Artificial Intelligence Review最新文献

Multi-agent cooperation among Uncrewed Aerial Vehicles (UAVs) has become a key contributor in various application areas such as surveillance, where mission efficiency and reliability are essential. This paper aims to provide a structured work for future progress in cooperative flight by presenting a comprehensive review of the hybrid and modified optimization algorithms developed for multi-agent UAV path planning. Initially, the paper categorizes existing approaches into classical, heuristic, metaheuristic, sampling-based, control-based, and Artificial Intelligence (AI)-based frameworks, with an emphasis on their objectives, constraints, and implementation strategies. Special attention is given to metaheuristic and AI-driven techniques, which demonstrate strong adaptability and scalability in dynamic and uncertain environments. The review further examines the environmental modeling strategies, swarm architectures, and mission types, revealing the predominance of static and known environments in current research and highlighting the limited exploration of dynamic and unknown operational considerations. While performance criteria such as collision avoidance, mission duration, and cumulative path length are commonly assessed, aspects like communication constraints, task allocation, and energy efficiency remain relatively underexplored. Open research challenges and future directions are identified, including the need for real-time adaptive optimization strategies and the incorporation of more realistic agent dynamics to enhance experimental validation and practical deployment.

Text-to-image (T2I) models are rapidly advancing into creative practice and increasingly support generating illustrated storybooks, i.e., sequential and image-based narratives conditioned on written text. Previous surveys have examined challenges in video coherence or single-image fidelity. To our best knowledge, there is no comprehensive review that addresses the unique requirements of storybook illustration. This survey fills this gap by grounding the study of AI-illustrated storybook generation in a narratology framework. Specifically, this survey introduces a six-dimensional consistency model encompassing time, space, character, event and plot, style, and theme. For each dimension, we include consolidate definitions, representative methods, datasets, and evaluation metrics, thereby mapping the current landscape of the field. Building on this analysis, we further identify cross-dimensional failure modes and limitations of current approaches. Finally, we propose potential future research directions, including the development of book-scale integrated evaluation systems tailored for illustrated storybooks, more robust and controllable generation pipelines, enhanced multimodal semantic–visual alignment mechanisms, and the establishment of reader-oriented safety and educational guidelines.

This research proposes a holistic agentic Artificial Intelligence framework that seeks to solve the palpable requirement of autonomy, versatility, and world-scale generative Artificial Intelligence systems. When framing the concept of an agentic Artificial Intelligence as a paradigm shift where Large Language Models are relatively reactive, tool-augmented to a proactive, modular agent that pursues goals in the long term, the research discusses how large language models are becoming an architectural mandate. The proposed research uses a skillful literature synthesis and develops a concept and technical framework that integrates perception, memory, planning, execution, and communication modules. In contrast to current frameworks like AutoGPT and ReAct, where the deep memory, explainability, and certain control of scalability are often lacking, the proposed framework offers a solution with persistent memory layers, semantic routing, and modular orchestration pipelines when it comes to cloud-native deployments. Experimental verification offers a greater degree of autonomy, coordination, and resilience in a diverse range of activities such as enterprise automation and robotics. It is also an edge deployment with the help of lightweight microservices framework. In practice, this method supports scaled, comprehensible agents adapted to long-loop thinking and human-in-the-loop management and adaptive value chain serving. The novelty of the work is attributed to its reusable architecture, as it is not only capable of modifying agentic behavior alone, but it can also connect the theoretical principles and industry feasibility. Future efforts will be with the integration of ethical governance, uniform benchmarking, and multimodal memory remodeling in next generation of real-world autonomous systems.

Super-resolution (SR) imaging is a key task in computer vision, and recent progress has been driven by deep learning. However, manually designed SR networks often suffer from poor generalization, inefficiency, and long development cycles. Neural Architecture Search (NAS) offers an automated paradigm to overcome these limitations. However, its application to SR remains in a nascent stage, presenting significant research gaps such as prohibitive computational costs and the limited generalization of searched architectures. This review summarizes advances of NAS in SR, analyzing its essential components search space, search strategy, and performance evaluation and discussing applications in single image SR, remote sensing, and video SR. Studies show that NAS-based models can achieve competitive or superior performance with lower computational cost compared to handcrafted designs. Specifically, we emphasize the following contributions: (1) a comprehensive analysis of NAS components tailored to SR tasks; (2) a review of NAS applications across various SR domains with demonstrated improvements in performance and efficiency; and (3) identification of these unresolved challenges to outline actionable future directions, including reducing search costs, enhancing cross-domain robustness of lightweight models, and expanding NAS applications in SR-related tasks. This work aims to provide theoretical and methodological insights to support research and practical deployment of NAS in SR imaging.

Structured sentiment analysis (SSA) aims to automatically extract people’s opinions from a text in natural language and adequately represent that information in a graph structure. One of the most accurate methods for performing SSA was recently proposed and consists of approaching it as a dependency graph parsing task. Although we can find in the literature how transition-based algorithms excel in different dependency graph parsing tasks in terms of accuracy and efficiency, all proposed attempts to tackle SSA following that approach were based on graph-based models. In this article, we present the first transition-based method to address SSA as dependency graph parsing. Specifically, we design a transition system that processes the input text in a left-to-right pass, incrementally generating the graph structure containing all identified opinions. To effectively implement our final transition-based model, we resort to a Pointer Network architecture as a backbone. From an extensive evaluation, we demonstrate that our model offers the best performance to date in practically all cases among prior dependency-based methods, and surpasses recent task-specific techniques on the most challenging datasets. We additionally include an in-depth analysis and empirically prove that the average-case time complexity of our approach is quadratic in the sentence length, being more efficient than top-performing graph-based parsers.

This paper presents the first systematic benchmark evaluating Large Language Models (LLMs), specifically GPT-2, GPT-Neo-125M, and LLaMA-3.2-1B, as standalone classifiers for intrusion detection, covering both binary and multiclass classification tasks, using structured Zeek logs derived from the CIC IoT 2023 dataset. We compare their performance against established and widely used Machine Learning (XGBoost, Random Forest, Decision Tree) and Deep Learning models (MLP, GRU, LeNet-5) across key evaluation metrics: detection effectiveness (precision, recall and F1-score), inference speed, and resource consumption. All models are consistently trained and rigorously evaluated on the CIC IoT 2023 dataset, ensuring fair, reproducible, and transparent comparisons. Our findings indicate that while LLMs achieve strong F1-score exceeding 95%, and do not fully utilize available GPU resources, they still do not outperform top-performing ML models. Notably XGBoost achieves a higher F1-score of 96.96%, using only 4% of the available CPU. These results emphasize the practical trade-offs between detection capability, inference efficiency, and hardware requirements when applying LLMs in flow-based IDS contexts, particularly in resource-constrained environments such as IoT or edge deployments.

In recent years, research on the integration of evolutionary multi-objective optimization and (hyper-) heuristics (MOHHs) has significantly grown. This paper presents a comprehensive survey of MOHH research, categorizing existing approaches into four main classes: selection, generation, portfolio, and configuration MOHHs. Each category is analyzed in terms of methodology, key contributions, and open challenges. The analysis reveals an imbalance in research focus, with selection and portfolio MOHHs receiving the most attention, followed by configuration MOHHs, while generation MOHHs remain largely unaddressed. Selection MOHHs are further divided by the hierarchy of components they control: low-level approaches (which typically manage evolutionary operators) require further study on move acceptance methods, whereas mid-level approaches (which typically manage multi-objective evolutionary algorithms) need deeper exploration of selection strategies. Generation MOHHs, primarily based on genetic programming and grammatical evolution, lack investigation into alternative methodologies. Portfolio MOHHs, which produce a set of non-dominated constructive (hyper-) heuristics based on performance trade-offs, have been predominantly applied to combinatorial problems and exhibit limited diversity in the use of MOEAs as underlying optimizers. Configuration MOHHs, which focus on configuring algorithmic components for multi-objective optimizers, have largely relied on a single performance indicator, leaving room for multi-criteria performance approaches. Beyond this, the paper also reviews the test problems and practical applications that have been addressed by MOHHs, and outlines potential avenues for future research in the field.

Group fairness in machine learning is an important area of research focused on achieving equitable outcomes across different groups defined by sensitive attributes such as race or gender. Federated learning, a decentralized approach to training machine learning models across multiple clients, amplifies the need for fairness methodologies due to its inherent heterogeneous data distributions that can exacerbate biases. The intersection of federated learning and group fairness has attracted significant interest, with 48 research works specifically dedicated to addressing this issue. However, no comprehensive survey has specifically focused on group fairness in Federated Learning. In this work, we analyze the key challenges of this topic, propose practices for its identification and benchmarking, and create a novel taxonomy based on criteria such as data partitioning, location, and strategy. Furthermore, we analyze broader concerns, review how different approaches handle the complexities of various sensitive attributes, examine common datasets and applications, and discuss the ethical, legal, and policy implications of group fairness in FL. We conclude by highlighting key areas for future research, emphasizing the need for more methods to address the complexities of achieving group fairness in federated systems.

The rapid urbanization process has presented complex challenges that require innovative strategies to enhance urban living and promote sustainable growth. In this context, the concept of smart cities has quickly evolved, illustrating urban environments that utilize advanced technology to achieve greater efficiency, sustainability, and an improved quality of life for residents. The development of these smart environments relies on technologies like the Internet of Things (IoT), which collects extensive data through sensors, and Artificial Intelligence (AI), for advanced data processing and decision-making. For the latter, while traditional AI solutions have improved urban systems in multiple ways, emerging Generative Artificial Intelligence (GenAI) models signify a new era for smart cities, offering breakthroughs in urban design, simulation, and personalized, context-aware solutions. This article explores the applications, impacts, challenges, and promising future trends of GenAI within the context of smart cities, discussing generative urban intelligence perspectives for simulating alternative urban scenarios, co-designing infrastructure prototypes, and improving service delivery. It provides a pioneering perspective on an underexplored field that is expected to transform urban design, planning, and management.

The relentless growth of connected devices is transforming industrial, urban and domestic environments, yet it also expands the attack surface for distributed denial of service (DDoS), unauthorized access and data manipulation. Centralized security architectures struggle to cope with the scale and heterogeneity of the Internet of Things, creating single points of failure and privacy risks. This review takes a close look at how blockchain and artificial intelligence (AI) can work together to solve these problems. Blockchain plays an important role in decentralizing trust, maintaining data integrity, and enabling transparent audit trails. AI subfields such as machine learning (ML), deep learning (DL), reinforcement learning (RL), and multi-agent systems (MAS) enhance these benefits. They enable real-time anomaly detection, predictive analytics, and adaptive policy control. A seven axis Blockchain–AI Security Integration Schema (BASIS) is proposed to classify solutions by security objectives, intelligence modalities, trust primitives, deployment choices, scalability techniques, privacy controls and interoperability mechanisms. In this study also review Layer-2 consensus protocols, federated learning and lightweight deep learning models that address energy and computational constraints. Case studies from supply chains, healthcare and smart grids illustrate the benefits and limitations of current deployments. The evidence suggests that while AI improves the accuracy and responsiveness of threat detection, blockchain offers tamper-proof data provenance. However, there are still issues in achieving scalability, reducing computational overhead, and striking a balance between auditability and privacy. Hybrid on-chain/off-chain architectures, quantum-safe cryptography, and standardized frameworks to guarantee adoption and interoperability are some future research avenues.