Argumentation is a vital aspect of intelligent behaviour by humans. It provides the means for comparing
information by analysing pros and cons when trying to make a decision. Formalising argumentation in
computational environment has become a topic of increasing interest in artificial intelligence research
over the last decade.
Computational argumentation involves reasoning with uncertainty by making use of logic in order
to formalize the presentation of arguments and counterarguments and deal with conflicting information.
A common assumption for logic-based argumentation is that an argument is a pair where Φ is
a consistent set which is minimal for entailing a claim α. Different logics provide different definitions
for consistency and entailment and hence give different options for formalising arguments and counterarguments.
The expressivity of classical propositional logic allows for complicated knowledge to be
represented but its computational cost is an issue. This thesis is based on monological argumentation
using classical propositional logic [12] and aims in developing algorithms that are viable despite the
computational cost. The proposed solution adapts well established techniques for automated theorem
proving, based on resolution and connection graphs. A connection graph is a graph where each node is
a clause and each arc denotes there exist complementary disjuncts between nodes. A connection graph
allows for a substantially reduced search space to be used when seeking all the arguments for a claim
from a given knowledgebase. In addition, its structure provides information on how its nodes can be linked
with each other by resolution, providing this way the basis for applying algorithms which search for
arguments by traversing the graph. The correctness of this approach is supported by theoretical results,
while experimental evaluation demonstrates the viability of the algorithms developed. In addition, an
extension of the theoretical work for propositional logic to first-order logic is introduced
