Abstract
This paper deals with an approach to opponent-modelling
in argumentation-based persuasion dialogues. It assumes that dialogue
participants (agents) have models of their opponents' knowledge, which
can be augmented based on previous dialogues. Specifically, previous di-
alogues indicate relationships of support, which refer both to arguments
as abstract entities and to their logical constituents. The augmentation
of an opponent model relies on these relationships. An argument ex-
ternal to an opponent model can augment that model with its logical
constituents, if that argument shares support relationships with other
arguments that can be constructed from that model. The likelihood that
the constituents of supporting arguments will in fact be known to an
opponent, varies according to support types. We therefore provide cor-
responding quantifications for each support type.
in argumentation-based persuasion dialogues. It assumes that dialogue
participants (agents) have models of their opponents' knowledge, which
can be augmented based on previous dialogues. Specifically, previous di-
alogues indicate relationships of support, which refer both to arguments
as abstract entities and to their logical constituents. The augmentation
of an opponent model relies on these relationships. An argument ex-
ternal to an opponent model can augment that model with its logical
constituents, if that argument shares support relationships with other
arguments that can be constructed from that model. The likelihood that
the constituents of supporting arguments will in fact be known to an
opponent, varies according to support types. We therefore provide cor-
responding quantifications for each support type.
Original language | English |
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Title of host publication | Theory and Applications of Formal Argumentation |
Subtitle of host publication | Third International Workshop, TAFA 2015, Buenos Aires, Argentina, July 25-26, 2015, Revised selected papers |
Publisher | Springer Berlin Heidelberg |
Pages | 128-145 |
Volume | LNAI 9524 |
ISBN (Electronic) | 9783319284606 |
ISBN (Print) | 9783319284590 |
DOIs | |
Publication status | E-pub ahead of print - 7 Jan 2016 |