An abstract, argumentation-theoretic approach to default reasoning
Artificial Intelligence
JELIA '02 Proceedings of the European Conference on Logics in Artificial Intelligence
An Argumentation Theoretic Semantics Based on Non-Refutable Falsity
ICLP '94/NMELP '94 Selected papers from the Workshop on Non-Monotonic Extensions of Logic Programming
Dialectic proof procedures for assumption-based, admissible argumentation
Artificial Intelligence
Revised stable models – a semantics for logic programs
EPIA'05 Proceedings of the 12th Portuguese conference on Progress in Artificial Intelligence
A Classification Theory Of Semantics Of Normal Logic Programs: Ii. Weak Properties
Fundamenta Informaticae
Argumentation and answer set programming
Logic programming, knowledge representation, and nonmonotonic reasoning
Adaptive reasoning for cooperative agents
INAP'09 Proceedings of the 18th international conference on Applications of declarative programming and knowledge management
Fundamenta Informaticae - Logic, Language, Information and Computation
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We introduce an original 2-valued semantics for Normal Logic Programs (NLPs) extending the well-known Argumentation work of Phan Minh Dung on Admissible Arguments and Preferred Extensions. In the 2-valued Approved Models Semantics set forth, an Approved Model (AM) correspond to the minimal positive strict consistent 2-valued completion of a Dung Preferred Extension. The AMs Semantics enjoys several non-trivial useful properties such as (1) Existence of a 2-valued Model for every NLP; (2) Relevancy, and (3) Cumulativity. Crucially, we show that the AMs Semantics is a conservative extension to the Stable Models (SMs) Semantics in the sense that every SM of a NLP is also an AM, thus providing every NLP with a model: a property not enjoyed by SMs. Integrity constraints, written in a simpler way, are introduced to identify undesired semantic scenarios, whilst permitting these to be produced nevertheless. We end the paper with some conclusions and mention of future work.