Paper 120 (In-Use track)

Synthesising Machine Learning and Semantic Technologies for Enhanced Teaching of Virtual Agents

Author(s): Nicole Merkle, Stefan Zander

Abstract: Major problems of autonomous virtual agents in complex IoT environments are their lack of sufficient startup knowledge as well as their incapability to adequately adjust their internal knowledge base during runtime to specific user requirements and preferences. In Ambient Assisted Living as well as Health-Care use cases this is problematic, since an agent has to expediently operate from the beginning of its lifecycle and adequately address the target users’ needs. However, without prior knowledge about the user and the environment this is hardly possible. Our approach addresses these problems by providing a semantic knowledge representation framework in order to enable a simulator to simulate device events and user activities based on semantic task- and ambient descriptions. Through that simulated environment, we allow agents to learn the best matching actions and to adjust their policies based on generated datasets. In this way, the target user benefits from adequate and approved agent services.

Keywords: Agent-based systems; Semantic Web; Machine Learning; Simulation; IoT; WoT

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