A preliminary study of computational complexity in non-monotonic reasoning

In this work we analyze existing complexity results in the area of non-monotonic reasoning in general and argumentation in particular. Even though the area of argumentation is based on solid theoretical foundations, its main problems rely on the computational complexity of the system that have so far been developed. In order to use argumentation in real time scenarios we must find an implementation with a reasonable response time. Complexity analysis of argument systems is an indispensable tool for addressing this taks. We expect that the development of this research line will result in a general analysis of the issues in complexity of argument systems, leading to an efficient implementation of a particular formalism, observation-based defeasible logic programming, that could be integrated in an intelligent agent architecture.Eje: Inteligencia artificialRed de Universidades con Carreras en Informática (RedUNCI

Ultimate approximations in nonmonotonic knowledge representation systems

We study fixpoints of operators on lattices. To this end we introduce the notion of an approximation of an operator. We order approximations by means of a precision ordering. We show that each lattice operator O has a unique most precise or ultimate approximation. We demonstrate that fixpoints of this ultimate approximation provide useful insights into fixpoints of the operator O. We apply our theory to logic programming and introduce the ultimate Kripke-Kleene, well-founded and stable semantics. We show that the ultimate Kripke-Kleene and well-founded semantics are more precise then their standard counterparts We argue that ultimate semantics for logic programming have attractive epistemological properties and that, while in general they are computationally more complex than the standard semantics, for many classes of theories, their complexity is no worse.Comment: This paper was published in Principles of Knowledge Representation and Reasoning, Proceedings of the Eighth International Conference (KR2002

A preliminary study of computational complexity in non-monotonic reasoning

In this work we analyze existing complexity results in the area of non-monotonic reasoning in general and argumentation in particular. Even though the area of argumentation is based on solid theoretical foundations, its main problems rely on the computational complexity of the system that have so far been developed. In order to use argumentation in real time scenarios we must find an implementation with a reasonable response time. Complexity analysis of argument systems is an indispensable tool for addressing this taks. We expect that the development of this research line will result in a general analysis of the issues in complexity of argument systems, leading to an efficient implementation of a particular formalism, observation-based defeasible logic programming, that could be integrated in an intelligent agent architecture.Eje: Inteligencia artificialRed de Universidades con Carreras en Informática (RedUNCI

Parainconsistency of credibility-based belief states

In our approach credibility of information plays an important role in modeling of both belief state and belief change [4]. It turns out that the credibility-based consequence operators used to define the notion of belief state tolerate inconsistency under some conditions

Aligning English Sentences with Abstract Meaning Representation Graphs using Inductive Logic Programming

abstract: In this thesis, I propose a new technique of Aligning English sentence words with its Semantic Representation using Inductive Logic Programming(ILP). My work focusses on Abstract Meaning Representation(AMR). AMR is a semantic formalism to English natural language. It encodes meaning of a sentence in a rooted graph. This representation has gained attention for its simplicity and expressive power. An AMR Aligner aligns words in a sentence to nodes(concepts) in its AMR graph. As AMR annotation has no explicit alignment with words in English sentence, automatic alignment becomes a requirement for training AMR parsers. The aligner in this work comprises of two components. First, rules are learnt using ILP that invoke AMR concepts from sentence-AMR graph pairs in the training data. Second, the learnt rules are then used to align English sentences with AMR graphs. The technique is evaluated on publicly available test dataset and the results are comparable with state-of-the-art aligner.Dissertation/ThesisMasters Thesis Computer Science 201