Toward Universal and Interpretable World Models for Open-ended Learning Agents
<|reference_start|>Toward Universal and Interpretable World Models for Open-ended Learning Agents: We introduce a generic, compositional and interpretable class of generative world models that supports open-ended learning agents. This is a sparse class of Bayesian networks capable of approximating a broad range of stochastic processes, which provide agents with the ability to learn world models in a manner that may be both interpretable and computationally scalable. This approach integrating Bayesian structure learning and intrinsically motivated (model-based) planning enables agents to actively develop and refine their world models, which may lead to open-ended learning and more robust, adaptive behavior.<|reference_end|>
@article{da costa2024toward,
    title={Toward Universal and Interpretable World Models for Open-ended Learning
  Agents},
    author={Lancelot Da Costa},
    journal={NeurIPS 2024 Workshop on Intrinsically Motivated Open-ended
  Learning (IMOL)},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.18676},
    primaryClass={cs.AI cs.MA q-bio.NC}
}