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Personalized Collaboration 267
only evaluation were deemed more successful. Except task-oriented conversational moves,
REA can engage in social dialog moves analogous to forms of small talk – questions ("It's
a nice morning, isn't it?"), statements, and stories not relating to the task.
Small talk, also referred to as phatic communication (Malinowski, 1923), is a talk in
which interpersonal goals are emphasized and task goals deemphasized (e.g. social chat,
conversational stories). Besides its transitional function of moving into a conversation that
might otherwise be uncomfortable, it helps people establish expertise and credentials (Jaworski,
2006). Small talk avoids face threat by using safe topics, establishes common
ground by topics that are clearly in the current context, increases coordination between
participants by allowing them to synchronize short units of talk and nonverbal acknowledgments,
and allows for reciprocal appreciation of each other's contributions, therefore
maintains solidarity and increases familiarity and affect (Bickmore, 2003).
9.3.2 Modeling Social Dialogue
In the remainder of this section we describe REA's approach to modeling mixed socialand-task
dialogue, some computational details are simplified due to brevity, and a full
account can be found in the original work (ibid, p. 64–70).
REA's relationship model is based on the Svennevig's model described earlier, and
uses three of its relational dimensions: familiarity depth, familiarity breath, and solidarity.
Each dimension is normalized to unit scalar (ranging from zero to one), and is updated
dynamically during the interaction with the user. REA maintains conversational topics
that she can engage in, for each the minimum and the maximum value of social invasiveness
are specified. Then, as conversation progresses (topics are introduced) familiarity
depth is updated as the degree of social invasiveness of topics that were already introduced
(i.e. the more intrusive topics are introduced, the higher familiarity depth gets),
familiarity breadth corresponds to the ratio of how many of the available topics were already
introduced, and solidarity increases gradually (linearly) with each dialogue move as
the number of dialogue moves is limited and they cannot be repeated.
Each available REA's dialogue move corresponds to a speech act (e.g. story, query,
statement) and is about a predefined set of topics; thus the coherency of two sets of topics
can be easily computed. Then, the value of face threat of introducing a particular dialogue
move is computed according to the current relational state as weighted sum of: threat due
to familiarity, intrinsic face threat of a corresponding speech act, and amount of threat due
to topic incoherency. The weights in this sum provide a flexible mechanism for implementing
different agent personalities, normal, goal-oriented, and chatty agents were considered.
With the ability to evaluate a single step in dialogue (as described in previous paragraphs),
REA's discourse planner is capable to interleave small talk and the task using an
activation network-based approach based on Maes' Do the Right Thing architecture (Maes, 1989).
Nodes in the network represent conversational moves and edges between them represent
various enabling and disabling conditions among the moves (Figure 9-6).
Dialogue planning is seen as a spreading activation process that uses information
from the current state, relational model and task goals to determine which moves are more
likely to succeed. Dialogue plans correspond to paths in the network. Activation process is