We examine the tuning of cooperative behavior in repeated multi-agent games using an analytically tractable, continuous-time, nonlinear model of opinion dynamics. Each modeled agent updates its real-valued opinion about each available strategy in response to payoffs and other agents’ opinions, as observed over a network. We show how the model provides a principled and systematic means to investigate behavior of agents that select strategies using rationality and reciprocity, key features of human decision-making in social dilemmas. For two-strategy games, we use bifurcation analysis to prove conditions for the bistability of two equilibria and conditions for the first (second) equilibrium to reflect all agents favoring the first (second) strategy. We prove how model parameters, e.g., level of attention to opinions of others, network structure, and payoffs, influence dynamics and, notably, the size of the region of attraction to each stable equilibrium. We provide insights by examining the tuning of the bistability of mutual cooperation and mutual defection and their regions of attraction for the repeated prisoner’s dilemma and the repeated multi-agent public goods game. Our results generalize to games with more strategies, heterogeneity, and additional feedback dynamics, such as those designed to elicit cooperation or coordination.

In this article, we propose a trajectory planning algorithm that enables autonomous surface vessels to perform socially compliant navigation in a city’s canal. The key idea behind the proposed algorithm is to adopt an optimal control formulation in which the deviation of movements of the autonomous vessel from nominal movements of human-operated vessels is penalized. Consequently, given a pair of origin and destination points, it finds vessel trajectories that resemble those of human-operated vessels. To formulate this, we adopt kernel density estimation (KDE) to build a nominal movement model of human-operated vessels from a prerecorded trajectory dataset, and use a Kullback–Leibler control cost to measure the deviation of the autonomous vessel’s movements from the model. We establish an analogy between our trajectory planning approach and the maximum entropy inverse reinforcement learning (MaxEntIRL) approach to explain how our approach can learn the navigation behavior of human-operated vessels. On the other hand, we distinguish our approach from the MaxEntIRL approach in that it does not require well-defined bases, often referred to as features, to construct its cost function as required in many of inverse reinforcement learning approaches in the trajectory planning context. Through experiments using a dataset of vessel trajectories collected from the automatic identification system, we demonstrate that the trajectories generated by our approach resemble those of human-operated vessels and that using them for canal navigation is beneficial in reducing head-on encounters between vessels and improving navigation safety.

We propose a decentralized, learning-based framework for dynamic coalition formation in Multi-Robot Task Allocation (MRTA). Our approach extends MAPPO by integrating spatial action maps, robot motion planning, intention sharing, and task allocation revision to enable effective and adaptive coalition formation. Extensive simulation studies confirm the effectiveness of our model, enabling each robot to rely solely on local information to learn timely revisions of task selections and form coalitions with other robots to complete collaborative tasks. The results also highlight the proposed framework's ability to handle large robot populations and adapt to scenarios with diverse task sets.

Utilizing heterogeneous mobile sensors to actively gather information improves adaptability and reliability in extended environments. This paper presents a cooperative multi-robot multi-target search and tracking framework aimed at enhancing the efficiency of the heterogeneous sensor network and, consequently, improving overall target tracking accuracy. The concept of normalized unused sensing capacity is introduced to quantify the information a sensor is currently gathering relative to its theoretical maximum. This measurement can be computed using entirely local information and is applicable to various sensor models, distinguishing it from previous literature on the subject. It is then utilized to develop a heuristics distributed coverage control strategy for a heterogeneous sensor network, adaptively balancing the workload based on each sensor's current unused capacity. The algorithm is validated through a series of ROS and MATLAB simulations, demonstrating superior results compared to standard approaches that do not account for heterogeneity or current usage rates.

Optical fibers, the backbone of global transoceanic communications infrastructure, have the potential to support distributed acoustic sensing (DAS) enhanced by pattern recognition algorithms for oceanic soundscape monitoring and animal conservation. This work enhances subsea telecommunication single-mode fibers (SMFs) by using wavelength division multiplexing (WDM) to concurrently transmit communication and DAS signals. We identified orthogonal frequency division multiplexing (OFDM) as the optimal modulation scheme, minimizing interference between DAS and communications. OFDM achieved a signal-to-noise ratio (SNR) of 10.71 dB for DAS, compared to 8.76 dB for on-off keying (OOK), and enabled a data rate of 7.59 Gbps with a bit error rate (BER) of 2.93 × 10 ⁻³ over 1 km of SMF. The system, tested in a water tank with marine animal sounds, achieved over 90% classification accuracy when integrated with a convolutional neural network (CNN), and maintained 85% accuracy in real-world conditions in the Red Sea. These results demonstrate the potential for enhanced oceanic monitoring without the need for installing new subsea monitoring tools.