Probing for Consciousness in Machines

• URL: http://arxiv.org/abs/2411.16262v1
• Date: Mon, 25 Nov 2024 10:27:07 GMT
• Title: Probing for Consciousness in Machines
• Authors: Mathis Immertreu, Achim Schilling, Andreas Maier, Patrick Krauss,
• Abstract summary: This study explores the potential for artificial agents to develop core consciousness. The emergence of core consciousness relies on the integration of a self model, informed by representations of emotions and feelings, and a world model. Our results demonstrate that the agent can form rudimentary world and self models, suggesting a pathway toward developing machine consciousness.
• Score: 3.196204482566275
• License:
• Abstract: This study explores the potential for artificial agents to develop core consciousness, as proposed by Antonio Damasio's theory of consciousness. According to Damasio, the emergence of core consciousness relies on the integration of a self model, informed by representations of emotions and feelings, and a world model. We hypothesize that an artificial agent, trained via reinforcement learning (RL) in a virtual environment, can develop preliminary forms of these models as a byproduct of its primary task. The agent's main objective is to learn to play a video game and explore the environment. To evaluate the emergence of world and self models, we employ probes-feedforward classifiers that use the activations of the trained agent's neural networks to predict the spatial positions of the agent itself. Our results demonstrate that the agent can form rudimentary world and self models, suggesting a pathway toward developing machine consciousness. This research provides foundational insights into the capabilities of artificial agents in mirroring aspects of human consciousness, with implications for future advancements in artificial intelligence.

Related papers

• Emergence of Implicit World Models from Mortal Agents [0.276240219662896]
  We discuss the possibility of world models and active exploration as emergent properties of open-ended behavior optimization in autonomous agents. In discussing the source of the open-endedness of living things, we start from the perspective of biological systems as understood by the mechanistic approach of theoretical biology and artificial life.
  arXiv  Detail & Related papers  (2024-11-19T07:43:30Z)
• Position Paper: Agent AI Towards a Holistic Intelligence [53.35971598180146]
  We emphasize developing Agent AI -- an embodied system that integrates large foundation models into agent actions. In this paper, we propose a novel large action model to achieve embodied intelligent behavior, the Agent Foundation Model.
  arXiv  Detail & Related papers  (2024-02-28T16:09:56Z)
• On the Emergence of Symmetrical Reality [51.21203247240322]
  We introduce the symmetrical reality framework, which offers a unified representation encompassing various forms of physical-virtual amalgamations. We propose an instance of an AI-driven active assistance service that illustrates the potential applications of symmetrical reality.
  arXiv  Detail & Related papers  (2024-01-26T16:09:39Z)
• Agent AI: Surveying the Horizons of Multimodal Interaction [83.18367129924997]
  "Agent AI" is a class of interactive systems that can perceive visual stimuli, language inputs, and other environmentally-grounded data. We envision a future where people can easily create any virtual reality or simulated scene and interact with agents embodied within the virtual environment.
  arXiv  Detail & Related papers  (2024-01-07T19:11:18Z)
• Self-mediated exploration in artificial intelligence inspired by cognitive psychology [1.3351610617039975]
  Exploration of the physical environment is an indispensable precursor to data acquisition and enables knowledge generation via analytical or direct trialing. This work links human behavior and artificial agents to endorse self-development. A study is subsequently designed to mirror previous human trials, which artificial agents are made to undergo repeatedly towards convergence. Results demonstrate causality, learned by the vast majority of agents, between their internal states and exploration to match those reported for human counterparts.
  arXiv  Detail & Related papers  (2023-02-13T18:20:44Z)
• World Models and Predictive Coding for Cognitive and Developmental Robotics: Frontiers and Challenges [51.92834011423463]
  We focus on the two concepts of world models and predictive coding. In neuroscience, predictive coding proposes that the brain continuously predicts its inputs and adapts to model its own dynamics and control behavior in its environment.
  arXiv  Detail & Related papers  (2023-01-14T06:38:14Z)
• Backprop-Free Reinforcement Learning with Active Neural Generative Coding [84.11376568625353]
  We propose a computational framework for learning action-driven generative models without backpropagation of errors (backprop) in dynamic environments. We develop an intelligent agent that operates even with sparse rewards, drawing inspiration from the cognitive theory of planning as inference. The robust performance of our agent offers promising evidence that a backprop-free approach for neural inference and learning can drive goal-directed behavior.
  arXiv  Detail & Related papers  (2021-07-10T19:02:27Z)
• AGENT: A Benchmark for Core Psychological Reasoning [60.35621718321559]
  Intuitive psychology is the ability to reason about hidden mental variables that drive observable actions. Despite recent interest in machine agents that reason about other agents, it is not clear if such agents learn or hold the core psychology principles that drive human reasoning. We present a benchmark consisting of procedurally generated 3D animations, AGENT, structured around four scenarios.
  arXiv  Detail & Related papers  (2021-02-24T14:58:23Z)
• Imitating Interactive Intelligence [24.95842455898523]
  We study how to design artificial agents that can interact naturally with humans using the simplification of a virtual environment. To build agents that can robustly interact with humans, we would ideally train them while they interact with humans. We use ideas from inverse reinforcement learning to reduce the disparities between human-human and agent-agent interactive behaviour.
  arXiv  Detail & Related papers  (2020-12-10T13:55:47Z)
• 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)
• Sensorimotor representation learning for an "active self" in robots: A model survey [10.649413494649293]
  In humans, these capabilities are thought to be related to our ability to perceive our body in space. This paper reviews the developmental processes of underlying mechanisms of these abilities. We propose a theoretical computational framework, which aims to allow the emergence of the sense of self in artificial agents.
  arXiv  Detail & Related papers  (2020-11-25T16:31:01Z)

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.