Cognitive Systems Research最新文献

A graph reinforcement LearningPowered Online-Computational task offloading and latency minimization framework for wireless mobile edge computing networks

IF 2.1 3区心理学

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

{"title":"A graph reinforcement LearningPowered Online-Computational task offloading and latency minimization framework for wireless mobile edge computing networks","authors":"Akshat Agrawal , Aayush Agrawal , Nilesh Kumar Verma , Arepalli Peda Gopi , K. Jairam Naik","doi":"10.1016/j.cogsys.2025.101355","DOIUrl":"10.1016/j.cogsys.2025.101355","url":null,"abstract":"<div><div>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 %.</div></div>","PeriodicalId":55242,"journal":{"name":"Cognitive Systems Research","volume":"91 ","pages":"Article 101355"},"PeriodicalIF":2.1,"publicationDate":"2025-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143687970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Towards visual-symbolic integration in the Soar cognitive architecture

IF 2.1 3区心理学

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

James Boggs

{"title":"Towards visual-symbolic integration in the Soar cognitive architecture","authors":"James Boggs","doi":"10.1016/j.cogsys.2025.101353","DOIUrl":"10.1016/j.cogsys.2025.101353","url":null,"abstract":"<div><div>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 <em>deliberate</em> 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.</div></div>","PeriodicalId":55242,"journal":{"name":"Cognitive Systems Research","volume":"91 ","pages":"Article 101353"},"PeriodicalIF":2.1,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725591","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

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

{"title":"Navigating the complex dynamics of human-automation driving: A guide to the use of the dynamical systems analysis (DSA) toolbox","authors":"Tri Nguyen , Corey Magaldino , Jayci Landfair , Polemnia G. Amazeen , Mustafa Demir , Lixiao Huang , Nancy Cooke","doi":"10.1016/j.cogsys.2025.101347","DOIUrl":"10.1016/j.cogsys.2025.101347","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":55242,"journal":{"name":"Cognitive Systems Research","volume":"91 ","pages":"Article 101347"},"PeriodicalIF":2.1,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143685199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Human robot interaction (HRI): An artificial cognitive autonomy approach to enhance Decision-Making

IF 2.1 3区心理学

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

{"title":"Human robot interaction (HRI): An artificial cognitive autonomy approach to enhance Decision-Making","authors":"Walter Teixeira Lima Junior, Rudinei André Welter, Wellington Pacheco Ferreira, Rodrigo Ferreira Souza, Tiago Eduardo","doi":"10.1016/j.cogsys.2025.101336","DOIUrl":"10.1016/j.cogsys.2025.101336","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":55242,"journal":{"name":"Cognitive Systems Research","volume":"91 ","pages":"Article 101336"},"PeriodicalIF":2.1,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143611618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Circling the void: Using Heidegger and Lacan to think about large language models

IF 2.1 3区心理学

Cognitive Systems Research Pub Date : 2025-03-04 DOI: 10.1016/j.cogsys.2025.101349

Marc Heimann, Anne-Friederike Hübener

引用次数: 0

The effect of visual working memory consolidation on long-term memory for Chinese characters

IF 2.1 3区心理学

Cognitive Systems Research Pub Date : 2025-03-04 DOI: 10.1016/j.cogsys.2025.101348

Li Chen, Yuhuan Chen, Li Wang, Chunyin Wang

引用次数: 0

Active exploration and working memory synaptic plasticity shapes goal-directed behavior in curiosity-driven learning

IF 2.1 3区心理学

Cognitive Systems Research Pub Date : 2025-03-03 DOI: 10.1016/j.cogsys.2025.101339

Quentin Houbre, Roel Pieters

引用次数: 0

Neurons as autonomous agents: A biologically inspired framework for cognitive architectures in artificial intelligence

IF 2.1 3区心理学

Cognitive Systems Research Pub Date : 2025-02-24 DOI: 10.1016/j.cogsys.2025.101338

Artur Luczak

{"title":"Neurons as autonomous agents: A biologically inspired framework for cognitive architectures in artificial intelligence","authors":"Artur Luczak","doi":"10.1016/j.cogsys.2025.101338","DOIUrl":"10.1016/j.cogsys.2025.101338","url":null,"abstract":"<div><div>Despite impressive recent advances in artificial intelligence (AI), current deep neural networks still lack the adaptability and energy efficiency inherent to biological systems. Here we suggest that this problem may be overcome by taking inspiration from the brain where neurons operate as autonomous agents, each capable of adjusting its synaptic connections and internal states based on local information. Currently, typical artificial neurons are static nodes, which is in striking contrast to the rich, dynamic computations performed by biological neurons. In this review, we propose redesigning artificial neurons as self-regulating, agent-like units, making actions to maximize future energy/reward. Similarly, as single-celled organisms which can autonomously navigate in complex environments in search for food, neurons can also be viewed as autonomous decision-makers, seeking to maximize their own energy resources. Thus, neurons could be operating similarly like reinforcement learning (RL) agents, which make actions to obtain maximum future reward. Here first we review literature illustrating that biological neurons perform complex computations and employ local, predictive learning rules to anticipate future activity to maximize metabolic energy. Next, we provide examples of recent biologically inspired learning algorithms where artificial neurons are empowered with computational flexibility, similarly to autonomous agents. Networks with neurons using such local learning rules can in some examples outperform current AI algorithms. We also discuss how this can improve scalability of current multi-agent systems (MAS) and energy efficiency. Therefore, designing neurons as autonomous agents may provide an important step toward building human-like cognition.</div></div>","PeriodicalId":55242,"journal":{"name":"Cognitive Systems Research","volume":"90 ","pages":"Article 101338"},"PeriodicalIF":2.1,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143479106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Human guided empathetic AI agent for mental health support leveraging reinforcement learning-enhanced retrieval-augmented generation

IF 2.1 3区心理学

Cognitive Systems Research Pub Date : 2025-02-22 DOI: 10.1016/j.cogsys.2025.101337

Gayathri Soman, M.V. Judy, Aadhil Muhammad Abou

{"title":"Human guided empathetic AI agent for mental health support leveraging reinforcement learning-enhanced retrieval-augmented generation","authors":"Gayathri Soman, M.V. Judy, Aadhil Muhammad Abou","doi":"10.1016/j.cogsys.2025.101337","DOIUrl":"10.1016/j.cogsys.2025.101337","url":null,"abstract":"<div><div>Global mental health issues is increasing due to problems such as the social stigma around treatment, a long-neglected burdens of insufficient resources, and the rising tide of mental issues. Large language models (LLMs) can accelerate the development of comprehensive, extensive solutions that support mental health. However, the LLMs’ capability to generate and comprehend human-like conversations is one of the main challenges faced by psychiatric counselling. This work proposes a mental health counselling LLM-based conversational agent that relies on the integration of Retrieval Augmented Generation (RAG) and Reinforcement learning. RAG provides the proposed LLM-based conversational agent with contextually relevant and accurate responses through useful information extracted from a curated dataset of psychological questions and answers pooled from mental health forums. Reinforcement Learning Integrated reward Model trained with Human feedback has also been used in the proposed framework to ensure contractually of the responses generated with moral and human values. By setting up a reward mechanism that considers variables like user feedback and empathetic scores of responses, the proposed Conversational Agent learns to prioritize empathetic answers and the ones that are user preferable. With the utilization of reward-based training, the agent was able to show substantial improvements in response quality. Improved emotional alignment, steady training dynamics, decreased hallucination rates with responses having less distress and increased empathy values were the significant outcomes. The proposed methodology ensures that the conversational agent remains attentive to the emotional requirements of people seeking for mental health care and provide improved relevance and accuracy in its responses.</div></div>","PeriodicalId":55242,"journal":{"name":"Cognitive Systems Research","volume":"90 ","pages":"Article 101337"},"PeriodicalIF":2.1,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143510955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Combination of reward-modulated spike-timing dependent plasticity and temporal difference long-term potentiation in actor–critic spiking neural network

IF 2.1 3区心理学

Cognitive Systems Research Pub Date : 2025-02-06 DOI: 10.1016/j.cogsys.2025.101334

Yunes Tihomirov , Roman Rybka , Alexey Serenko , Alexander Sboev

引用次数: 0