Argumentation theory conceptualises the human practice of debating. Implemented as
computational argumentation it enables a computer to perform a virtual debate. Using
existing knowledge from research into argumentation theory, this thesis investigates
the potential of computational argumentation within biology.
As a form of non-monotonic reasoning, argumentation can be used to tackle inconsistent
and incomplete information - two common problems for the users of biological
data. Exploration of argumentation shall be conducted by examining these issues
within one biological subdomain: in situ gene expression information for the developmental
mouse.
Due to the complex and often contradictory nature of biology, occasionally it
is not apparent whether or not a particular gene is involved in the development of
a particular tissue. Expert biological knowledge is recorded, and used to generate
arguments relating to this matter. These arguments are presented to the user in
order to help him/her decide whether or not the gene is expressed.
In order to do this, the notion of argumentation schemes has been borrowed from
philosophy, and combined with ideas and technologies from arti cial intelligence. The
resulting conceptualisation is implemented and evaluated in order to understand the
issues related to applying computational argumentation within biology.
Ultimately, this work concludes with a discussion of Argudas - a real world tool
developed for the biological community, and based on the knowledge gained during
this work
