how they might be used to help us design more aligned and cooperative AI systems.

Martin Smit, Fernando P. Santos

Altruistic cooperation is costly yet socially desirable. As a result, agents struggle to learn cooperative policies through independent reinforcement learning (RL). Indirect reciprocity, where agents consider their interaction partner’s reputation, has been shown to stabilise cooperation in homogeneous, idealised populations. However, more realistic settings are comprised of heterogeneous agents with different characteristics and group-based social identities. We study cooperation when agents are stratified into two such groups, and allow reputation updates and actions to depend on group information. We consider two modelling approaches: evolutionary game theory, where we comprehensively search for social norms (i.e., rules to assign reputations) leading to cooperation and fairness; and RL, where we consider how the stochastic dynamics of policy learning affects the analytically identified equilibria. We observe that a defecting majority leads the minority group to defect, but not the inverse. Moreover, changing the norms that judge inand out-group interactions can steer a system towards either fair or unfair cooperation. This is made clearer when moving beyond equilibrium analysis to independent RL agents, where convergence to fair cooperation occurs with a narrower set of norms. Our results highlight that, in heterogeneous populations with reputations, carefully defining interaction norms is fundamental to tackle both dilemmas of cooperation and of fairness.

Jonathan Skaggs, Michael Richards, Melissa Morris, Michael A. Goodrich, Jacob W. Crandall

As AI integrates into human societies, its ability to engage in collective action is increasingly important. Human social systems have large and flexible strategy spaces, conflicting interests, power asymmetry, and interdependence among members, which together make it challenging for agents to learn collective action. In this paper, we explore the ability of community-based agents to learn collective action within a novel model of complex social systems. We first present this social model, called the Junior High Game (JHG). The JHG embodies key elements of human social systems that require players to act collectively. We then describe an agent, called CAB, which is based on community detection and formation algorithms. Via simulations and user studies, we evaluate the ability of CAB agents to interact in JHG societies consisting of humans and AI agents. These evaluations both identify requirements for successful collective behaviors in the JHG and identify important unsolved problems for developing AI agents capable of collective action in complex social systems.

Munindar P. Singh, Samuel H. Christie V., Amit K. Chopra

Current languages for specifying multiagent protocols either over-constrain protocol enactments or complicate capturing their meanings. We propose Langshaw, a declarative protocol language based on (1) sayso, a new construct that captures who has priority over setting each attribute, and (2) nono and nogo, two constructs to capture conflicts between actions. Langshaw combines flexibility with an information model to express meaning. We give a formal semantics for Langshaw, procedures for determining the safety and liveness of a protocol, and a method to generate a message-oriented protocol (embedding needed coordination) suitable for flexible asynchronous enactment.

Tianyu Ren, Xiao-Jun Zeng

The significance of network structures in promoting group cooperation within social dilemmas has been widely recognized. Prior studies attribute this facilitation to the assortment of strategies driven by spatial interactions. Although reinforcement learning has been employed to investigate the impact of dynamic interaction on the evolution of cooperation, there remains a lack of understanding about how agents develop neighbour selection behaviours and the formation of strategic assortment within an explicit interaction structure. To address this, our study introduces a computational framework based on multi-agent reinforcement learning in the spatial Prisoner's Dilemma game. This framework allows agents to select dilemma strategies and interacting neighbours based on their long-term experiences, differing from existing research that relies on preset social norms or external incentives. By modelling each agent using two distinct Q-networks, we disentangle the coevolutionary dynamics between cooperation and interaction. The results indicate that long-term experience enables agents to develop the ability to identify non-cooperative neighbours and exhibit a preference for interaction with cooperative ones. This emergent self-organizing behaviour leads to the clustering of agents with similar strategies, thereby increasing network reciprocity and enhancing group cooperation.

Yang Qu, Jinming Ma, Feng Wu

Active voltage control presents a promising avenue for relieving power congestion and enhancing voltage quality, taking advantage of the distributed controllable generators in the power network, such as roof-top photovoltaics. While Multi-Agent Reinforcement Learning (MARL) has emerged as a compelling approach to address this challenge, existing MARL approaches tend to overlook the constrained optimization nature of this problem, failing in guaranteeing safety constraints. In this paper, we formalize the active voltage control problem as a constrained Markov game and propose a safety-constrained MARL algorithm. We expand the primal-dual optimization RL method to multi-agent settings, and augment it with a novel approach of double safety estimation to learn the policy and to update the Lagrange-multiplier. In addition, we proposed different cost functions and investigated their influences on the behavior of our constrained MARL method. We evaluate our approach in the power distribution network simulation environment with real-world scale scenarios. Experimental results demonstrate the effectiveness of the proposed method compared with the state-of-the-art MARL methods.

Atasi Panda, Anand Louis, Prajakta Nimbhorkar

We study the probabilistic assignment of items to platforms that satisfies both group and individual fairness constraints. Each item belongs to specific groups and has a preference ordering over platforms. Each platform enforces group fairness by limiting the number of items per group that can be assigned to it. There could be multiple optimal solutions that satisfy the group fairness constraints, but this alone ignores item preferences. Our approach explores a `best of both worlds fairness' solution to get a randomized matching, which is ex-ante individually fair and ex-post group-fair. Thus, we seek a `probabilistic individually fair' distribution over `group-fair' matchings where each item has a `high' probability of matching to one of its top choices. This distribution is also ex-ante group-fair. Users can customize fairness constraints to suit their requirements. Our first result is a polynomial-time algorithm that computes a distribution over `group-fair' matchings such that the individual fairness constraints are approximately satisfied and the expected size of a matching is close to OPT. We empirically test this on real-world datasets. We present two additional polynomial-time bi-criteria approximation algorithms that users can choose from to balance group fairness and individual fairness trade-offs. For disjoint groups, we provide an exact polynomial-time algorithm adaptable to additional lower `group fairness' bounds. Extending our model, we encompass `maxmin group fairness,' amplifying underrepresented groups, and `mindom group fairness,' reducing the representation of dominant groups.'

Dung Nguyen, Hung Le, Kien Do, Sunil Gupta, Svetha Venkatesh, Truyen Tran

The challenge in constructing artificial social agents is to enable adaptation ability to novel agents, and is called zero-shot coordination (ZSC). A promising approach is to train the adaptive agents by interacting with a diverse pool of collaborators, assuming that the greater the diversity in other agents seen during training, the better the generalisation. In this paper, we explore an alternative procedure by considering the behavioural predictability of collaborators, i.e. whether their actions and intentions are predictable, and use it to select a diverse set of agents for the training pool. More specifically, we develop a pool of agents through self-play training during which agents' behaviour evolves and has diversity in levels of behavioural predictability (LoBP) through its evolution. We construct an observer to compute the level of behavioural predictability for each version of the collaborators. To do so, the observer is equipped with the theory of mind (ToM) capability to learn to infer the actions and intentions of others. We then use an episodic memory based on the LoBP metric to maintain agents with different levels of behavioural predictability in the pool of agents. Since behaviours that emerge at the later training phase are more complex and meaningful, the memory is updated with the latest versions of training agents. Our extensive experiments demonstrate that LoBP-based diversity training leads to better ZSC than other diversity training methods.

Katherine Mayo, Nicholas Grabill, Michael P. Wellman

Whereas standard financial mechanisms for payment may take days to finalize, real-time payments (RTPs) provide immediate processing and final receipt of funds. The speed of settlement benefits customers, but raises vulnerability to fraud. We seek to understand how bank nodes may strategically mitigate fraud risk in RTPs, through investment in fraud detection and restricting payments eligible for real-time processing. To study this, we introduce an agent-based model of the payment network supporting both real-time and standard payments, and define a game among banks and fraudsters. Using empirical game-theoretic analysis, we identify Nash equilibria in nine game configurations defined by network attributes. Our analysis finds that as banks become more liable for fraud, they continue to allow RTPs but are more likely to employ both restrictions and a high level of fraud detection. Fraudsters, in response, switch from targeting only RTPs to attempting fraud with any type of payment and tend to exploit banks where they have historically been most successful. We also conduct a strategic feature gains assessment to further understand the benefit offered by each of the bank's risk mitigation measures, which confirms the importance of selective RTP restrictions. Finally, we find that in equilibrium bank strategic decisions negatively affect fraudsters while minimally impacting customers.

Zun Li, Michael P. Wellman

Evaluating deep multiagent reinforcement learning (MARL) algorithms is complicated by stochasticity in training and sensitivity of agent performance to the behavior of other agents. We propose a meta-game evaluation framework for deep MARL, by framing each MARL algorithm as a meta-strategy, and repeatedly sampling normal-form empirical games over combinations of meta-strategies resulting from different random seeds. Each empirical game captures both self-play and cross-play factors across seeds. These empirical games provide the basis for constructing a sampling distribution, using bootstrapping, over a variety of game analysis statistics. We use this approach to evaluate state-of-the-art deep MARL algorithms on a class of negotiation games. From statistics on individual payoffs, social welfare, and empirical best-response graphs, we uncover strategic relationships among self-play, population-based, model-free, and model-based MARL methods. We also investigate the effect of run-time search as a meta-strategy operator, and find via meta-game analysis that the search version of a meta-strategy generally leads to improved performance.

Pengdeng Li, Shuxin Li, Chang Yang, Xinrun Wang, Xiao Huang, Hau Chan, Bo An

Policy-Space Response Oracles (PSRO) as a general algorithmic framework has achieved state-of-the-art performance in learning equilibrium policies of two-player zero-sum games. However, the hand-crafted hyperparameter value selection in most of the existing works requires extensive domain knowledge, forming the main barrier to applying PSRO to different games. In this work, we make the first attempt to investigate the possibility of self-adaptively determining the optimal hyperparameter values in the PSRO framework. Our contributions are three-fold: (1) Using several hyperparameters, we propose a parametric PSRO that unifies the gradient descent ascent (GDA) and different PSRO variants. (2) We propose the self-adaptive PSRO (SPSRO) by casting the hyperparameter value selection of the parametric PSRO as a hyperparameter optimization (HPO) problem where our objective is to learn an HPO policy that can self-adaptively determine the optimal hyperparameter values during the running of the parametric PSRO. (3) To overcome the poor performance of online HPO methods, we propose a novel offline HPO approach to optimize the HPO policy based on the Transformer architecture. Experiments on various two-player zero-sum games demonstrate the superiority of SPSRO over different baselines.

Kanxue Li, Baosheng Yu, Qi Zheng, Yibing Zhan, Yuhui Zhang, Tianle Zhang, Yijun Yang, Yue Chen, Lei Sun, Qiong Cao 等

Foundation models have demonstrated significant emergent abilities, holding great promise for enhancing embodied agents' reasoning and planning capacities. However, the absence of a comprehensive benchmark for evaluating embodied agents with multimodal observations in complex environments remains a notable gap. In this paper, we present MuEP, a comprehensive Multimodal benchmark for Embodied Planning. MuEP facilitates the evaluation of multimodal and multi-turn interactions of embodied agents in complex scenes, incorporating fine-grained evaluation metrics that provide insights into the performance of embodied agents throughout each task. Furthermore, we evaluate embodied agents with recent state-of-the-art foundation models, including large language models (LLMs) and large multimodal models (LMMs), on the proposed benchmark. Experimental results show that foundation models based on textual representations of environments usually outperform their visual counterparts, suggesting a gap in embodied planning abilities with multimodal observations. We also find that control language generation is an indispensable ability beyond common-sense knowledge for accurate embodied task completion. We hope the proposed MuEP benchmark can contribute to the advancement of embodied AI with foundation models.

Chao Li, Yujing Hu, Shangdong Yang, Tangjie Lv, Changjie Fan, Wenbin Li, Chongjie Zhang, Yang Gao

This paper focuses on the problem of learning compressed state representations for multi-agent tasks. Under the assumption of rich observation, we pinpoint that the state representations should be compressed both spatially and temporally to enable efficient prioritization of task-relevant features, while existing works typically fail. To overcome this limitation, we propose a novel method named Spatio-Temporal stAte compRession (STAR) that explicitly defines both spatial and temporal compression operations on the learned state representations to encode per-agent task-relevant features. Specifically, we first formalize this problem by introducing Task Informed Partially Observable Stochastic Game (TI-POSG). Then, we identify the spatial representation compression in it as encoding the latent states from the joint observations of all agents, and achieve this by learning representations that approximate the latent states based on the information theoretical principle. After that, we further extract the task-relevant features of each agent from these representations by aligning them based on their reward similarities, which is regarded as the temporal representation compression. Structurally, we implement these two compression by learning a set of agent-specific decoding functions and incorporate them into a critic shared by agents for scalable learning. We evaluate our method by developing decentralized policies on 12 maps of the StarCraft Multi-Agent Challenge benchmark, and the superior performance demonstrates its effectiveness.

Chin-wing Leung, Tom Lenaerts, Paolo Turrini

If given the choice, what strategy should agents use to switch partners in strategic social interactions? While many analyses have been performed on specific switching heuristics, showing how and when these lead to more cooperation, no insights have been provided into which rule will actually be learnt by agents when given the freedom to do so. Starting from a baseline model that has demonstrated the potential of rewiring for cooperation, we provide answers to this question over the full spectrum of social dilemmas. Multi-agent Q-learning with Boltzmann exploration is used to learn when to sever or maintain an association. In both the Prisoner's Dilemma and the Stag Hunt games we observe that the Out-for-Tat rewiring rule, breaking ties with other agents choosing socially undesirable actions, becomes dominant, confirming at the same time that cooperation flourishes when rewiring is fast enough relative to imitation. Nonetheless, in the transitory region before full cooperation, a Stay strategy, keeping a connection at all costs, remains present, which shows that loyalty needs to be overcome for full cooperation to emerge. In conclusion, individuals learn cooperation-promoting rewiring rules but need to overcome a kind of loyalty to achieve full cooperation in the full spectrum of social dilemmas.

Panagiotis Kouvaros, Elena Botoeva, Cosmo De Bonis-Campbell

We study the problem of verifying multi-agent systems composed of arbitrarily many neural-symbolic agents. We introduce a novel parameterised model, where the parameter denotes the number of agents in the system, each homogeneously constructed from an agent template equipped with a neural network-based perception unit and a traditionally programmed action selection mechanism. We define the verification and emergence identification problems for these models against a bounded fragment of CTL. We put forward an abstraction methodology that enables us to recast both problems to the problem of checking Neural Interpreted Systems with a bounded number of agents. We present an implementation and discuss experimental results obtained on a social dilemma game based on guarding.

Haoyuan Jiang, Ziyue Li, Hua Wei, Xuantang Xiong, Jingqing Ruan, Jiaming Lu, Hangyu Mao, Rui Zhao

The effectiveness of traffic light control has been significantly improved by current reinforcement learning-based approaches via better cooperation among multiple traffic lights. However, a persisting issue remains: how to obtain a multi-agent traffic signal control algorithm with remarkable transferability across diverse cities? In this paper, we propose a Transformer on Transformer (TonT) model for cross-city meta multi-agent traffic signal control, named as X-Light: We input the full Markov Decision Process trajectories, and the Lower Transformer aggregates the states, actions, rewards among the target intersection and its neighbors within a city, and the Upper Transformer learns the general decision trajectories across different cities. This dual-level approach bolsters the model's robust generalization and transferability. Notably, when directly transferring to unseen scenarios, ours surpasses all baseline methods with +7.91% on average, and even +16.3% in some cases, yielding the best results.

Kangyao Huang, Di Guo, Xinyu Zhang, Xiangyang Ji, Huaping Liu

Training an agent to adapt to specific tasks through co-optimization of morphology and control has widely attracted attention. However, whether there exists an optimal configuration and tactics for agents in a multiagent competition scenario is still an issue that is challenging to definitively conclude. In this context, we propose competitive evolution (CompetEvo), which co-evolves agents' designs and tactics in confrontation. We build arenas consisting of three animals and their evolved derivatives, placing agents with different morphologies in direct competition with each other. The results reveal that our method enables agents to evolve a more suitable design and strategy for fighting compared to fixed-morph agents, allowing them to obtain advantages in combat scenarios. Moreover, we demonstrate the amazing and impressive behaviors that emerge when confrontations are conducted under asymmetrical morphs.

Úrsula Hébert-Johnson, Daniel Lokshtanov, Chinmay Sonar, Vaishali Surianarayanan

As of January 2023, there are more than 90,000 people on the national transplant waiting list in need of a kidney in the United States. These patients often have a friend or family member who is willing to donate, but whose kidney type might not be compatible. To help match these patients to suitable donors, patient-donor compatibility can be modeled as a directed graph. Specifically, in the Kidney Exchange problem, the input is a directed graph G, a subset B of vertices (altruistic donors), and two integers l_p and l_c. An altruistic donor is a donor who is not paired with a patient, and the remaining vertices are patient-donor pairs. Whenever a donor is compatible with a patient from a patient-donor pair, we place a directed edge from the donor vertex to the patient-donor pair. Here the donor vertex can be either altruistic or non-altruistic. The goal is to find a collection of vertex-disjoint cycles and paths covering the maximum number of patients such that each cycle has length at most l_c, and such that each path has length at most l_p and begins at a vertex in B. The path and cycle lengths are bounded so that the surgeries for a given path or cycle can be performed simultaneously. Kidney Exchange has received a great deal of attention in recent years. We contribute to this line of work by closing two open problems from IJCAI '18 and IJCAI '22: "Is Kidney Exchange FPT when parameterized by (i) the treewidth (omega) of G and (ii) the number of vertex types in G?'' Two vertices have the same vertex type if they have the same in- and out-neighborhoods. We show that Kidney Exchange is FPT parameterized by the number of vertex types. On the other hand, we show W[1]-hardness with respect to omega. We also design a randomized 4^t * n^O(1)-time algorithm parameterized by t, the number of patients helped, significantly improving upon the previous state of the art, which was 161^t * n^O(1).