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Works382 from public data
- Application of artificial intelligence to wastewater treatment: A bibliometric analysis and systematic review of technology, economy, management, and wastewater reuse440
This study examines the literature from 1995 to 2019 to conduct a large-scale bibliometric analysis of trends in the application of artificial intelligence technology to wastewater treatment to provide perspectives on the potential future directions of new research frontiers in the utilization of artificial Intelligence in wastewater treatment plants that simultaneously address pollutant removal, cost reduction, water reuse, and management challenges in complex practical applications.
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- A Survey on Vision--Language--Action Models for Embodied AI403
This work provides a detailed taxonomy of VLAs, organized into three major lines of research, dedicated to developing VLA-based control policies adept at predicting low-level actions and guiding VLAs to follow more general user instructions.
- Multi-Agent Game Abstraction via Graph Attention Neural Network355
A novel game abstraction mechanism based on two-stage attention network (G2ANet) is proposed, which can indicate whether there is an interaction between two agents and the importance of the interaction and two novel learning algorithms GA-Comm and GA-AC are proposed.
- Exploration in Deep Reinforcement Learning: From Single-Agent to Multiagent Domain207
This article provides a comprehensive and unified empirical comparison of different exploration methods for DRL on a set of commonly used benchmarks and summarizes the open problems of exploration in DRL and deep MARL and point out a few future directions.
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- A survey on interpretable reinforcement learning189
This survey provides an overview of various approaches to achieve higher interpretability in reinforcement learning (RL), and argues that interpretable RL may embrace different facets: interpretable inputs, interpretable (transition/reward) models, and interpretable decision-making.
- An end-to-end heterogeneous graph representation learning-based framework for drug–target interaction prediction179
A novel 'end-to-end' learning-based framework based on heterogeneous 'graph' convolutional networks for 'DTI' prediction called end- to-end graph (EEG)-DTI, which performs better than existing state-of-art methods for identifying DTIs.
- A learning-based framework for miRNA-disease association identification using neural networks178
A novel learning-based framework, MDA-CNN, is proposed, which outperforms some state-of-the-art approaches in a large margin on both tasks of miRNA-disease association prediction and mi RNA-phenotype association prediction.
- From Few to More: Large-Scale Dynamic Multiagent Curriculum Learning147
A novel Dynamic Multiagent Curriculum Learning (DyMA-CL) to solve large-scale problems by starting from learning on a multiagent scenario with a small size and progressively increasing the number of agents, and proposes three transfer mechanisms across curricula to accelerate the learning process.
- Modeling Scale-free Graphs with Hyperbolic Geometry for Knowledge-aware Recommendation117
LKGR facilitates better modeling of scale-free tripartite graphs after the data unification and employs different information propagation strategies in the hyperbolic space to explicitly encode heterogeneous information from historical interactions and KGs.
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- Falsification of Cyber-Physical Systems Using Deep Reinforcement Learning111
State-of-the-art Deep Reinforcement Learning techniques are explored, i.e., Asynchronous Advantage Actor-Critic and Double Deep Q Network (DDQN), to reduce the number of simulation runs required to find such counterexamples and identify two factors of CPS which make DRL based methods better than existing methods.
- Bridging Evolutionary Algorithms and Reinforcement Learning: A Comprehensive Survey on Hybrid Algorithms108
This survey offers a comprehensive overview of the diverse research branches in ERL, systematically summarize the recent advancements in related algorithms and identify three primary research directions: 1) EA-assisted optimization of RL; 2) RL-assisted optimization of EA; and 3) synergistic optimization of EA and RL.
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- Automatic Web Testing Using Curiosity-Driven Reinforcement Learning104
This paper proposes WebExplor, an automatic end-to-end web testing framework that adopts a curiosity-driven reinforcement learning to generate high-quality action sequences (test cases) with temporal logical relations that can achieve significantly higher failure detection rate, code coverage and efficiency than existing state-of-the-art web testing techniques.
- CellAgent: An LLM-driven Multi-Agent Framework for Automated Single-cell Data Analysis98
This work introduces CellAgent, an LLM-driven multi-agent framework, specifically designed for the automatic processing and execution of scRNA-seq data analysis tasks, providing high-quality results with no human intervention.
- Learning to select cuts for efficient mixed-integer programming98
A data-driven and generalizable cut selection approach, named Cut Ranking, in the settings of multiple instance learning, which has been deployed in an industrial solver for large-scale MIPs and has achieved the average speedup ratio of 12.42% over the production solver without any accuracy loss of solution.
- Ordering-Based Causal Discovery with Reinforcement Learning96
This work forms the ordering search problem as a multi-step Markov decision process, implements the ordering generating process with an encoder-decoder architecture, and uses RL to optimize the proposed model based on the reward mechanisms designed for each ordering.
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- A benchmark for automatic medical consultation system: frameworks, tasks and datasets93
This article creates a new large medical dialogue dataset with multi-level fine-grained annotations and establishes five independent tasks, including named entity recognition, dialogue act classification, symptom label inference, medical report generation and diagnosis-oriented dialogue policy.
- SPA-Bench: A Comprehensive Benchmark for SmartPhone Agent Evaluation90
SPA-Bench is presented, a comprehensive SmartPhone Agent Benchmark designed to evaluate (M)LLM-based agents in an interactive environment that simulates real-world conditions and proposes future research directions to ease difficulties, moving closer to real-world smartphone agent applications.
- Deep Multi-Agent Reinforcement Learning with Discrete-Continuous Hybrid Action Spaces89
The empirical results show that both Deep MAPQN and Deep MAHHQN are effective and significantly outperform existing independent deep parameterized Q-learning method.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-14. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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