An agent-independent task learning framework
Year: 2008
Type: dissertation
Abstract: We propose that for all situated agents, the process of task learning has many elements in common. A better understanding of these elements would be beneficial to both engineers attempting to design new agents for task learning and completion, and also to scientists seeking to better understand natural task learning. Therefore, this dissertation sets out our characterisation of agent-independent task learning, and explores its grounding in nature and utility in practise. We achieve this chiefly through the construction and demonstration of two novel task learning systems. Cross-Channel Observation and Imitation Learning (COIL; Wood and Bryson, 2007a,b) is our adaptation of Deb Roy’s Cross-Channel Early Lexical Learning System (CELL; Roy, 1999; Roy and Pentland, 2002) for agent-independent task learning by imitation. The General Task Learning Framework (GTLF) is built upon many of the principles learned through the development of COIL, and can additionally facilitate multi-modal, lifelong learning of complex skills and skill hierarchies. Both systems are validated through experiments conducted in the virtual reality-style game domain of Unreal Tournament (Digital Extremes, 1999). By applying agent-independent learning processes to virtual agents of this kind, we hope that researchers will be more inclined to consider them on a par with robots as tools for learning research.
Author: Mark A. Wood
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: green
OpenAlex ID: W6705875