Gustavo Lacerda

We outline an abstract approach to defeasible reasoning and argumentation which
includes many existing formalisms, including default logic, extended logic programming,
non-monotonic modal logic and auto-epistemic logic, as special cases. We show, in particular,
that the admissibility" semantics for all these formalisms has a natural argumentation theoretic
interpretation and proof procedure, which seem to correspond well with informal
argumentation.

We present a family of dialectic proof procedures for the admissibility semantics
of assumption-based argumentation. These proof procedures are defined for any
conventional logic formulated as a collection of inference rules and show how any
such logic can be extended to a dialectic argumentation system.
The proof procedures find a set of assumptions, to defend a given belief, by starting
from an initial set of assumptions that supports an argument for the belief
and adding defending assumptions incrementally to counter-attack all attacks.
...
The novelty of our
approach lies mainly in its use of backward reasoning to construct arguments
and potential arguments, and the fact that the proponent and opponent can
attack one another before an argument is completed. The definition of winning
strategy can be implemented directly as a non-deterministic program, whose
search strategy implements the search for defences.

In conventional logic, beliefs are derived from axioms, which are held to be beyond
dispute. In everyday argumentation, however, beliefs are based on assumptions, which
can be questioned and disputed...

The purpose of this paper is to study the fundamental mechanism, humans use in
argumentation, and to explore ways to implement this mechanism on computers.
Roughly, the idea of argumentational reasoning is that a statement is believable if it can be
argued successfully against attacking arguments.

This book presents, within a conceptually unified theoretical framework, a body of methods that have been developed over the past fifteen years for building and simulating qualitative models of physical systems (bathtubs, tea kettles, automobiles, the physiology of the body, chemical processing plants, control systems, electrical circuits, and the like) where knowledge of that system is incomplete. The primary tool for this work is the author's QSIM algorithm which is discussed in detail.