Publikation
Adaptive User-centered Neuro-symbolic Learning for Multimodal Interaction with Autonomous Systems
Amr Gomaa; Michael Feld
In: AI & HCI Workshop at the 40th International Conference on Machine Learning (ICML). AI&HCI Workshop @ ICML, located at icml, July 29, Honolulu, Hawaii, USA, PMLR, 2023.
Zusammenfassung
Recent advances in machine learning, particularly deep learning, have enabled autonomous systems to perceive and comprehend objects and their environments in a perceptual subsymbolic manner. These systems can now perform object detection, sensor data fusion, and language understanding tasks. However, there is a growing need to enhance these systems to understand objects and their environments more conceptually and symbolically. It is essential to consider both the explicit teaching provided by humans (e.g., describing a situation or explaining how to act) and the implicit teaching obtained by observing human behavior (e.g., through the system's sensors) to achieve this level of powerful artificial intelligence. Thus, the system must be designed with multimodal input and output capabilities to support implicit and explicit interaction models. In this position paper, we argue for considering both types of inputs, as well as human-in-the-loop and incremental learning techniques, for advancing the field of artificial intelligence and enabling autonomous systems to learn like humans. We propose several hypotheses and design guidelines and highlight a use case from related work to achieve this goal.
Projekte
CAMELOT - Kontinuierliches adaptives maschinelles Lernen für Kontrollübergabe-Situationen,
SC_Gomaa - TeachTAM: Machine Teaching with Hybrid Neurosymbolic Reinforcement Learning; The Apprenticeship Model
SC_Gomaa - TeachTAM: Machine Teaching with Hybrid Neurosymbolic Reinforcement Learning; The Apprenticeship Model