Constitutive Components for Human-Like Autonomous Artificial Intelligence

• URL: http://arxiv.org/abs/2506.12952v1
• Date: Sun, 15 Jun 2025 19:35:27 GMT
• Title: Constitutive Components for Human-Like Autonomous Artificial Intelligence
• Authors: Kazunori D Yamada,
• Abstract summary: This study is the first to clearly identify the functions required to construct artificial entities capable of behaving autonomously like humans.<n>It proposes a stepwise model of autonomy comprising reactive, weak autonomous, and strong autonomous levels.
• Score: 0.9065034043031668
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This study is the first to clearly identify the functions required to construct artificial entities capable of behaving autonomously like humans, and organizes them into a three-layer functional hierarchy. Specifically, it defines three levels: Core Functions, which enable interaction with the external world; the Integrative Evaluation Function, which selects actions based on perception and memory; and the Self Modification Function, which dynamically reconfigures behavioral principles and internal components. Based on this structure, the study proposes a stepwise model of autonomy comprising reactive, weak autonomous, and strong autonomous levels, and discusses its underlying design principles and developmental aspects. It also explores the relationship between these functions and existing artificial intelligence design methods, addressing their potential as a foundation for general intelligence and considering future applications and ethical implications. By offering a theoretical framework that is independent of specific technical methods, this work contributes to a deeper understanding of autonomy and provides a foundation for designing future artificial entities with strong autonomy.

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)
• Nature's Insight: A Novel Framework and Comprehensive Analysis of Agentic Reasoning Through the Lens of Neuroscience [11.174550573411008]
  We propose a novel neuroscience-inspired framework for agentic reasoning.<n>We apply this framework to systematically classify and analyze existing AI reasoning methods.<n>We propose new neural-inspired reasoning methods, analogous to chain-of-thought prompting.
  arXiv  Detail & Related papers  (2025-05-07T14:25:46Z)
• 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.<n>As these agents increasingly drive AI research and practical applications, their design, evaluation, and continuous improvement present intricate, multifaceted challenges.<n>This survey provides a comprehensive overview, framing intelligent agents within a modular, brain-inspired architecture.
  arXiv  Detail & Related papers  (2025-03-31T18:00:29Z)
• Emergence of Self-Awareness in Artificial Systems: A Minimalist Three-Layer Approach to Artificial Consciousness [0.0]
  This paper proposes a minimalist three-layer model for artificial consciousness, focusing on the emergence of self-awareness.<n>Unlike brain-replication approaches, we aim to achieve minimal self-awareness through essential elements only.
  arXiv  Detail & Related papers  (2025-02-04T10:06:25Z)
• A Quantitative Autonomy Quantification Framework for Fully Autonomous Robotic Systems [0.0]
  This paper focuses on the full autonomous mode and proposes a quantitative autonomy assessment framework based on task requirements. The framework provides not only a tool for quantifying autonomy, but also a regulatory interface and common language for autonomous systems developers and users.
  arXiv  Detail & Related papers  (2023-11-03T14:26:53Z)
• Incremental procedural and sensorimotor learning in cognitive humanoid robots [52.77024349608834]
  This work presents a cognitive agent that can learn procedures incrementally. We show the cognitive functions required in each substage and how adding new functions helps address tasks previously unsolved by the agent. Results show that this approach is capable of solving complex tasks incrementally.
  arXiv  Detail & Related papers  (2023-04-30T22:51:31Z)
• Kernel Based Cognitive Architecture for Autonomous Agents [91.3755431537592]
  This paper considers an evolutionary approach to creating a cognitive functionality. We consider a cognitive architecture which ensures the evolution of the agent on the basis of Symbol Emergence Problem solution.
  arXiv  Detail & Related papers  (2022-07-02T12:41:32Z)
• Autonomous Open-Ended Learning of Tasks with Non-Stationary Interdependencies [64.0476282000118]
  Intrinsic motivations have proven to generate a task-agnostic signal to properly allocate the training time amongst goals. While the majority of works in the field of intrinsically motivated open-ended learning focus on scenarios where goals are independent from each other, only few of them studied the autonomous acquisition of interdependent tasks. In particular, we first deepen the analysis of a previous system, showing the importance of incorporating information about the relationships between tasks at a higher level of the architecture. Then we introduce H-GRAIL, a new system that extends the previous one by adding a new learning layer to store the autonomously acquired sequences
  arXiv  Detail & Related papers  (2022-05-16T10:43:01Z)
• Active Inference in Robotics and Artificial Agents: Survey and Challenges [51.29077770446286]
  We review the state-of-the-art theory and implementations of active inference for state-estimation, control, planning and learning. We showcase relevant experiments that illustrate its potential in terms of adaptation, generalization and robustness.
  arXiv  Detail & Related papers  (2021-12-03T12:10:26Z)
• An Artificial Consciousness Model and its relations with Philosophy of Mind [0.0]
  This work seeks to study the beneficial properties that an autonomous agent can obtain by implementing a cognitive architecture similar to the one of conscious beings. We show in a large experiment set how an autonomous agent can benefit from having a cognitive architecture such as the one described.
  arXiv  Detail & Related papers  (2020-11-30T00:24:17Z)
• Machine Common Sense [77.34726150561087]
  Machine common sense remains a broad, potentially unbounded problem in artificial intelligence (AI) This article deals with the aspects of modeling commonsense reasoning focusing on such domain as interpersonal interactions.
  arXiv  Detail & Related papers  (2020-06-15T13:59:47Z)

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.