The additive manufacturing(AM)landscape has significantly transformed in alignment with Industry 4.0 principles,primarily driven by the integration of artificial intelligence(AI)and digital twins(DT).However,current i...The additive manufacturing(AM)landscape has significantly transformed in alignment with Industry 4.0 principles,primarily driven by the integration of artificial intelligence(AI)and digital twins(DT).However,current intelligent AM(IAM)systems face limitations such as fragmented AI tool usage and suboptimal human-machine interaction.This paper reviews existing IAM solutions,emphasizing control,monitoring,process autonomy,and end-to-end integration,and identifies key limitations,such as the absence of a high-level controller for global decision-making.To address these gaps,we propose a transition from IAM to autonomous AM,featuring a hierarchical framework with four integrated layers:knowledge,generative solution,operational,and cognitive.In the cognitive layer,AI agents notably enable machines to independently observe,analyze,plan,and execute operations that traditionally require human intervention.These capabilities streamline production processes and expand the possibilities for innovation,particularly in sectors like in-space manufacturing.Additionally,this paper discusses the role of AI in self-optimization and lifelong learning,positing that the future of AM will be characterized by a symbiotic relationship between human expertise and advanced autonomy,fostering a more adaptive,resilient manufacturing ecosystem.展开更多
Humanity has fantasized about artificial intelligence tools able to discuss with human beings fluently for decades.Numerous efforts have been proposed ranging from ELIZA to the modern vocal assistants.Despite the larg...Humanity has fantasized about artificial intelligence tools able to discuss with human beings fluently for decades.Numerous efforts have been proposed ranging from ELIZA to the modern vocal assistants.Despite the large interest in this research and innovation field,there is a lack of common understanding on the concept of conversational agents and general over expectations that hide the current limitations of existing solutions.This work proposes a literature review on the subject with a focus on the most promising type of conversational agents that are powered on top of knowledge bases and that can offer the ground knowledge to hold conversation autonomously on different topics.We describe a conceptual architecture to define the knowledge-enhanced conversational agents and investigate different domains of applications.We conclude this work by listing some promising research pathways for future work.展开更多
The expansion of rail transport infrastructures necessitates accurate and efficient soil surveys to ensure long-term stability and performance,particularly in regions prone to soil heaving.This study aimed to demonstr...The expansion of rail transport infrastructures necessitates accurate and efficient soil surveys to ensure long-term stability and performance,particularly in regions prone to soil heaving.This study aimed to demonstrate the potential of non-destructive spectral analysis combined with Agentic Artificial Intelligence for automating the identification of soil heaving potential,providing a transformative approach to soil assessment in railway construction.A robust AI-agent was developed to predict soil heaving potential across temperature regimes(ranging from 0℃ to-5℃ and back),enabling characterization of the relative acoustic compressibility coefficient(β)based on the physical and mechanical properties of the soil.The main objective was to develop a framework that integrated spectral reflectance data with machine learning algorithms to predict soil heaving potential and reduce the reliance on traditional invasive methods.The experimental setup employed digital techniques to process and record longitudinal and transverse acoustic pulse signals reflected from piezoelectric sensors mounted on soil specimens.The processed signals were automatically transferred via a USB adapter to a PC for further analysis by the AI-agent.Acoustic diagnostics of the soils were performed using Fast-Fourier Transform(FFT)Spectral Analysis,followed by correlation of waveform spectra with heaving deformation.The AI-agent utilized a hybrid architecture combining Convolutional Neural Network(CNN),Support Vector Machine(SVM),and Random Forest(RF)algorithms to address the complexities of heterogeneous soil data and multifaceted prediction tasks—including heaving classification and deformation regression—while mitigating overfitting.Soil heaving potential was accurately predicted by the AI agent,with minor variations attributed to equipment sensitivity.展开更多
The booming of artificial intelligence(AI)agents has brought about promising business scenarios for sixth-generation(6G)mobile networks,while simultaneously posing significant challenges to network functionalities and...The booming of artificial intelligence(AI)agents has brought about promising business scenarios for sixth-generation(6G)mobile networks,while simultaneously posing significant challenges to network functionalities and infrastructure.These AI agents can be deployed on end devices(e.g.,intelligent robots and intelligent cars)or as digital entities(e.g.,personal AI assistants).As novel service entities with autonomous decision-making and task execution capabilities,AI agents introduce potential risks of uncontrollable actions and privacy disclosures.AI agents also require new 6G capabilities beyond traditional communication,including multimodality information interaction(e.g.,AI models and tokens)and support for service requirements(e.g.,computing and sensing of data).In this article,we introduce the concept of AI-agent communication network(ACN),a new paradigm to enable global information interaction and on-demand capability provisioning for single or multiple AI agents.We first introduce the vision and architectural framework of ACN.Then,key technologies and future research directions related to ACN are discussed.Furthermore,we provide potential use cases to elaborate on how ACN can expand the service capabilities of 6G networks.展开更多
X-ray diffraction(XRD)is a powerful method for determining a material’s crystal structure in highthroughput experimentation,and is widely being incorporated in artificially intelligent agents for autonomous scientifi...X-ray diffraction(XRD)is a powerful method for determining a material’s crystal structure in highthroughput experimentation,and is widely being incorporated in artificially intelligent agents for autonomous scientific discovery.However,rapid,automated,and reliable analysis of XRD data at rates that match the pace of experimental measurements at a synchrotron source remains a major challenge.To address these issues,we developed CrystalShift for rapid and efficient probabilisticXRD phase labeling employing symmetry-constrained optimization,best-first tree search,and Bayesian model comparison.The algorithm estimates probabilities for phase combinations without requiring additional phase space information or training.We demonstrate that CrystalShift provides robust probability estimates,outperforming existing methods on synthetic and experimental datasets,and can be readily integrated into high-throughput experimental workflows.In addition to efficient phase labeling,CrystalShift offers quantitative insights into materials’structural parameters,which facilitate both expert evaluation and AI-based modeling of the phase space,ultimately accelerating materials identification and discovery.展开更多
Artificial intelligence(AI)agents driven by large language models have demonstrated substantial advancements in addressing complex problem-solving tasks.While current AI agents’collaborations are predominantly organi...Artificial intelligence(AI)agents driven by large language models have demonstrated substantial advancements in addressing complex problem-solving tasks.While current AI agents’collaborations are predominantly organized by centralized platforms,this organizational model inherently introduces systemic challenges such as data monopolies and interoperability barriers,thereby hindering cross-domain collaborative potential.Although current multi-agent collaboration methods have enabled secure data and resource sharing,they still fall short in utilizing distributed knowledge and adopting validatable collaborative workflows.To overcome these limitations,this paper proposes a distributed multi-agent collaboration mechanism leveraging the smart-contracts-based validation method with specifically designed planning,execution and validation smart contracts for robust workflow validation.An illustrative example of decentralized science collaborative writing is provided to demonstrate the applicability of the proposed mechanism.To further bridge the data silos and autonomously update the smart contracts,we employ decentralized autonomous organizations and operations(DAOs)and adopt an auction-based tool supplier matching model to architect the organizing and incentive scheme to enhance selfadaptivity and scalability.To further evaluate the performance of the proposed mechanism,we delineate a hierarchical indicator system and discuss the corresponding key technologies.This paper outlines a smart-contract-based approach to enhance the reliability of multi-agent collaboration,which could serve as a methodological basis for constructing future distributed multiagent collaboration ecosystems.展开更多
基金funded by the MUREP High Volume project(80NSSC22M0132)through the U.S.NASA Office of STEM Engagementthe SMART IAC Project(DE-EE0009726)through the U.S.Department of Energy Office of Manufacturing and Energy Supply Chainssupport of San Diego Supercomputer Center(SDSC)National Research Platform(NRP)Nautilus sponsored by the U.S.NSF(2100237,2120019)。
文摘The additive manufacturing(AM)landscape has significantly transformed in alignment with Industry 4.0 principles,primarily driven by the integration of artificial intelligence(AI)and digital twins(DT).However,current intelligent AM(IAM)systems face limitations such as fragmented AI tool usage and suboptimal human-machine interaction.This paper reviews existing IAM solutions,emphasizing control,monitoring,process autonomy,and end-to-end integration,and identifies key limitations,such as the absence of a high-level controller for global decision-making.To address these gaps,we propose a transition from IAM to autonomous AM,featuring a hierarchical framework with four integrated layers:knowledge,generative solution,operational,and cognitive.In the cognitive layer,AI agents notably enable machines to independently observe,analyze,plan,and execute operations that traditionally require human intervention.These capabilities streamline production processes and expand the possibilities for innovation,particularly in sectors like in-space manufacturing.Additionally,this paper discusses the role of AI in self-optimization and lifelong learning,positing that the future of AM will be characterized by a symbiotic relationship between human expertise and advanced autonomy,fostering a more adaptive,resilient manufacturing ecosystem.
文摘Humanity has fantasized about artificial intelligence tools able to discuss with human beings fluently for decades.Numerous efforts have been proposed ranging from ELIZA to the modern vocal assistants.Despite the large interest in this research and innovation field,there is a lack of common understanding on the concept of conversational agents and general over expectations that hide the current limitations of existing solutions.This work proposes a literature review on the subject with a focus on the most promising type of conversational agents that are powered on top of knowledge bases and that can offer the ground knowledge to hold conversation autonomously on different topics.We describe a conceptual architecture to define the knowledge-enhanced conversational agents and investigate different domains of applications.We conclude this work by listing some promising research pathways for future work.
文摘The expansion of rail transport infrastructures necessitates accurate and efficient soil surveys to ensure long-term stability and performance,particularly in regions prone to soil heaving.This study aimed to demonstrate the potential of non-destructive spectral analysis combined with Agentic Artificial Intelligence for automating the identification of soil heaving potential,providing a transformative approach to soil assessment in railway construction.A robust AI-agent was developed to predict soil heaving potential across temperature regimes(ranging from 0℃ to-5℃ and back),enabling characterization of the relative acoustic compressibility coefficient(β)based on the physical and mechanical properties of the soil.The main objective was to develop a framework that integrated spectral reflectance data with machine learning algorithms to predict soil heaving potential and reduce the reliance on traditional invasive methods.The experimental setup employed digital techniques to process and record longitudinal and transverse acoustic pulse signals reflected from piezoelectric sensors mounted on soil specimens.The processed signals were automatically transferred via a USB adapter to a PC for further analysis by the AI-agent.Acoustic diagnostics of the soils were performed using Fast-Fourier Transform(FFT)Spectral Analysis,followed by correlation of waveform spectra with heaving deformation.The AI-agent utilized a hybrid architecture combining Convolutional Neural Network(CNN),Support Vector Machine(SVM),and Random Forest(RF)algorithms to address the complexities of heterogeneous soil data and multifaceted prediction tasks—including heaving classification and deformation regression—while mitigating overfitting.Soil heaving potential was accurately predicted by the AI agent,with minor variations attributed to equipment sensitivity.
基金Project supported by the National Science and Technology Major Project of China on Mobile Information Networks(No.2024ZD1300400)。
文摘The booming of artificial intelligence(AI)agents has brought about promising business scenarios for sixth-generation(6G)mobile networks,while simultaneously posing significant challenges to network functionalities and infrastructure.These AI agents can be deployed on end devices(e.g.,intelligent robots and intelligent cars)or as digital entities(e.g.,personal AI assistants).As novel service entities with autonomous decision-making and task execution capabilities,AI agents introduce potential risks of uncontrollable actions and privacy disclosures.AI agents also require new 6G capabilities beyond traditional communication,including multimodality information interaction(e.g.,AI models and tokens)and support for service requirements(e.g.,computing and sensing of data).In this article,we introduce the concept of AI-agent communication network(ACN),a new paradigm to enable global information interaction and on-demand capability provisioning for single or multiple AI agents.We first introduce the vision and architectural framework of ACN.Then,key technologies and future research directions related to ACN are discussed.Furthermore,we provide potential use cases to elaborate on how ACN can expand the service capabilities of 6G networks.
基金supported by the Air Force Office of Scientific Research under award number FA9550-18-1-0136This work is based upon research conducted at the Materials Solutions Network at CHESS(MSN-C)which is supported by the Air Force Research Laboratory under award FA8650-19-2-5220+1 种基金Experimental work was performed in part at the Cornell NanoScale Facility,an NNCI member supported by NSF Grant NNCI-2025233Additionally,this research was conducted with support from the Cornell University Center for Advanced Computing.Use of the Stanford Synchrotron Radiation Lightsource,SLAC National Accelerator Laboratory,is supported by the U.S.Department of Energy,Office of Science,Office of Basic Energy Sciences under Contract No.DE-AC02-76SF00515.
文摘X-ray diffraction(XRD)is a powerful method for determining a material’s crystal structure in highthroughput experimentation,and is widely being incorporated in artificially intelligent agents for autonomous scientific discovery.However,rapid,automated,and reliable analysis of XRD data at rates that match the pace of experimental measurements at a synchrotron source remains a major challenge.To address these issues,we developed CrystalShift for rapid and efficient probabilisticXRD phase labeling employing symmetry-constrained optimization,best-first tree search,and Bayesian model comparison.The algorithm estimates probabilities for phase combinations without requiring additional phase space information or training.We demonstrate that CrystalShift provides robust probability estimates,outperforming existing methods on synthetic and experimental datasets,and can be readily integrated into high-throughput experimental workflows.In addition to efficient phase labeling,CrystalShift offers quantitative insights into materials’structural parameters,which facilitate both expert evaluation and AI-based modeling of the phase space,ultimately accelerating materials identification and discovery.
基金supported by the Science and Technology Development Fund,Macao SAR(Nos.0093/2023/RIA2,0157/2024/RIA2,and 0145/2023/RIA3).
文摘Artificial intelligence(AI)agents driven by large language models have demonstrated substantial advancements in addressing complex problem-solving tasks.While current AI agents’collaborations are predominantly organized by centralized platforms,this organizational model inherently introduces systemic challenges such as data monopolies and interoperability barriers,thereby hindering cross-domain collaborative potential.Although current multi-agent collaboration methods have enabled secure data and resource sharing,they still fall short in utilizing distributed knowledge and adopting validatable collaborative workflows.To overcome these limitations,this paper proposes a distributed multi-agent collaboration mechanism leveraging the smart-contracts-based validation method with specifically designed planning,execution and validation smart contracts for robust workflow validation.An illustrative example of decentralized science collaborative writing is provided to demonstrate the applicability of the proposed mechanism.To further bridge the data silos and autonomously update the smart contracts,we employ decentralized autonomous organizations and operations(DAOs)and adopt an auction-based tool supplier matching model to architect the organizing and incentive scheme to enhance selfadaptivity and scalability.To further evaluate the performance of the proposed mechanism,we delineate a hierarchical indicator system and discuss the corresponding key technologies.This paper outlines a smart-contract-based approach to enhance the reliability of multi-agent collaboration,which could serve as a methodological basis for constructing future distributed multiagent collaboration ecosystems.
