Related papers: Cooperative Resilience in Artificial Intelligence Multiagent Systems

Related papers

Multi-Agent Collaborative Intrusion Detection for Low-Altitude Economy IoT: An LLM-Enhanced Agentic AI Framework [60.72591149679355]
The rapid expansion of low-altitude economy Internet of Things (LAE-IoT) networks has created unprecedented security challenges.<n>Traditional intrusion detection systems fail to tackle the unique characteristics of aerial IoT environments.<n>We introduce a large language model (LLM)-enabled agentic AI framework for enhancing intrusion detection in LAE-IoT networks.
arXiv Detail & Related papers (2026-01-25T12:47:25Z)
Agentic Reasoning for Large Language Models [122.81018455095999]
Reasoning is a fundamental cognitive process underlying inference, problem-solving, and decision-making.<n>Large language models (LLMs) demonstrate strong reasoning capabilities in closed-world settings, but struggle in open-ended and dynamic environments.<n>Agentic reasoning marks a paradigm shift by reframing LLMs as autonomous agents that plan, act, and learn through continual interaction.
arXiv Detail & Related papers (2026-01-18T18:58:23Z)
Logic-Driven Semantic Communication for Resilient Multi-Agent Systems [26.964933264412412]
6G networks are accelerating autonomy and intelligence in large-scale, decentralized multi-agent systems.<n>This article proposes a formal definition of MAS resilience grounded in two complementary dimensions.<n>We design an agent architecture and develop decentralized algorithms to achieve both epistemic and action resilience.
arXiv Detail & Related papers (2026-01-11T00:54:09Z)
Bio-inspired Agentic Self-healing Framework for Resilient Distributed Computing Continuum Systems [4.003029907200818]
ReCiSt is a bio-inspired agentic self-healing framework designed to achieve resilience in Distributed Computing Continuum Systems (DCCS)<n>ReCiSt reconstructs the biological phases of Hemostasis, Inflammation, Proliferation, and Remodeling into the computational layers Containment, Diagnosis, Meta-Cognitive, and Knowledge for DCCS.<n>These four layers perform autonomous fault isolation, causal diagnosis, adaptive recovery, and long-term knowledge consolidation through Language Model (LM)-powered agents.
arXiv Detail & Related papers (2026-01-01T13:30:38Z)
The Landscape of Agentic Reinforcement Learning for LLMs: A Survey [103.32591749156416]
The emergence of agentic reinforcement learning (Agentic RL) marks a paradigm shift from conventional reinforcement learning applied to large language models (LLM RL)<n>This survey formalizes this conceptual shift by contrasting the degenerate single-step Markov Decision Processes (MDPs) of LLM-RL with the temporally extended, partially observable Markov decision processes (POMDPs) that define Agentic RL.
arXiv Detail & Related papers (2025-09-02T17:46:26Z)
Multimodal AI Systems for Enhanced Laying Hen Welfare Assessment and Productivity Optimization [0.0]
Future of poultry production depends on replacing subjective, labor-intensive welfare checks with data-driven, intelligent monitoring ecosystems.<n>Traditional welfare assessments-limited by human observation and single-sensor data-cannot fully capture the complex, multidimensional nature of laying hen welfare in modern farms.<n>This work lays the foundation for a transition from reactive, unimodal monitoring to proactive, precision-driven welfare systems that unite productivity with ethical, science based animal care.
arXiv Detail & Related papers (2025-08-11T05:17:16Z)
A Survey of Self-Evolving Agents: On Path to Artificial Super Intelligence [87.08051686357206]
Large Language Models (LLMs) have demonstrated strong capabilities but remain fundamentally static.<n>As LLMs are increasingly deployed in open-ended, interactive environments, this static nature has become a critical bottleneck.<n>This survey provides the first systematic and comprehensive review of self-evolving agents.
arXiv Detail & Related papers (2025-07-28T17:59:05Z)
Situationally-Aware Dynamics Learning [57.698553219660376]
We propose a novel framework for online learning of hidden state representations.<n>Our approach explicitly models the influence of unobserved parameters on both transition dynamics and reward structures.<n>Experiments in both simulation and real world reveal significant improvements in data efficiency, policy performance, and the emergence of safer, adaptive navigation strategies.
arXiv Detail & Related papers (2025-05-26T06:40:11Z)
PolicyEvol-Agent: Evolving Policy via Environment Perception and Self-Awareness with Theory of Mind [9.587070290189507]
PolicyEvol-Agent is a comprehensive framework characterized by systematically acquiring intentions of others. PolicyEvol-Agent integrates a range of cognitive operations with Theory of Mind alongside internal and external perspectives.
arXiv Detail & Related papers (2025-04-20T06:43:23Z)
On the Definition of Robustness and Resilience of AI Agents for Real-time Congestion Management [0.0]
The European Union's Artificial Intelligence (AI) Act defines robustness, resilience, and security requirements for high-risk sectors. This paper introduces a novel framework for quantitatively evaluating the robustness and resilience of reinforcement learning agents in congestion management.
arXiv Detail & Related papers (2025-04-17T20:01:48Z)
Collaborative Value Function Estimation Under Model Mismatch: A Federated Temporal Difference Analysis [55.13545823385091]
Federated reinforcement learning (FedRL) enables collaborative learning while preserving data privacy by preventing direct data exchange between agents. In real-world applications, each agent may experience slightly different transition dynamics, leading to inherent model mismatches. We show that even moderate levels of information sharing can significantly mitigate environment-specific errors.
arXiv Detail & Related papers (2025-03-21T18:06:28Z)
Identifying Trustworthiness Challenges in Deep Learning Models for Continental-Scale Water Quality Prediction [64.4881275941927]
We present the first comprehensive evaluation of trustworthiness in a continental-scale multi-task LSTM model. Our investigation uncovers systematic patterns of model performance disparities linked to basin characteristics. This work serves as a timely call to action for advancing trustworthy data-driven methods for water resources management.
arXiv Detail & Related papers (2025-03-13T01:50:50Z)
Reflection-Bench: Evaluating Epistemic Agency in Large Language Models [10.801745760525838]
Epistemic agency is the ability to flexibly construct, adapt, and monitor beliefs about dynamic environments.<n>We propose Reflection-Bench, a benchmark consisting of seven tasks with long-term relevance and minimization of data leakage.<n>Our findings suggest several promising research directions, including enhancing core cognitive functions, improving cross-functional coordination, and developing adaptive processing mechanisms.
arXiv Detail & Related papers (2024-10-21T17:59:50Z)
Multidimensional Human Activity Recognition With Large Language Model: A Conceptual Framework [0.0]
In high-stake environments like emergency response or elder care, the integration of large language model (LLM) revolutionizes risk assessment, resource allocation, and emergency responses. We propose a conceptual framework that utilizes various wearable devices, each considered as a single dimension, to support a multidimensional learning approach within Human Activity Recognition (HAR) systems.
arXiv Detail & Related papers (2024-09-16T21:36:23Z)
Sample-Efficient Robust Multi-Agent Reinforcement Learning in the Face of Environmental Uncertainty [40.55653383218379]
This work focuses on learning in distributionally robust Markov games (RMGs) We propose a sample-efficient model-based algorithm (DRNVI) with finite-sample complexity guarantees for learning robust variants of various notions of game-theoretic equilibria.
arXiv Detail & Related papers (2024-04-29T17:51:47Z)
Entropy-Regularized Token-Level Policy Optimization for Language Agent Reinforcement [67.1393112206885]
Large Language Models (LLMs) have shown promise as intelligent agents in interactive decision-making tasks. We introduce Entropy-Regularized Token-level Policy Optimization (ETPO), an entropy-augmented RL method tailored for optimizing LLMs at the token level. We assess the effectiveness of ETPO within a simulated environment that models data science code generation as a series of multi-step interactive tasks.
arXiv Detail & Related papers (2024-02-09T07:45:26Z)
Offline Risk-sensitive RL with Partial Observability to Enhance Performance in Human-Robot Teaming [1.3980986259786223]
We propose a method to incorporate model uncertainty, thus enabling risk-sensitive sequential decision-making. Experiments were conducted with a group of twenty-six human participants within a simulated robot teleoperation environment.
arXiv Detail & Related papers (2024-02-08T14:27:34Z)
HAZARD Challenge: Embodied Decision Making in Dynamically Changing Environments [93.94020724735199]
HAZARD consists of three unexpected disaster scenarios, including fire, flood, and wind. This benchmark enables us to evaluate autonomous agents' decision-making capabilities across various pipelines.
arXiv Detail & Related papers (2024-01-23T18:59:43Z)
Multi-Agent Dynamic Relational Reasoning for Social Robot Navigation [50.01551945190676]
Social robot navigation can be helpful in various contexts of daily life but requires safe human-robot interactions and efficient trajectory planning. We propose a systematic relational reasoning approach with explicit inference of the underlying dynamically evolving relational structures. We demonstrate its effectiveness for multi-agent trajectory prediction and social robot navigation.
arXiv Detail & Related papers (2024-01-22T18:58:22Z)
AntEval: Evaluation of Social Interaction Competencies in LLM-Driven Agents [65.16893197330589]
Large Language Models (LLMs) have demonstrated their ability to replicate human behaviors across a wide range of scenarios. However, their capability in handling complex, multi-character social interactions has yet to be fully explored. We introduce the Multi-Agent Interaction Evaluation Framework (AntEval), encompassing a novel interaction framework and evaluation methods.
arXiv Detail & Related papers (2024-01-12T11:18:00Z)
Feature Interaction Aware Automated Data Representation Transformation [27.26916497306978]
We develop a hierarchical reinforcement learning structure with cascading Markov Decision Processes to automate feature and operation selection. We reward agents based on the interaction strength between selected features, resulting in intelligent and efficient exploration of the feature space that emulates human decision-making.
arXiv Detail & Related papers (2023-09-29T06:48:16Z)
Ensemble Differential Evolution with Simulation-Based Hybridization and Self-Adaptation for Inventory Management Under Uncertainty [0.0]
This study proposes an Ensemble Differential Evolution with Simula-tion-Based Hybridization and Self-Adaptation (EDESH-SA) approach for inven-tory management (IM) under uncertainty.
arXiv Detail & Related papers (2023-09-22T13:25:58Z)
Efficient Empowerment Estimation for Unsupervised Stabilization [75.32013242448151]
empowerment principle enables unsupervised stabilization of dynamical systems at upright positions. We propose an alternative solution based on a trainable representation of a dynamical system as a Gaussian channel. We show that our method has a lower sample complexity, is more stable in training, possesses the essential properties of the empowerment function, and allows estimation of empowerment from images.
arXiv Detail & Related papers (2020-07-14T21:10:16Z)
Towards Understanding the Adversarial Vulnerability of Skeleton-based Action Recognition [133.35968094967626]
Skeleton-based action recognition has attracted increasing attention due to its strong adaptability to dynamic circumstances. With the help of deep learning techniques, it has also witnessed substantial progress and currently achieved around 90% accuracy in benign environment. Research on the vulnerability of skeleton-based action recognition under different adversarial settings remains scant.
arXiv Detail & Related papers (2020-05-14T17:12:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.