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1. What’s in a resource gradient? Comparing alternative cues for foraging in dynamic environments via movement, perception, and memory

   https://doi.org/10.1007/s12080-022-00542-0  

   Fagan, William F.; Saborio, Cole; Hoffman, Tyler D.; Gurarie, Eliezer; Cantrell, Robert Stephen; Cosner, Chris (August 2022, Theoretical Ecology)

   Abstract Consumers must track and acquire resources in complex landscapes. Much discussion has focused on the concept of a ‘resource gradient’ and the mechanisms by which consumers can take advantage of such gradients as they navigate their landscapes in search of resources. However, the concept of tracking resource gradients means different things in different contexts. Here, we take a synthetic approach and consider six different definitions of what it means to search for resources based on density or gradients in density. These include scenarios where consumers change their movement behavior based on the density of conspecifics, on the density of resources, and on spatial or temporal gradients in resources. We also consider scenarios involving non-local perception and a form of memory. Using a continuous space, continuous time model that allows consumers to switch between resource-tracking and random motion, we investigate the relative performance of these six different strategies. Consumers’ success in matching the spatiotemporal distributions of their resources differs starkly across the six scenarios. Movement strategies based on perception and response to temporal (rather than spatial) resource gradients afforded consumers with the best opportunities to match resource distributions. All scenarios would allow for optimization of resource-matching in terms of the underlying parameters, providing opportunities for evolutionary adaptation, and links back to classical studies of foraging ecology. 

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2. Distributed Multi-Agent Bayesian Optimization for Unknown Design Space Exploration

   https://doi.org/10.1115/DETC2024-143377  

   Chen, Siyu; Bayrak, Alparslan Emrah; Sha, Zhenghui (August 2024, American Society of Mechanical Engineers)

   In multi-agent Bayesian optimization for Design Space Exploration (DSE), identifying a communication network among agents to share useful design information for enhanced cooperation and performance, considering the trade-off between connectivity and cost, poses significant challenges. To address this challenge, we develop a distributed multi-agent Bayesian optimization (DMABO) framework and study how communication network structures/connectivity and the resulting cost would impact the performance of a team of agents when finding the global optimum. Specifically, we utilize Lloyd’s algorithm to partition the design space to assign distinct regions to individual agents for exploration in the distributed multi-agent system (MAS). Based on this partitioning, we generate communication networks among agents using two models: 1) a range-limited model of communication constrained by neighborhood information; and 2) a range-free model without neighborhood constraints. We introduce network density as a metric to quantify communication costs. Then, we generate communication networks by gradually increasing the network density to assess the impact of communication costs on the performance of MAS in DSE. The experimental results show that the communication network based on the range-limited model can significantly improve performance without incurring high communication costs. This indicates that increasing the density of a communication network does not necessarily improve MAS performance in DSE. Furthermore, the results indicate that communication is only beneficial for team performance if it occurs between specific agents whose search regions are critically relevant to the location of the global optimum. The proposed DMABO framework and the insights obtained can help identify the best trade-off between communication structure and cost for MAS in unknown design space exploration. 

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3. Action Fluency Facilitates Perceptual Discrimination

   https://doi.org/10.1177/0956797619859361  

   Guo, Jianfei; Song, Joo-Hyun (September 2019, Psychological Science)

   Perception and action interact in nearly every moment of daily life. Previous studies have demonstrated not only that perceptual input shapes action but also that various factors associated with action—including individual abilities and biomechanical costs—influence perceptual decisions. However, it is unknown how action fluency affects the sensitivity of early-stage visual perception, such as orientation. To address this question, we used a dual-task paradigm: Participants prepared an action (e.g., grasping), while concurrently performing an orientation-change-detection task. We demonstrated that as actions became more fluent (e.g., as grasping errors decreased), perceptual-discrimination performance also improved. Importantly, we found that grasping training prior to discrimination enhanced subsequent perceptual sensitivity, supporting the notion of a reciprocal relation between perception and action. 

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4. Lévy movements and a slowly decaying memory allow efficient collective learning in groups of interacting foragers

   https://doi.org/10.1371/journal.pcbi.1011528  

   Falcón-Cortés, Andrea; Boyer, Denis; Aldana, Maximino; Ramos-Fernández, Gabriel (October 2023, PLOS Computational Biology)

   Martinez-Garcia, Ricardo (Ed.)

   Many animal species benefit from spatial learning to adapt their foraging movements to the distribution of resources. Learning involves the collection, storage and retrieval of information, and depends on both the random search strategies employed and the memory capacities of the individual. For animals living in social groups, spatial learning can be further enhanced by information transfer among group members. However, how individual behavior affects the emergence of collective states of learning is still poorly understood. Here, with the help of a spatially explicit agent-based model where individuals transfer information to their peers, we analyze the effects on the use of resources of varying memory capacities in combination with different exploration strategies, such as ordinary random walks and Lévy flights. We find that individual Lévy displacements associated with a slow memory decay lead to a very rapid collective response, a high group cohesion and to an optimal exploitation of the best resource patches in static but complex environments, even when the interaction rate among individuals is low. 

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5. TAVIC-DAS: Task and Channel-Aware Variable-Rate Image Compression for Distributed Autonomous System

   https://doi.org/10.1109/PerComWorkshops65533.2025.00074  

   Shinde, Gaurav; Ravi, Anuradha; Dey, Emon; Lewis, Jared; Roy, Nirmalya (March 2025, IEEE)

   In network-constrained environments, distributed multi-agent systems—such as UGVs and UAVs—must communicate effectively to support computationally demanding scene perception tasks like semantic and instance segmentation. These tasks are challenging because they require high accuracy even when using low-quality images, and the network limitations restrict the amount of data that can be transmitted between agents. To overcome the above challenges, we propose TAVIC-DAS to perform a task and channel-aware variable-rate image compression to enable distributed task execution and minimize communication latency by transmitting compressed images. TAVIC-DAS proposes a novel image compression and decompression framework (distributed across agents) that integrates channel parameters such as RSSI and data rate into a task-specific "semantic segmentation" DNN to generate masks representing the object of interest in the scene (ROI maps) by determining a high pixel density needed to represent objects of interest and low density to represents surrounding pixels within an image. Additionally, to accommodate agents with limited computational resources, TAVIC-DAS incorporates resource-aware model quantization. We evaluated TAVIC-DAS on platforms such as ROSMaster X3 and Jetson Xavier, which communicated using a low-frequency proprietary Doodle radio operating at 915 MHz. The experimental results show that TAVIC-DAS achieves approximately 7.62% higher PSNR and is about 6.39% more resource efficient compared to state-of-the-art techniques. 

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