1,721,020 research outputs found
Abductive concept learning
No full textWe investigate how abduction and induction can be integrated into a common
learning framework. In particular, we consider an extension of Inductive
Logic Programming (ILP) for the case in which both the background and the
target theories are abductive logic programs and where an abductive notion
of entailment is used as the basic coverage relation for learning. This extended
learning framework has been called Abductive Concept Learning (ACL). In
this framework, it is possible to learn with incomplete background
information about the training examples by exploiting the hypothetical
reasoning of abduction. We also study how the ACL framework can be
used as a basis for multiple predicate learning.
An algorithm for ACL is developed by suitably extending the top-down ILP
method: the deductive proof procedure of Logic Programming is replaced by
an abductive proof procedure for Abductive Logic Programming. This
algorithm also incorporates a phase for learning integrity constraints by
suitably employing a system that learns from interpretations like ICL. The
framework of ACL thus integrates the two ILP settings of explanatory
(predictive) learning and confirmatory (descriptive) learning. The above
algorithm has been implemented into a system also called ACL\footnote{The learning systems developed in this work together
with sample experimental data can be found at the following
address: {\tt http://www-lia.deis.unibo.it/Software/ACL/}} Several
experiments have been performed that show the effectiveness of the ACL
framework in learning from incomplete data and its appropriate use for
multiple predicate learning
Argumentation for Propositional Logic and Nonmonotonic Reasoning
No full textArgumentation has played a significant role in understanding and unifying under a common framework different forms
of defeasible reasoning in AI. Argumentation is also close to
the original inception of logic as a framework for formalizing
human argumentation and debate. In this context, the purpose of this paper is twofold: to draw a formal connection between argumentation and classical reasoning (in the form of
Propositional Logic) and link this to support defeasible, NonMonotonic Reasoning in AI. To this effect, we propose Argumentation Logic and show properties and extensions thereof
Argumentation Logic
No full textWe propose a novel logic-based argumentation framework, called Argumentation Logic (AL), built upon a restriction of classical Propositional Logic (PL) as its underlying logic. This allows us to control the application of Reduction ad Absurdum (RA). In the case of classically consistent theories, AL and PL are equivalent, and RA is recovered through a notion of (non-)acceptability of arguments. In the case of classically inconsistent theories, AL is an extension of PL that does not trivialize, enjoying good logic-based argumentation and general logical properties
On the Extension of Argumentation Logic
Full text linkThis paper shows how Argumentation Logic can be further extended to cover more fully paraconsistent forms of logical reasoning. The extension is based on the notion of non-acceptable self-defeating arguments as a generalization of the Reductio ad Absurdum principle
Learning Multiple Predicates
No full textWe present an approach for solving some of the problems of
top-down Inductive Logic Programming systems when learning multiple predicates. The approach is based on an algorithm for learning abductive logic programs. Abduction is used to generate additional information that is useful for solving the problem of global inconsistency when learning multiple predicates
Learning with abduction
No full textWe investigate how abduction and induction can be integrated into a common learning framework through the notion of Abductive Concept Learning (ACL). ACL is an extension of Inductive Logic Programming (ILP) to the case in which both the background and the target theory are abductive logic programs and where an abductive notion of entailment is used as the coverage relation. In this framework, it is then possible to learn with incomplete information about the examples by exploiting the hypothetical reasoning of abduction. The paper presents the basic framework of ACL with its main characteristics. An algorithm for an intermediate version of ACL is developed by suitably extending the top-down ILP method and integrating this with an abductive proof procedure for Abductive Logic Programming (ALP). A prototype system has been developed and applied to learning problems with incomplete information
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