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World Models in Artificial Intelligence: Sensing, Learning, and Reasoning Like a Child

Javier Del Ser, Jesus L. Lobo, Heimo Müller, Andreas Holzinger

TL;DR

The paper argues that contemporary AI systems excel at pattern recognition but lack genuine reasoning, common sense, and causal understanding. It proposes a Piagetian constructivist framework in which AI builds dynamic World Models through interaction, assimilation/accommodation, and progressive generalization, moving beyond passive data fitting. Six interdependent research areas—physics-informed learning with embodied AI, neurosymbolic systems, causal inference, open-world learning, knowledge injection with human-in-the-loop, and trustworthy AI—form a cohesive blueprint for structured, interpretable intelligence. If realized, these World Models would yield AI with robust generalization, causal reasoning, and ethical alignment, enabling safer deployment in complex real-world environments.

Abstract

World Models help Artificial Intelligence (AI) predict outcomes, reason about its environment, and guide decision-making. While widely used in reinforcement learning, they lack the structured, adaptive representations that even young children intuitively develop. Advancing beyond pattern recognition requires dynamic, interpretable frameworks inspired by Piaget's cognitive development theory. We highlight six key research areas -- physics-informed learning, neurosymbolic learning, continual learning, causal inference, human-in-the-loop AI, and responsible AI -- as essential for enabling true reasoning in AI. By integrating statistical learning with advances in these areas, AI can evolve from pattern recognition to genuine understanding, adaptation and reasoning capabilities.

World Models in Artificial Intelligence: Sensing, Learning, and Reasoning Like a Child

TL;DR

The paper argues that contemporary AI systems excel at pattern recognition but lack genuine reasoning, common sense, and causal understanding. It proposes a Piagetian constructivist framework in which AI builds dynamic World Models through interaction, assimilation/accommodation, and progressive generalization, moving beyond passive data fitting. Six interdependent research areas—physics-informed learning with embodied AI, neurosymbolic systems, causal inference, open-world learning, knowledge injection with human-in-the-loop, and trustworthy AI—form a cohesive blueprint for structured, interpretable intelligence. If realized, these World Models would yield AI with robust generalization, causal reasoning, and ethical alignment, enabling safer deployment in complex real-world environments.

Abstract

World Models help Artificial Intelligence (AI) predict outcomes, reason about its environment, and guide decision-making. While widely used in reinforcement learning, they lack the structured, adaptive representations that even young children intuitively develop. Advancing beyond pattern recognition requires dynamic, interpretable frameworks inspired by Piaget's cognitive development theory. We highlight six key research areas -- physics-informed learning, neurosymbolic learning, continual learning, causal inference, human-in-the-loop AI, and responsible AI -- as essential for enabling true reasoning in AI. By integrating statistical learning with advances in these areas, AI can evolve from pattern recognition to genuine understanding, adaptation and reasoning capabilities.

Paper Structure

This paper contains 17 sections, 1 figure.

Table of Contents

  1. Can Modern AI Systems reason? Are we already there?
  2. What is Reasoning in AI? Why is modern AI unable to understand and reason? How could World Models surpass these limitations?
  3. Towards a Constructivist Theory of World Models
  4. Cognitive Schema and World Models
  5. Stages of Learning and Cognitive Development
  6. Learning through Interaction
  7. Assimilation, Accommodation, and World Model Updates
  8. Biases and the Need for Quality Data
  9. Generalization, Abstract Thought and Management of the Unknown
  10. Key Research Areas for Constructing World Models
  11. Physics-Informed Machine Learning and Embodied Artificial Intelligence
  12. Neurosymbolic Systems
  13. Causal Inference
  14. Open-World Machine Learning
  15. Knowledge Injection and Human-in-the-Loop (HiTL)
  16. ...and 2 more sections

Figures (1)

  • Figure 1: We envision a World Model built upon 6 research areas: 1) embodied AI and physics-informed machine learning, allowing AI to interact and grasp fundamental relationships about the real world; neuro-symbolic approaches that integrate statistical learning and symbolic representations of the world; 3) causal inference, to capture action-effect mechanisms observed in its collected data; 4) open-world machine learning, to ensure adaptability to unknown situations; (5) Human-in-the-Loop, infusing common sense and oversight; and (6) Trustworthy and Responsible AI, fostering accountability and reliability. Together, these key research area can realize a reasoning-capable, human-centered approach to World Models in AI.