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