Cognitive Systems Research最新文献_第8页

Trust, distrust, and appropriate reliance in (X)AI: A conceptual clarification of user trust and survey of its empirical evaluation (X)人工智能中的信任、不信任和适当依赖：用户信任的概念澄清及其实证评估调查

IF 2.1 3区心理学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Cognitive Systems Research

Pub Date : 2025-04-27 DOI: 10.1016/j.cogsys.2025.101357

Roel Visser , Tobias M. Peters , Ingrid Scharlau , Barbara Hammer

A current concern in the field of Artificial Intelligence (AI) is to ensure the trustworthiness of AI systems. The development of explainability methods is one prominent way to address this, which has often resulted in the assumption that the use of explainability will lead to an increase in the trust of users and wider society. However, the dynamics between explainability and trust are not well established and empirical investigations of their relation remain mixed or inconclusive.

In this paper we provide a detailed description of the concepts of user trust and distrust in AI and their relation to appropriate reliance. For that we draw from the fields of machine learning, human–computer interaction, and the social sciences. Based on these insights, we have created a focused study of empirical literature of existing empirical studies that investigate the effects of AI systems and XAI methods on user (dis)trust, in order to substantiate our conceptualization of trust, distrust, and reliance. With respect to our conceptual understanding we identify gaps in existing empirical work. With clarifying the concepts and summarizing the empirical studies, we aim to provide researchers, who examine user trust in AI, with an improved starting point for developing user studies to measure and evaluate the user’s attitude towards and reliance on AI systems.

如何保证人工智能系统的可信赖性是当前人工智能领域关注的一个问题。可解释性方法的发展是解决这一问题的一个突出方法，这往往导致假设使用可解释性将导致用户和更广泛社会的信任增加。然而，可解释性和信任之间的动态关系并没有很好地建立起来，对它们之间关系的实证调查仍然是混合的或不确定的。本文详细描述了人工智能中用户信任和不信任的概念以及它们与适当依赖的关系。为此，我们从机器学习、人机交互和社会科学等领域汲取经验。基于这些见解，我们对现有实证研究的实证文献进行了重点研究，这些研究调查了人工智能系统和XAI方法对用户（不信任）信任的影响，以证实我们对信任、不信任和依赖的概念化。就我们的概念理解而言，我们确定了现有实证工作中的差距。通过澄清概念和总结实证研究，我们旨在为研究人工智能用户信任的研究人员提供一个改进的起点，以开展用户研究，以衡量和评估用户对人工智能系统的态度和依赖。

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引用次数: 0

The interplay of hot and cool executive functions: Implications for a unified executive framework 冷热执行职能的相互作用：对统一执行框架的启示

IF 2.1 3区心理学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Cognitive Systems Research

Pub Date : 2025-04-21 DOI: 10.1016/j.cogsys.2025.101360

Stjepan Sambol, Emra Suleyman, Michelle Ball

Executive functions (EFs) are integral to emotion regulation, yet current models often overlook the interactions between hot and cool EFs. This study aimed to develop a new framework by incorporating both hot and cool EF constructs. A sample of 150 participants (18–58 years, M = 25.87; SD = 7.48) completed assessments of hot EF (Iowa Gambling Task, Columbia Card Task, and the Understanding Emotions branch of the MSCEIT) and cool EF (Digit Span, N-Back, Keep Track, Modified Card Sorting Task, Colour-Shape Task, and Stop-Signal Task). A confirmatory factor analysis (CFA) tested a three-factor model: hot EF, working memory, and cognitive flexibility. Results partially supported this structure: while a robust working memory factor emerged and the hot EF construct was upheld, albeit with weaker loadings, item loadings for the cognitive flexibility factor were non-significant, indicating inadequate measurement. The hot EF and working memory latent factors were shown to be significantly related, supporting notions that hot and cool EFs interact. Subsequent regression analyses revealed that only the cognitive flexibility factor significantly predicted planning performance on later, more difficult Tower of Hanoi trials. This pattern suggests that demanding planning tasks appear to rely particularly on the cognitive flexibility aspect of avoiding perseverative errors—a relationship largely driven by the MCST. However, because the cognitive flexibility factor was generally weakly measured, these findings should be interpreted with caution. Ultimately, integrating hot EF into EF assessments remains essential, yet existing hot EF tasks must be further refined to more accurately reflect real-world executive demands.

执行功能（ef）是情绪调节不可或缺的一部分，但目前的模型往往忽略了热执行功能和冷执行功能之间的相互作用。本研究旨在通过结合热EF和冷EF结构来开发一个新的框架。150名参与者(18-58岁，M = 25.87；SD = 7.48)完成热EF（爱荷华赌博任务、哥伦比亚卡片任务和理解情绪分支）和冷EF（数字广度、N-Back、跟踪、修改卡片分类任务、颜色-形状任务和停止信号任务）的评估。验证性因子分析（CFA）测试了三因素模型：热EF，工作记忆和认知灵活性。结果部分支持这一结构：虽然出现了一个强大的工作记忆因素和热EF结构，但在较弱的负荷下，认知灵活性因素的项目负荷不显著，表明测量不充分。热EF和工作记忆潜因子显著相关，支持热EF和冷EF相互作用的观点。随后的回归分析显示，只有认知灵活性因素显著地预测了后来，更困难的河内塔试验的规划绩效。这种模式表明，要求高的计划任务似乎特别依赖于避免持续性错误的认知灵活性方面——一种主要由MCST驱动的关系。然而，由于认知灵活性因素通常测量较弱，因此这些发现应谨慎解释。最终，将热EF集成到EF评估中仍然是必要的，但是现有的热EF任务必须进一步细化，以更准确地反映现实世界的执行需求。

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引用次数: 0

Hidden rules of visual perception in the Rorschach test 罗夏测验中视觉感知的隐藏规则

IF 2.1 3区心理学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Cognitive Systems Research

Pub Date : 2025-04-20 DOI: 10.1016/j.cogsys.2025.101359

Stanislav Matousek , Pavel Skobrtal , Radim Badosek

Despite the ongoing controversy, the Rorschach remains one of the most popular tests among professionals conducting personality assessments. The present study focuses on using eye tracking to obtain gaze density maps of 20 healthy participants on standardized visual material, offering insight into the patterns of visual attention distribution during the examination of standard Rorschach inkblots. Specifically, our analysis shows that gaze density confirms our empirical experience and exhibits considerable symmetry around the central axis of symmetry of the inkblots. In addition, inkblots commonly interpreted as depicting human or animal figures show increased human attention to the ‘head’ and upper ‘torso’ regions. These hypotheses are supported by the significance testing results, except the symmetry hypothesis, which suggests considerable but incomplete symmetry. Interpreting these findings in the context of current cognitive research, we consider them evidence for an evolutionarily rooted system governing automatic perception and attention. These findings could enhance psychologists’ understanding of their responses to the Rorschach test. They may also help in the development of more efficient systems, particularly in the field of artificial intelligence learning. This approximation of knowledge regarding human behavior in visuomotor preferences could also benefit other disciplines.

尽管争议不断，罗夏墨迹测验仍然是从事人格评估的专业人士中最受欢迎的测试之一。本研究主要利用眼动追踪技术获取20名健康受试者在标准化视觉材料上的注视密度图，以深入了解标准罗夏墨迹测试期间的视觉注意分布模式。具体来说，我们的分析表明，凝视密度证实了我们的经验经验，并且在墨点对称的中轴线周围表现出相当大的对称性。此外，通常被解释为描绘人类或动物形象的墨迹表明，人类对“头部”和上部“躯干”区域的关注有所增加。这些假设都得到了显著性检验结果的支持，除了对称性假设，它表明相当大的但不完全的对称性。在当前认知研究的背景下解释这些发现，我们认为它们是控制自动感知和注意的进化根源系统的证据。这些发现可以增强心理学家对他们对罗夏测试反应的理解。它们还可能有助于开发更高效的系统，特别是在人工智能学习领域。这种关于视觉运动偏好中人类行为的近似知识也可以使其他学科受益。

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引用次数: 0

Multimodal metaphor recognition based on chain-of-cognition prompting 基于认知链提示的多模态隐喻识别

IF 2.1 3区心理学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Cognitive Systems Research

Pub Date : 2025-04-04 DOI: 10.1016/j.cogsys.2025.101356

Dongyu Zhang , Xingyuan Lu , Mulin Zhuang , Senqi Yang , Hongjun Chen

Metaphor is a way of thinking and cognition prevalent in human language. With the development of social media and multimodal data, metaphor recognition research has expanded from the traditional unimodal scope (such as text or images) to the multimodality. However, current multimodal metaphor processing methods mainly focus on fusion techniques for multiple modalities such as text and image, but neglect the cognitive mechanism of metaphor as a way of thinking, and are deficient in utilizing pre-trained information from large language models. Therefore, this paper proposes a chain-of-cognition prompting (CoC) method to address multimodal metaphor recognition task, which makes full use of the pre-training information of the large model in order to better recognize metaphors. The method utilizes prompting words to construct inputs that guide the large language model to reason about potential metaphorical source and target domain related entities and associations between entities in the sample. At the same time, visual information is obtained through image caption extraction and a visual encoder to enable the model to reason and produce metaphor recognition results. The experimental results show that the method performs well on the metaphor recognition task, which is better than the existing baseline model, verifying the effectiveness of the method on the metaphor recognition task.

隐喻是人类语言中普遍存在的一种思维和认知方式。随着社交媒体和多模态数据的发展，隐喻识别研究已经从传统的单模态范围（如文本或图像）扩展到多模态。然而，目前的多模态隐喻处理方法主要侧重于文本和图像等多模态的融合技术，而忽视了隐喻作为一种思维方式的认知机制，缺乏对大语言模型预训练信息的利用。因此，本文提出了一种认知链提示（CoC）方法来解决多模态隐喻识别任务，充分利用大模型的预训练信息来更好地识别隐喻。该方法利用提示词构建输入，引导大型语言模型推断样本中潜在的隐喻源和目标领域相关实体以及实体之间的关联。同时，通过图像标题提取和视觉编码器获得视觉信息，使模型能够推理并产生隐喻识别结果。实验结果表明，该方法在隐喻识别任务上表现良好，优于现有的基线模型，验证了该方法在隐喻识别任务上的有效性。

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引用次数: 0

Eliciting metaknowledge in Large Language Models 大型语言模型中元知识的提取

IF 2.1 3区心理学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Cognitive Systems Research

Pub Date : 2025-04-01 DOI: 10.1016/j.cogsys.2025.101352

Carmelo Fabio Longo , Misael Mongiovì , Luana Bulla , Antonio Lieto

The introduction of Large Language Models (LLMs) able to exhibit a number of linguistic and extra-linguistic capabilities has represented, in the last years, one of the main frontiers in Artificial Intelligence (AI) research. Researcher from various disciplines debate about whether or not, among the capabilities of LLMs, there is the one of using knowledge about knowledge – usually considered one of the antechambers of meta-cognition in cognitive agents – about a particular task in order to improve or self-correct previous errors. In this work we propose a novel fine-tuning approach for LLMs, named exar, based on a multi-stage process leveraging past predictions from an early version of the same, and aimed at injecting metacognitive features for the task of Question-Answering. The conducted experiments on Llama-2-7B-chat showed promising improvements on the quality of the outcomes, due to the fact that the LLM acquired the ability to detect its own wrong predictions forcing itself to repeat submissions, thorough a prompt designed to fix inadmissible predictions, whenever detected. Such detection is achieved by enquiring the same LLM acting as meta-validator, through another prompt specifically designed for such purpose.

在过去的几年里，能够展示多种语言和语言外能力的大型语言模型（llm）的引入已经成为人工智能（AI）研究的主要前沿之一。来自不同学科的研究人员争论法学硕士的能力中是否有利用关于知识的知识的能力——通常被认为是认知代理中元认知的前厅之一——关于特定任务以改进或自我纠正先前的错误。在这项工作中，我们提出了一种新的llm微调方法，名为exar，基于多阶段过程，利用来自早期版本的过去预测，旨在为问答任务注入元认知特征。在Llama-2-7B-chat上进行的实验显示，结果的质量有了很大的提高，因为LLM获得了检测自己错误预测的能力，迫使自己重复提交，在检测到错误预测时，通过提示来修复不可接受的预测。这种检测是通过查询作为元验证器的同一LLM来实现的，通过专门为此目的设计的另一个提示符。

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引用次数: 0

A general framework for reinforcement learning in cognitive architectures 认知架构中强化学习的一般框架

IF 2.1 3区心理学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Cognitive Systems Research

Pub Date : 2025-04-01 DOI: 10.1016/j.cogsys.2025.101354

Gustavo Morais , Eduardo Yuji , Paula Costa , Alexandre Simões , Ricardo Gudwin , Esther Colombini

Recent advancements in reinforcement learning (RL), particularly deep RL, show the capacity of this paradigm to perform varied and complex tasks. However, a series of exploration, generalization, and adaptation challenges hold RL back from operating in more general contexts. In this paper, we explore integrating techniques originating from cognitive research into existing RL algorithms by defining a general framework to standardize interoperation between arbitrary cognitive modules and arbitrary RL techniques. We show the potential of hybrid approaches through a comparative experiment that integrates an episodic memory encoder with a well-known deep RL algorithm. Furthermore, we show that built-in RL algorithms with different cognitive modules can fit our framework, as well as remotely run algorithms. Hence, we propose a way forward for RL in the form of innovative solutions that integrate research in cognitive systems with recent RL techniques.

强化学习（RL）的最新进展，特别是深度强化学习，显示了这种范式执行各种复杂任务的能力。然而，一系列的探索、推广和适应挑战阻碍了强化学习在更一般的环境中运作。在本文中，我们通过定义一个通用框架来标准化任意认知模块和任意强化学习技术之间的互操作，探索将源自认知研究的技术整合到现有的强化学习算法中。我们通过一个比较实验展示了混合方法的潜力，该实验将情景记忆编码器与著名的深度强化学习算法集成在一起。此外，我们表明，具有不同认知模块的内置强化学习算法可以适合我们的框架，以及远程运行的算法。因此，我们以创新解决方案的形式提出了强化学习的前进方向，将认知系统的研究与最新的强化学习技术相结合。

引用次数: 0

A graph reinforcement LearningPowered Online-Computational task offloading and latency minimization framework for wireless mobile edge computing networks 一种面向无线移动边缘计算网络的图强化学习在线计算任务卸载和延迟最小化框架

IF 2.1 3区心理学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Cognitive Systems Research

Pub Date : 2025-03-23 DOI: 10.1016/j.cogsys.2025.101355

Akshat Agrawal , Aayush Agrawal , Nilesh Kumar Verma , Arepalli Peda Gopi , K. Jairam Naik

Data processing capability of lower power networks can be improved by Mobile Edge Computing (MEC) extending to the wireless sensor networks and IoT. Creating a replication of MEC network with an offloading policy where a choice is made in the Wireless devices (WDs) for each computation task is the focus of this study. Deciding whether the task execution proceeds locally in the same environment or can be handed over to a remote MEC server, an optimized algorithm is needed which adopts task offloading decisions and wireless resource allocation in real time. But adopting this is a challenging solution to the real time fast combinatorial optimization problems, and impossible with the available traditional approaches. As a solution, heuristic algorithms encompassing Deep reinforcement learning (DRL) are emerging; however, it doesn’t make fair use of connection data like device-to-device interaction in MEC network. Moreover, heuristic algorithms rely on precise mathematical models for MEC systems which brought a new theory to the stage. This study revolves around this emerging technique relying on Graph neural networks (GNNs) learns from graph data while forwarding messages in the network. Utilizing GNN benefits, a Graph reinforcement learning-based online offloading framework (GROO) is proposed in this research, where the offloading policy is visualized as a graph state migration and MEC as an acyclic graph. The GROO achieves the lowest weighted task response latency (0.96 s) as compared to the existing DRL method (1.32 s) whereas on unseen circumstances and complex network topologies, GROO achieved lowest average latency up to 25 %.

移动边缘计算（MEC）扩展到无线传感器网络和物联网，可以提高低功耗网络的数据处理能力。本研究的重点是创建一个具有卸载策略的MEC网络的复制，其中每个计算任务在无线设备（wd）中进行选择。任务执行是在同一环境下本地进行，还是可以移交给远程MEC服务器，需要一种采用任务卸载决策和实时无线资源分配的优化算法。但是，采用这种方法来解决实时快速组合优化问题是一种具有挑战性的解决方案，用现有的传统方法是不可能的。作为解决方案，包含深度强化学习（DRL）的启发式算法正在出现；然而，它没有合理地利用连接数据，如MEC网络中的设备对设备交互。此外，启发式算法依赖于MEC系统的精确数学模型，这为MEC系统提供了一个新的理论。本研究围绕这一新兴技术展开，该技术依赖于图神经网络（gnn）在网络中转发消息时从图数据中学习。利用GNN的优势，提出了一种基于图强化学习的在线卸载框架（GROO），将卸载策略可视化为图状态迁移，将MEC可视化为无环图。与现有的DRL方法（1.32秒）相比，GROO实现了最低的加权任务响应延迟（0.96秒），而在不可见的环境和复杂的网络拓扑中，GROO实现了最低的平均延迟，最高可达25%。

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引用次数: 0

Towards visual-symbolic integration in the Soar cognitive architecture 在Soar认知架构中实现视觉-符号的整合

IF 2.1 3区心理学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Cognitive Systems Research

Pub Date : 2025-03-21 DOI: 10.1016/j.cogsys.2025.101353

James Boggs

Computational models of visual reasoning are largely separate from models of non-visual reasoning and include only enough high-level reasoning to perform specific visual reasoning tasks, such as Raven’s progressive matrices or visual question answering. Although these models perform well at the pure visual reasoning tasks for which they are designed, their lack of a connection with a general-purpose high-level reasoning system means they cannot be applied to tasks requiring deliberate reasoning about both visual and non-visual knowledge. Simultaneously, many of the most mature and heavily studied cognitive architectures (e.g., Soar, ACT-R) feature only partial visual reasoning capabilities or none at all. This work describes initial efforts to create a visual reasoning system tightly integrated with a broader reasoning system by extending the Soar cognitive architecture with low-level visual memories and reasoning processes, and an evaluation of this system on tasks in a simple domain. Its ultimate aim is to demonstrate a path towards accommodating multiple levels of visual knowledge representations within an otherwise mostly symbolic, rules-based architecture.

视觉推理的计算模型在很大程度上与非视觉推理模型分开，并且只包括足够的高级推理来执行特定的视觉推理任务，例如Raven的渐进矩阵或视觉问题回答。尽管这些模型在纯视觉推理任务中表现良好，但它们缺乏与通用高级推理系统的联系，这意味着它们不能应用于需要对视觉和非视觉知识进行深思熟虑推理的任务。同时，许多最成熟和被大量研究的认知架构（例如，Soar, ACT-R）只具有部分视觉推理能力，或者根本没有。这项工作描述了创建一个视觉推理系统与更广泛的推理系统紧密集成的初步努力，通过扩展具有低级视觉记忆和推理过程的Soar认知架构，并在一个简单领域对该系统的任务进行评估。它的最终目标是展示在一个主要是符号的、基于规则的体系结构中容纳多层次视觉知识表示的途径。

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引用次数: 0

Navigating the complex dynamics of human-automation driving: A guide to the use of the dynamical systems analysis (DSA) toolbox 驾驭人机交互驾驶的复杂动态：动态系统分析（DSA）工具箱使用指南

IF 2.1 3区心理学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Cognitive Systems Research

Pub Date : 2025-03-11 DOI: 10.1016/j.cogsys.2025.101347

Tri Nguyen , Corey Magaldino , Jayci Landfair , Polemnia G. Amazeen , Mustafa Demir , Lixiao Huang , Nancy Cooke

Driver-environment-automation systems exhibit a wide range of distinctive behavioral patterns that organically arise from complex interactions. To understand and quantify their emergence, we examined the nested underlying processes that contribute to observable behavior using three dynamical systems analyses: multifractal detrended fluctuation analysis (MFDFA), recurrence quantification analysis (RQA), and wavelet coherence analysis (WCT). As a technical demonstration of how to utilize multiple nonlinear analyses to probe multivariate data, we explain the appropriateness of each analysis for each stage of discovery, the information each provides, and the application of that information to driving. Results revealed that driving behaviors are influenced by both long-range (e.g., decision-making) and short-range (e.g., reaction time) processes whose relative contribution differs for the easier straight sections and more challenging S-curve sections of the track. The discussed methods provide information about (a) the timescale at which driving behaviors are being coordinated with environmental and automation considerations and (b) the time points where peak coordination is localized. This paper illustrates and empirically examines the utility of the Dynamical Systems Analysis (DSA) toolbox in understanding the behaviors of complex systems and highlights important considerations for researchers seeking to utilize this approach in their research.

驾驶员-环境-自动化系统表现出一系列独特的行为模式，这些行为模式是由复杂的相互作用有机产生的。为了理解和量化它们的出现，我们使用三种动力系统分析：多重分形去趋势波动分析（MFDFA）、递归量化分析（RQA）和小波相干分析（WCT），研究了导致可观察行为的嵌套潜在过程。作为如何利用多种非线性分析来探测多元数据的技术演示，我们解释了每种分析对每个发现阶段的适用性，每种分析提供的信息，以及该信息在驾驶中的应用。结果表明，驾驶行为同时受到长程过程（如决策过程）和短程过程（如反应时间过程）的影响，两者的相对贡献在较容易的直线路段和较困难的s曲线路段有所不同。所讨论的方法提供了关于(a)驾驶行为与环境和自动化因素协调的时间尺度和(b)峰值协调的时间点的信息。本文说明和实证检验了动力系统分析（DSA）工具箱在理解复杂系统行为方面的效用，并强调了在研究中寻求利用这种方法的研究人员的重要考虑因素。

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引用次数: 0

Human robot interaction (HRI): An artificial cognitive autonomy approach to enhance Decision-Making 人机交互（HRI）：一种增强决策的人工认知自主方法

IF 2.1 3区心理学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Cognitive Systems Research

Pub Date : 2025-03-05 DOI: 10.1016/j.cogsys.2025.101336

Walter Teixeira Lima Junior, Rudinei André Welter, Wellington Pacheco Ferreira, Rodrigo Ferreira Souza, Tiago Eduardo

This study explores the critical role of artificial cognitive autonomy in Human-Robot Interaction (HRI), focusing on scenarios where quick and safe decisions are imperative. We investigate a progressive autonomy strategy supported by advanced artificial cognition techniques to improve decision-making in unforeseen situations and in the face of unknown conditions. We highlight the importance of these systems in performing essential safety functions through a three-dimensional approach: advanced perception for detailed environmental analysis; decision making based on robust algorithms for logical assessment of risk scenarios; and precise action and control to perform essential autonomous tasks. Additionally, we present a conceptual modeling that illustrates the progression of autonomy levels from total dependence to completely autonomous operation, highlighting the evolution of HRI systems through artificial cognitive autonomy. This article argues that decision-making optimization in HRI can be significantly improved through a detailed and incremental understanding of autonomy. By adopting enabling technologies, we enable autonomous agents to not only evolve within their environments, but also learn, understand and fulfill their responsibilities effectively. This theoretical approach promotes a systematic evolution of autonomy, as well as ensuring that robotic systems adapt and respond appropriately to the complex and dynamic demands of the environments in which they operate.

本研究探讨了人工认知自主性在人机交互（HRI）中的关键作用，重点关注快速安全决策势在必行的场景。我们研究了一种先进的人工认知技术支持的渐进自治策略，以改善在不可预见的情况下和面对未知条件时的决策。我们强调这些系统在通过三维方法执行基本安全功能方面的重要性：对详细环境分析的高级感知；基于鲁棒算法的风险情景逻辑评估决策以及精确的行动和控制来执行基本的自主任务。此外，我们提出了一个概念模型，说明了自主水平从完全依赖到完全自主操作的进展，突出了HRI系统通过人工认知自主的演变。本文认为，通过对自主性的详细和渐进的理解，可以显著改善人力资源研究所的决策优化。通过采用使能技术，我们使自主代理不仅能够在其环境中进化，而且能够有效地学习、理解和履行其职责。这种理论方法促进了自主性的系统进化，并确保机器人系统适应并适当地响应其运行环境的复杂和动态需求。

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引用次数: 0