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Fig. 7.1
Nefertari playing
Senet. Painting in tomb of
Egyptian Queen Nefertari
around a table and engage in face-to-face interactions, reading each other's gestures
and facial expressions. Examples of such rich social interactions can be identified
when we look at more recent examples of board games. Players laugh with each
other when someone makes an ugly drawing playing Pictionary, yell at each other
when someone makes a bad deal at Monopoly, use their facial expressions to bluff
while playing Poker, or can even mock somebody who does not know the answer to
a simple Trivial Pursuit question.
However, the static nature of tabletop games can limit the scope of realisable
games (Magerkurth et al.
2004
) when compared to console or PC games. An area
named computer augmented tabletop games (Magerkurth et al.
2005
), tries to main-
tain the social aspects of these traditional board games and augment them with
computerized benefits. Computer augmented tabletop games gives us the best of two
worlds: the interaction and communication between the players, who sit around the
same table, facing each other at an intimate distance, and the computing support
that can enhance games with visual and audio effects or relieve players from tasks
such as score keeping. One of the benefits from this hybrid form of interaction is
the possibility of creating artificial opponents. However, opponents in such novel
environments are still scarce and generally don't have any kind of embodiment or
believable social behaviour. The social inability of current artificial opponents results
in humans perceiving them as not socially present.
This paper addresses the possibility of using today's technology to maintain user's
perceived social presence towards an artificial opponent steadier over longer periods
of time. In order to do so, we start by reviewing some literature on social presence
and performing some initial studies. Afterwards we establish guidelines for creating
socially present board game opponents and following these guidelines, we have
developed a scenario where an artificial opponent plays Risk against three human
