Learning Human-Environment Interaction through Predictive World Models for Intelligent Agents
Keywords:
predictive world models, human-environment interaction, intelligent agents, model-based reinforcement learning, socio-technical systems, AI governance, robustness, fairnessAbstract
Predictive world models have become a central component of intelligent agent design, enabling systems to anticipate the consequences of actions and to plan under uncertainty. However, learning human-environment interaction introduces challenges that extend beyond standard model-based reinforcement learning. Human environments are dynamic, socially structured, institutionally governed, and often only partially observable. This paper provides a system-level examination of predictive world models for agents that must learn and act within such environments. It begins by tracing the conceptual foundations of predictive modeling and model-based control, then discusses architectural choices for integrating perception, memory, action generation, and interactive prediction. The paper further analyzes data infrastructure requirements, including demonstration data, sensing, and feedback loops, and examines the structural trade-offs associated with model capacity, planning depth, latency, and generalization. It considers robustness, fairness, and safety as first-class socio-technical requirements rather than downstream checks. Governance and policy implications are explored through the lens of accountability, transparency, and institutional oversight. Deployment challenges such as edge computing, cost, monitoring, and model drift are also discussed. Finally, the paper addresses sustainability and long-term evolution, arguing that intelligent agents must be embedded in adaptive governance arrangements that allow for continuous evaluation, contestation, and revision. Throughout, the paper emphasizes structural trade-offs and system-level interactions, rather than algorithmic detail alone, in shaping agents that can responsibly learn human-environment interaction.
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