An Initial Memory Model for Virtual and Robot Companions Supporting Migration and Long-term Interaction
Ho, W.C.; Lim, M.Y.; Vargas, P. A.; Enz, S.; Dautenhahn, K.; Aylett, R.
Citation: Ho , W C , Lim , M Y , Vargas , P A , Enz , S , Dautenhahn , K & Aylett , R 2009 , ' An Initial Memory Model for Virtual and Robot Companions Supporting Migration and Long-term Interaction ' . in Procs of the 18th IEEE Int Symposium on Robot and Human Interactive Communication, RO-MAN . vol. 2009 , IEEE , pp. 277-284 , 18th IEEE Int Symposium on Robot & Human Interactive Communication , Toyama , Japan , 27-2 October . , 10.1109/ROMAN.2009.5326204
This work proposes an initial memory model for a long-term artificial companion, which migrates among virtual and robot platforms based on the context of interactions with the human user. This memory model enables the companion to remember events that are relevant or significant to itself or to the user. For other events which are either ethically sensitive or with a lower long-term value, the memory model supports forgetting through the processes of generalisation and memory restructuring. The proposed memory model draws inspiration from the human short-term and long-term memories. The short-term memory will support companions in focusing on the stimuli that are relevant to their current active goals within the environment. The long-term memory will contain episodic events that are chronologically sequenced and derived from the companion's interaction history both with the environment and the user. There are two key questions that we try to address in this work: 1) What information should the companion remember in order to generate appropriate behaviours and thus smooth the interaction with the user? And, 2) What are the relevant aspects to take into consideration during the design of memory for a companion that can have different types of virtual and physical bodies? Finally, we show an implementation plan of the memory model, focusing on issues of information grounding, activation and sensing based on specific hardware platforms.
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