Artificial Theories of Mind & Body

by Neo360

Investigator: Harold Soh.

The principal aim of this project is to develop core techniques for learning models of other (human) agents: Artificial Theories of Mind and Body (AToM/B).

We intend to build novel hybrid techniques that learn flexible deep models, yet are able to leverage prior knowledge that is expressive and human-interpretable. In essence, we aim to bridge theory-driven and data-driven approaches to model development.

While our goal is to develop general learning methods, we will ground our research in assistive scenarios; we plan to integrate our models into decision-making frameworks and evaluate our approach experimentally using assistive tasks with human subjects.

Funded by: AI Singapore

Further Reading:

  • Hyperprior Induced Unsupervised Disentanglement of Latent Representations, Abdul Fatir Ansari and Harold Soh, AAAI 2019
  • Semantically-Regularized Logic Graph Embeddings, Yaqi Xie, Ziwei Xu, Kuldeep Meel, Mohan Kankanhalli, and Harold Soh, Neural Information Processing Systems (NeurIPS), 2019

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