Cognitive AI framework: advances in the simulation of human thought
- URL: http://arxiv.org/abs/2502.04259v1
- Date: Thu, 06 Feb 2025 17:43:35 GMT
- Title: Cognitive AI framework: advances in the simulation of human thought
- Authors: Rommel Salas-Guerra,
- Abstract summary: The Human Cognitive Simulation Framework represents a significant advancement in integrating human cognitive capabilities into artificial intelligence systems.<n>By merging short-term memory (conversation context), long-term memory (interaction context), advanced cognitive processing, and efficient knowledge management, it ensures contextual coherence and persistent data storage.<n>This framework lays the foundation for future research in continuous learning algorithms, sustainability, and multimodal adaptability, positioning Cognitive AI as a transformative model in emerging fields.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: The Human Cognitive Simulation Framework represents a significant advancement in integrating human cognitive capabilities into artificial intelligence systems. By merging short-term memory (conversation context), long-term memory (interaction context), advanced cognitive processing, and efficient knowledge management, it ensures contextual coherence and persistent data storage, enhancing personalization and continuity in human-AI interactions. The framework employs a unified database that synchronizes these contexts while incorporating logical, creative, and analog processing modules inspired by human brain hemispheric functions to perform structured tasks and complex inferences. Dynamic knowledge updates enable real-time integration, improving adaptability and fostering applications in education, behavior analysis, and knowledge management. Despite its potential to process vast data volumes and enhance user experience, challenges remain in scalability, cognitive bias mitigation, and ethical compliance. This framework lays the foundation for future research in continuous learning algorithms, sustainability, and multimodal adaptability, positioning Cognitive AI as a transformative model in emerging fields.
Related papers
- A Survey of Self-Evolving Agents: On Path to Artificial Super Intelligence [87.08051686357206]
Large Language Models (LLMs) have demonstrated strong capabilities but remain fundamentally static.<n>As LLMs are increasingly deployed in open-ended, interactive environments, this static nature has become a critical bottleneck.<n>This survey provides the first systematic and comprehensive review of self-evolving agents.
arXiv Detail & Related papers (2025-07-28T17:59:05Z) - Thinking Beyond Tokens: From Brain-Inspired Intelligence to Cognitive Foundations for Artificial General Intelligence and its Societal Impact [27.722167796617114]
This paper offers a cross-disciplinary synthesis of artificial intelligence, cognitive neuroscience, psychology, generative models, and agent-based systems.<n>We analyze the architectural and cognitive foundations of general intelligence, highlighting the role of modular reasoning, persistent memory, and multi-agent coordination.<n>We identify key scientific, technical, and ethical challenges on the path to Artificial General Intelligence.
arXiv Detail & Related papers (2025-07-01T16:52:25Z) - Cognitive Weave: Synthesizing Abstracted Knowledge with a Spatio-Temporal Resonance Graph [2.800801614127705]
This paper introduces Cognitive Weave, a memory framework centered around a multi-layered dynamic resonance graph (GSTR)<n>GSTR manages information as semantically rich insight particles (IPs), which are enriched with resonance keys, signifiers, and situational imprints via a dedicated semantic oracle interface (ISO)<n>A key component of Cognitive Weave is the cognitive process, which includes the synthesis of insight aggregates (AsI) condensed, higher-level knowledge structures.
arXiv Detail & Related papers (2025-06-09T18:00:46Z) - When Models Know More Than They Can Explain: Quantifying Knowledge Transfer in Human-AI Collaboration [79.69935257008467]
We introduce Knowledge Integration and Transfer Evaluation (KITE), a conceptual and experimental framework for Human-AI knowledge transfer capabilities.<n>We conduct the first large-scale human study (N=118) explicitly designed to measure it.<n>In our two-phase setup, humans first ideate with an AI on problem-solving strategies, then independently implement solutions, isolating model explanations' influence on human understanding.
arXiv Detail & Related papers (2025-06-05T20:48:16Z) - Dynamic Programming Techniques for Enhancing Cognitive Representation in Knowledge Tracing [125.75923987618977]
We propose the Cognitive Representation Dynamic Programming based Knowledge Tracing (CRDP-KT) model.<n>It is a dynamic programming algorithm to optimize cognitive representations based on the difficulty of the questions and the performance intervals between them.<n>It provides more accurate and systematic input features for subsequent model training, thereby minimizing distortion in the simulation of cognitive states.
arXiv Detail & Related papers (2025-06-03T14:44:48Z) - CAIM: Development and Evaluation of a Cognitive AI Memory Framework for Long-Term Interaction with Intelligent Agents [1.6082737760346446]
Large language models (LLMs) have advanced the field of artificial intelligence (AI) and are a powerful enabler for interactive systems.<n>They still face challenges in long-term interactions that require adaptation towards the user as well as contextual knowledge and understanding of the ever-changing environment.<n>To overcome these challenges, holistic memory modeling is required to efficiently retrieve and store relevant information across interaction sessions.<n> Cognitive AI, which aims to simulate the human thought process in a computerized model, highlights interesting aspects, such as thoughts, memory mechanisms, and decision-making.
arXiv Detail & Related papers (2025-05-19T12:33:52Z) - Neural Brain: A Neuroscience-inspired Framework for Embodied Agents [58.58177409853298]
Current AI systems, such as large language models, remain disembodied, unable to physically engage with the world.<n>At the core of this challenge lies the concept of Neural Brain, a central intelligence system designed to drive embodied agents with human-like adaptability.<n>This paper introduces a unified framework for the Neural Brain of embodied agents, addressing two fundamental challenges.
arXiv Detail & Related papers (2025-05-12T15:05:34Z) - Artificial Behavior Intelligence: Technology, Challenges, and Future Directions [1.5237607855633524]
This paper defines the technical framework of Artificial Behavior Intelligence (ABI)<n>ABI comprehensively analyzes and interprets human posture, facial expressions, emotions, behavioral sequences, and contextual cues.<n>It details the essential components of ABI, including pose estimation, face and emotion recognition, sequential behavior analysis, and context-aware modeling.
arXiv Detail & Related papers (2025-05-06T08:45:44Z) - Semi-parametric Memory Consolidation: Towards Brain-like Deep Continual Learning [59.35015431695172]
We propose a novel biomimetic continual learning framework that integrates semi-parametric memory and the wake-sleep consolidation mechanism.
For the first time, our method enables deep neural networks to retain high performance on novel tasks while maintaining prior knowledge in real-world challenging continual learning scenarios.
arXiv Detail & Related papers (2025-04-20T19:53:13Z) - Siamese Network with Dual Attention for EEG-Driven Social Learning: Bridging the Human-Robot Gap in Long-Tail Autonomous Driving [0.0]
This study presents a brain-computer interface (BCI) framework that enables classification of Electroencephalogram (EEG) signals to detect cognitively demanding and safety-critical events.
As a timely and motivating co-robotic engineering application, we simulate a human-in-the-loop scenario to flag risky events in semi-autonomous robotic driving.
We propose a dual-attention Siamese convolutional network paired with Dynamic Time Warping Barycenter Averaging approach to generate robust EEG-encoded signal representations.
arXiv Detail & Related papers (2025-04-14T15:06:17Z) - A Human Digital Twin Architecture for Knowledge-based Interactions and Context-Aware Conversations [0.9580312063277943]
Recent developments in Artificial Intelligence (AI) and Machine Learning (ML) are creating new opportunities for Human-Autonomy Teaming (HAT)
We present a real-time Human Digital Twin (HDT) architecture that integrates Large Language Models (LLMs) for knowledge reporting, answering, and recommendation, embodied in a visual interface.
The HDT acts as a visually and behaviorally realistic team member, integrated throughout the mission lifecycle, from training to deployment to after-action review.
arXiv Detail & Related papers (2025-04-04T03:56:26Z) - From Consumption to Collaboration: Measuring Interaction Patterns to Augment Human Cognition in Open-Ended Tasks [2.048226951354646]
The rise of Generative AI, and Large Language Models (LLMs) in particular, is fundamentally changing cognitive processes in knowledge work.
We present a framework that analyzes interaction patterns along two dimensions: cognitive activity mode (exploration vs. exploitation) and cognitive engagement mode (constructive vs. detrimental)
arXiv Detail & Related papers (2025-04-03T17:20:36Z) - Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems [133.45145180645537]
The advent of large language models (LLMs) has catalyzed a transformative shift in artificial intelligence.
As these agents increasingly drive AI research and practical applications, their design, evaluation, and continuous improvement present intricate, multifaceted challenges.
This survey provides a comprehensive overview, framing intelligent agents within a modular, brain-inspired architecture.
arXiv Detail & Related papers (2025-03-31T18:00:29Z) - Mimicking Human Intuition: Cognitive Belief-Driven Q-Learning [5.960184723807347]
We propose Cognitive Belief-Driven Q-Learning (CBDQ), which integrates subjective belief modeling into the Q-learning framework.
CBDQ enhances decision-making accuracy by endowing agents with human-like learning and reasoning capabilities.
We evaluate the proposed method on discrete control benchmark tasks in various complicate environments.
arXiv Detail & Related papers (2024-10-02T16:50:29Z) - Emulating Brain-like Rapid Learning in Neuromorphic Edge Computing [3.735012564657653]
Digital neuromorphic technology simulates the neural and synaptic processes of the brain using two stages of learning.
We demonstrate our approach using event-driven vision sensor data and the Intel Loihi neuromorphic processor with its plasticity dynamics.
Our methodology can be deployed with arbitrary plasticity models and can be applied to situations demanding quick learning and adaptation at the edge.
arXiv Detail & Related papers (2024-08-28T13:51:52Z) - Converging Paradigms: The Synergy of Symbolic and Connectionist AI in LLM-Empowered Autonomous Agents [55.63497537202751]
Article explores the convergence of connectionist and symbolic artificial intelligence (AI)
Traditionally, connectionist AI focuses on neural networks, while symbolic AI emphasizes symbolic representation and logic.
Recent advancements in large language models (LLMs) highlight the potential of connectionist architectures in handling human language as a form of symbols.
arXiv Detail & Related papers (2024-07-11T14:00:53Z) - Enabling High-Level Machine Reasoning with Cognitive Neuro-Symbolic
Systems [67.01132165581667]
We propose to enable high-level reasoning in AI systems by integrating cognitive architectures with external neuro-symbolic components.
We illustrate a hybrid framework centered on ACT-R and we discuss the role of generative models in recent and future applications.
arXiv Detail & Related papers (2023-11-13T21:20:17Z) - Dual Cognitive Architecture: Incorporating Biases and Multi-Memory
Systems for Lifelong Learning [21.163070161951868]
We introduce Dual Cognitive Architecture (DUCA), which includes multiple sub-systems, implicit and explicit knowledge representation, inductive bias, and a multi-memory system.
DUCA shows improvement across different settings and datasets, and it also exhibits reduced task recency bias, without the need for extra information.
To further test the versatility of lifelong learning methods on a challenging distribution shift, we introduce a novel domain-incremental dataset DN4IL.
arXiv Detail & Related papers (2023-10-17T15:24:02Z) - A Framework for Inference Inspired by Human Memory Mechanisms [9.408704431898279]
We propose a PMI framework that consists of perception, memory and inference components.
The memory module comprises working and long-term memory, with the latter endowed with a higher-order structure to retain extensive and complex relational knowledge and experience.
We apply our PMI to improve prevailing Transformers and CNN models on question-answering tasks like bAbI-20k and Sort-of-CLEVR datasets.
arXiv Detail & Related papers (2023-10-01T08:12:55Z) - Incorporating Neuro-Inspired Adaptability for Continual Learning in
Artificial Intelligence [59.11038175596807]
Continual learning aims to empower artificial intelligence with strong adaptability to the real world.
Existing advances mainly focus on preserving memory stability to overcome catastrophic forgetting.
We propose a generic approach that appropriately attenuates old memories in parameter distributions to improve learning plasticity.
arXiv Detail & Related papers (2023-08-29T02:43:58Z) - A brain basis of dynamical intelligence for AI and computational
neuroscience [0.0]
More brain-like capacities may demand new theories, models, and methods for designing artificial learning systems.
This article was inspired by our symposium on dynamical neuroscience and machine learning at the 6th Annual US/NIH BRAIN Initiative Investigators Meeting.
arXiv Detail & Related papers (2021-05-15T19:49:32Z) - Cognitive architecture aided by working-memory for self-supervised
multi-modal humans recognition [54.749127627191655]
The ability to recognize human partners is an important social skill to build personalized and long-term human-robot interactions.
Deep learning networks have achieved state-of-the-art results and demonstrated to be suitable tools to address such a task.
One solution is to make robots learn from their first-hand sensory data with self-supervision.
arXiv Detail & Related papers (2021-03-16T13:50:24Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.