Abstract
In teamwork when a user and an agent are working together
on a joint task it may be important to share information in order to
determine the appropriate course of action. However, communication between
agents and users can constitute costly user interruptions. One of
the most important issue concerning the initiation of information sharing
in teamwork is the ability to accurately estimate the cost and benefit
arising from those interruptions. While cost estimation of interruptions
has been investigated in prior works, all of those works assumed either a
large amount of information existed about each user, or only a small number
of states needed consideration. This paper presents a novel synthesis
between Collaborative Filtering methods together with classification algorithms
tools in order to create a fast learning algorithm. This algorithm
exploits the similarities between users in order to learn from known users
to new but similar users and therefore demands less information on each
user. Experimental results indicate the algorithm significantly improves
system performance even with a small amount of data on each user.
Original language | American English |
---|---|
Title of host publication | MIMS09 workshop, AAMAS 2009 |
State | Published - 2009 |