H. Rahnama, M. Alirezaie, and A. Pentland (2021) A Neural-Symbolic Approach for User Mental Modeling: A Step Towards Building Exchangeable Identities, AAAI 2021 Spring Symposium on Combining Machine Learning and Knowledge Engineering

Abstract

Combining symbolic-reasoning and data learning in a unified double-loop learning system can contribute to the emergence of artificial intelligence solutions that are more adaptive to social and behavioural context. This paper presents a hybrid user modeling framework that relies on the integration of machine learning and reason- ing methods equipped with formally represented domain knowledge. We find that this approach contributes to the design of context-aware systems that require less data, manage bias better, provide better transparency and can handle data sparsity more effectively. We present the impact of our work in different social domains from building trusted digital surrogates to decentralization of social recommendation services. Our approach can construct software agents from identity and expertise of users and allows such entities to become more digitally portable. Our approach also contributes to the emergence of expertise sharing paradigms that are less prone to biases and more privacy preserving. The paper uses these domain applications to validate the scalability and versatility of our approach augmented with principles of open and transparent algorithms.