Abstract--The coordination of autonomous agents in dynamic environments is hampered by the semantic gap between high-level mission objectives and low-level planner inputs. T o address this, we introduce a framework centered on a Knowledge Graph (KG) that functions as an intelligent translation layer . The KG's two-plane architecture compiles declarative facts into per-agent, mission-aware "worldviews" and physics-aware traversal rules, decoupling mission semantics from a domain-agnostic planner . This allows complex, coordinated paths to be modified simply by changing facts in the KG. A case study involving Autonomous Underwater V ehicles (AUVs) in the Gulf of Mexico visually demonstrates the end-to-end process and quantitatively proves that different declarative policies produce distinct, high-performing outcomes. This work establishes the KG not merely as a data repository, but as a powerful, stateful orchestrator for creating adaptive and explainable autonomous systems. The effective coordination of autonomous agents, be they robotic vehicles, sensor networks, or even human teams, in dynamic, real-world environments presents a formidable challenge.

This work presents a proof-of-concept implementation of a distributed, in-network reinforcement learning (IN-RL) framework for adaptive path selection in programmable networks. By combining Stochastic Learning Automata (SLA) with real-time telemetry data collected via In-Band Network Telemetry (INT), the proposed system enables local, data-driven forwarding decisions that adapt dynamically to congestion conditions. The system is evaluated on a Mininet-based testbed using P4-programmable BMv2 switches, demonstrating how our SLA-based mechanism converges to effective path selections and adapts to shifting network conditions at line rate.

Similar to the proof of Proposition 12, given the stability constraint in Eq. Eq. (4), we have C 0, l Theorem 14. Multi-agent load balancing is MPG with the VBF Solid and dashed arrows represent deterministic and non-deterministic procedures respectively. Real-world network applications can be CPU-bound or IO-bound [47, 48]. The simulator allows configuring applications that require multi-stage processes switching between CPU/IO queues (Figure 1b). Two different processing models are used for CPU and IO queues, respectively.

The frequency or volume estimation of unending data streams is a concern in many domains, starting with telecommunications but spreading to social networks, finance, and learning-augmented streaming algorithms [10-15] is receiving website engine. In network fields, for example, professionals significant attention due to the powerful potential of machine want to keep track of the activity frequency to identify overall learning (ML) to relieve or eliminate the binding of data network health and potential anomalies or changes in behavior, characteristics and the sketch design. Their typical workflow which, however, is often challenging because the amount of involves training a heavy hitter oracle, which receives a key information may be too large to store in an embedded device and returns a prediction of whether it will be heavy or not, then or to keep conveniently in fast storage [1]. As a consequence, inserts the most frequent keys into unique buckets and applies sketch, which is a set of counters or bitmaps associated with a sketch to the remaining keys. Although filtering heavy items hash functions, and a set of simple operations that record has been proven to improve the overall sketch performance on approximate information [2], has grown in popularity in the heavy-tailed distribution [4, 10], these offline and supervised context of high-velocity data streams and limited computational methods could hardly work in real-world applications.

The emerging programmable networks sparked significant research on Intelligent Network Data Plane (INDP), which achieves learning-based traffic analysis at line-speed. Prior art in INDP focus on deploying tree/forest models on the data plane. We observe a fundamental limitation in tree-based INDP approaches: although it is possible to represent even larger tree/forest tables on the data plane, the flow features that are computable on the data plane are fundamentally limited by hardware constraints. In this paper, we present BoS to push the boundaries of INDP by enabling Neural Network (NN) driven traffic analysis at line-speed. Many types of NNs (such as Recurrent Neural Network (RNN), and transformers) that are designed to work with sequential data have advantages over tree-based models, because they can take raw network data as input without complex feature computations on the fly. However, the challenge is significant: the recurrent computation scheme used in RNN inference is fundamentally different from the match-action paradigm used on the network data plane. BoS addresses this challenge by (i) designing a novel data plane friendly RNN architecture that can execute unlimited RNN time steps with limited data plane stages, effectively achieving line-speed RNN inference; and (ii) complementing the on-switch RNN model with an off-switch transformer-based traffic analysis module to further boost the overall performance. We implement a prototype of BoS using a P4 programmable switch as our data plane, and extensively evaluate it over multiple traffic analysis tasks. The results show that BoS outperforms state-of-the-art in both analysis accuracy and scalability.

Cooperative perception is the key approach to augment the perception of connected and automated vehicles (CAVs) toward safe autonomous driving. However, it is challenging to achieve real-time perception sharing for hundreds of CAVs in large-scale deployment scenarios. In this paper, we propose AdaMap, a new high-scalable real-time cooperative perception system, which achieves assured percentile end-to-end latency under time-varying network dynamics. To achieve AdaMap, we design a tightly coupled data plane and control plane. In the data plane, we design a new hybrid localization module to dynamically switch between object detection and tracking, and a novel point cloud representation module to adaptively compress and reconstruct the point cloud of detected objects. In the control plane, we design a new graph-based object selection method to un-select excessive multi-viewed point clouds of objects, and a novel approximated gradient descent algorithm to optimize the representation of point clouds. We implement AdaMap on an emulation platform, including realistic vehicle and server computation and a simulated 5G network, under a 150-CAV trace collected from the CARLA simulator. The evaluation results show that, AdaMap reduces up to 49x average transmission data size at the cost of 0.37 reconstruction loss, as compared to state-of-the-art solutions, which verifies its high scalability, adaptability, and computation efficiency.

Days and nights have been busy diving deeper into the AI landscape. I've been reading a great book by Tom Taulli called Artificial Intelligence Basics: A Non-Technical Introduction. It's been a huge help in getting down the vocabulary and understanding the background to the technology from the 1950s on. In combination with the book, I've been messing around a lot with Azure's OpenAI Service and looking closely at the infrastructure and security aspects of the service. In my last post I covered the controls available to customers to secure their specific instance of the service.

In the past few years, Deep Reinforcement Learning (DRL) has become a valuable solution to automatically learn efficient resource management strategies in complex networks. In many scenarios, the learning task is performed in the Cloud, while experience samples are generated directly by edge nodes or users. Therefore, the learning task involves some data exchange which, in turn, subtracts a certain amount of transmission resources from the system. This creates a friction between the need to speed up convergence towards an effective strategy, which requires the allocation of resources to transmit learning samples, and the need to maximize the amount of resources used for data plane communication, maximizing users' Quality of Service (QoS), which requires the learning process to be efficient, i.e., minimize its overhead. In this paper, we investigate this trade-off and propose a dynamic balancing strategy between the learning and data planes, which allows the centralized learning agent to quickly converge to an efficient resource allocation strategy while minimizing the impact on QoS. Simulation results show that the proposed method outperforms static allocation methods, converging to the optimal policy (i.e., maximum efficacy and minimum overhead of the learning plane) in the long run.

Traditional multicast routing methods have some problems in constructing a multicast tree, such as limited access to network state information, poor adaptability to dynamic and complex changes in the network, and inflexible data forwarding. To address these defects, the optimal multicast routing problem in software-defined networking (SDN) is tailored as a multi-objective optimization problem, and an intelligent multicast routing algorithm DRL-M4MR based on the deep Q network (DQN) deep reinforcement learning (DRL) method is designed to construct a multicast tree in SDN. First, the multicast tree state matrix, link bandwidth matrix, link delay matrix, and link packet loss rate matrix are designed as the state space of the DRL agent by combining the global view and control of the SDN. Second, the action space of the agent is all the links in the network, and the action selection strategy is designed to add the links to the current multicast tree under four cases. Third, single-step and final reward function forms are designed to guide the intelligence to make decisions to construct the optimal multicast tree. The experimental results show that, compared with existing algorithms, the multicast tree construct by DRL-M4MR can obtain better bandwidth, delay, and packet loss rate performance after training, and it can make more intelligent multicast routing decisions in a dynamic network environment.