Pragmatic Knowledge:
Dialog and Agents
Researchers:
Guido Boella
Rossana Damiano
Previous Research
Current Research
Areas:
 DIALOG
COHERENCE
When the user utters a new turn,
the system looks for a coherence link with the previous dialog context.
Coherence links are based on the participants' underlying intentions,
both at linguistic and domain level [Ardissono,
Boella and Lesmo 2000]. The goals underlying a certain turncould
berelated to the interlocutor's pending goals (goal adherence and goal
adoption), or could be related to a plan that the speaker is carrying on.
BACK
MISUNDERSTANDINGS
By exploiting the above-mentioned
notion of coherence, a model of misunderstandings has been built [Ardissono,
Boella and Damiano 1997,Ardissono,
Boella and Damiano 1998]. When
a participant detects a loss of coherence in the interaction, a misunderstanding
is hypothesized and a the goal to repair it is consequently set. In order
to restore the common ground and to realign the diverging interpretations,
the repairing agent looks for an alternative and coherent interpretation
of the past turns, up to the misunderstood one. Then, he proceeds
to repair his own interpretation (self-repair) or the other's (other-repair),
depending on who is the misunderstandig agent.
BACK
AGENTS
AND COOPERATION
We are currently developing a
utility-based approach to cooperation among agents [ Boella,
Damiano and Lesmo 1999, Boella,
Damiano and Lesmo 1999b, Boella 2000].
Agents - we assume - are rational, and they choose among alternative courses
of action by exploiting a multi-attribute utility function that expresses
the extent to which a certain course of action contributes to the achievement
of the agent's goals. Most interactions among agents, however, involve
some form of cooperation: when a group of agents is cooperating to a shared
plan, a combination of individual utility and grouputility should be used.
Each socially responsible agent, when evaluating alternative courses of
action, considers also the consequences on the others' choices, in the
light of this hybrid measure of group and individual utility. The effort
to maximize it leads to some interesting behavors, including goal adoption,
helpfulness, appropriate generation of communicative acts, and so on: all
these phenomena contibute to improve coordination and to reduce conflicts
among the individuals' actions.
BACK
NARRATIVE
AND PLANS
A dialog agent for interpreting Natural Language [Ardissono,
Boella and Lesmo 2000], whose knowledge is encoded in plan libraries,
can be extended to create a system that undestands narratives that contain
descriptions of domain and linguistic actions [Boella,
Damiano and Lesmo 1999c, Boella,
Damiano and Lesmo 1999d]. The system incrementally reconstructs coherence
links among described actions, by attributing intentions to the described
agents.
Moreover, the intention attribution activity can be extended to the
narrator's linguistic planning, by identifying communicative intentions
underlying the structure of the narrative. At the same time, the dialog
agent architecture lends itself to build a system that produces a description
his own activity.
BACK
Previous Research
The research in the plan recognition and pragmatics area
is concerned with the study and definition of techniques for modeling cooperative
interaction among agents [Ardissono,
Boella and Sestero 1996]. We are developing a prototype consultation
system for a restricted domain (the University domain)[Adissono,
Lesmo, Lombardo and Sestero 1993].
The consultation system has a plan-based representation
of the knowledge about actions; it uses the knowledge about how people
act when they try to obtain their goals in order to interpret the behavior
of an observed agent, and to identify the intentions underlying his communication
with other agents.
Our system is based on the GULL semantic interpreter for
the Italian language and accepts Italian NL sentences in input. The system
reasons on the sentences in input to identify the plans of the speaker;
this identification is important in order to select the contents of a cooperative
response to the user's questions. The input sentences undergo a sequence
of interpretation steps: syntactic, semantic and contextual interpretation;
speech-act interpretation and local domain-level analysis (where the domain-level
actions addressed by the speech acts are identified); integration of the
local interpretation of the input into the previous pragmatic context.
We are currently working at the answer generation phase, in order to decide
which contents should be included into the answer of the system, in order
to make it as cooperative as possible.
The system processes the input by exploiting different
knowledge sources:
-
A Domain plan library which contains the description of the
domain-level actions[Ardissono,
Lesmo, Lombardo and Sestero 1993].
-
A Speech-act library, which contains the description of the
communicative acts; this library describes direct and indirect speech acts
(and politeness communication strategies in general)[Ardissono,
Boella and Lesmo 1995], [Ardissono,
Boella and Sestero 1995], [Ardissono,
Boella and Sestero 1996].
-
An Agent Modelling library, which contains recipes describing
the process of plan formation and plan execution [Ardissono,
Boella and Lesmo 1996][Ardissono,
Boella and Lesmo 1997].
The system maintains a User Model which stores the description
of a specific user and is updated during the analysis of his input sentences
[Ardissono,
Lesmo and Sestero 1994], [Ardissono
and Sestero 1996]. The contents of the User Model are represented by
means of semantic nets, representing assumptions about the user's beliefs,
goals, knowledge, properties, preferences and intentions. Stereotypical
information about users is exploited to predict typical features of the
user, on the basis of the class of agents to which he belongs. The contents
of the User Model are exploited in order to reduce the number of hypotheses
on the user's intentions (when interpretation ambiguities arise) and to
decide which contents should be added to the answers of the system in order
to make them suitable to the background knowledge of the user [Ardissono
and Cohen 1995], [Ardissono,
Boella, Lesmo, Rizzo and Sestero 1996 ].
 Bibliography
on dialog and agents
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