Searching with keywords "abduction-based" argumentation Kakas
Kowalski, Toni (1996) - Abstract Argumentation
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.
Dung, Kowalski, Toni (2005) - Dialectic proof procedures for assumption-based, admissible 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...
Phan Minh Dung - ON THE ACCEPTABILITY OF ARGUMENTS AND ITS FUNDAMENTAL ROLE IN NONMONOTONIC REASONING, LOGIC PROGRAMMING AND N-PERSONS GAMES
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.
Panzarasa, Jennings, Norman - Formalizing Collaborative Decision-Making and Practical Reasoning in Multi-agent Systems
Kenneth Forbus - Exploring analogy in the large
A second problem with first-principles qualitative simulation algorithms as models of
human common sense reasoning is that their predictions tend to include a large number of
spurious behaviors (Kuipers 1994), behaviors that logically follow from the lowresolution
qualitative descriptions that they use as input, but are not in fact physically
possible. In engineering applications, such behaviors are generally pruned by using
more detailed knowledge (e.g., specific equations or numerical values). But that is not a
viable option for modeling the common sense of the person on the street, who is capable
of making reasonable predictions even without such detailed information.
We (Forbus & Gentner, 1997) suggest that the solution to this puzzle lies in our use of
within-domain analogies (e.g., literal similarity) in common sense reasoning. We claim
that a psychological account of qualitative reasoning should rely heavily on analogical
reasoning in addition to reasoning from first principles. Qualitative predictions of
behavior can be generated via analogical inference from prior observed behaviors,
described qualitatively. Predictions based on experience reduce the problems of purely
first-principles qualitative reasoning, because they are limited to what one has seen. The
set of observed behaviors, barring misinterpretations, does not include physically or
logically impossible occurrences. Predicted behaviors that are physically impossible
might still be generated, since an experience might be applied to a situation containing
differences that make it irrelevant, but there would still be many fewer of them than
would be generated by a first-principles algorithm. Moreover, predictions from
experience have the advantage of being more likely, since they have actually occurred,
rather than simply being logically possible, which greatly reduces the number of
predicted behaviors.
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.
Dung, Kowalski, Toni (2005) - Dialectic proof procedures for assumption-based, admissible 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...
Phan Minh Dung - ON THE ACCEPTABILITY OF ARGUMENTS AND ITS FUNDAMENTAL ROLE IN NONMONOTONIC REASONING, LOGIC PROGRAMMING AND N-PERSONS GAMES
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.
Panzarasa, Jennings, Norman - Formalizing Collaborative Decision-Making and Practical Reasoning in Multi-agent Systems
Kenneth Forbus - Exploring analogy in the large
A second problem with first-principles qualitative simulation algorithms as models of
human common sense reasoning is that their predictions tend to include a large number of
spurious behaviors (Kuipers 1994), behaviors that logically follow from the lowresolution
qualitative descriptions that they use as input, but are not in fact physically
possible. In engineering applications, such behaviors are generally pruned by using
more detailed knowledge (e.g., specific equations or numerical values). But that is not a
viable option for modeling the common sense of the person on the street, who is capable
of making reasonable predictions even without such detailed information.
We (Forbus & Gentner, 1997) suggest that the solution to this puzzle lies in our use of
within-domain analogies (e.g., literal similarity) in common sense reasoning. We claim
that a psychological account of qualitative reasoning should rely heavily on analogical
reasoning in addition to reasoning from first principles. Qualitative predictions of
behavior can be generated via analogical inference from prior observed behaviors,
described qualitatively. Predictions based on experience reduce the problems of purely
first-principles qualitative reasoning, because they are limited to what one has seen. The
set of observed behaviors, barring misinterpretations, does not include physically or
logically impossible occurrences. Predicted behaviors that are physically impossible
might still be generated, since an experience might be applied to a situation containing
differences that make it irrelevant, but there would still be many fewer of them than
would be generated by a first-principles algorithm. Moreover, predictions from
experience have the advantage of being more likely, since they have actually occurred,
rather than simply being logically possible, which greatly reduces the number of
predicted behaviors.