Balancing between cognitive and semantic acceptability of arguments

This paper addresses the problem concerning approximating human cognitions and semantic extensions regarding acceptability status of arguments. We introduce three types of logical equilibriums in terms of satisfiability, entailment and semantic equivalence in order to analyse balance of human cognitions and semantic extensions. The generality of our proposal is shown by the existence conditions of equilibrium solutions. The applicability of our proposal is demonstrated by the fact that it detects a flaw of argumentation actually taking place in an online forum and suggests its possible resolution

An Efficient Java-Based Solver for Abstract Argumentation Frameworks: jArgSemSAT

Dung’s argumentation frameworks are adopted in a variety of applications, from argument-mining, to intelligence analysis and legal reasoning. Despite this broad spectrum of already existing applications, the mostly adopted solver—in virtue of its simplicity—is far from being comparable to the current state-of-the-art solvers. On the other hand, most of the current state-of-the-art solvers are far too complicated to be deployed in real-world settings. In this paper we provide and extensive description of jArgSemSAT, a Java re-implementation of ArgSemSAT. ArgSemSAT represents the best single solver for argumentation semantics with the highest level of computational complexity. We show that jArgSemSAT can be easily integrated in existing argumentation systems (1) as an off-the-shelf, standalone, library; (2) as a Tweety compatible library; and (3) as a fast and robust web service freely available on the Web. Our large experimental analysis shows that—despite being written in Java—jArgSemSAT would have scored in most of the cases among the three bests solvers for the two semantics with highest computational complexity—Stable and Preferred—in the last competition on computational models of argumentation

A computational method for defeasible argumentation based on a recursive warrant semantics

Abstract. In a recent pape

Formalisation and logical properties of the maximal ideal recursive semantics for weighted defeasible logic programming

Possibilistic defeasible logic programming (P-DeLP) is a logic programming framework which combines features from argumentation theory and logic programming, in which defeasible rules are attached with weights expressing their relative belief or preference strength. In P-DeLP,a conclusion succeeds if there exists an argument that entails the conclusion and this argument is found to be undefeated by a warrant procedure that systematically explores the universe of arguments in order to present an exhaustive synthesis of the relevant chains of pros and cons for the given conclusion. Recently, we have proposed a new warrant recursive semantics for P-DeLP, called Recursive P-DeLP (RP-DeLP for short), based on the claim that the acceptance of an argument should imply also the acceptance of all its sub-arguments which reflect the different premises on which the argument is based. This paper explores the relationship between the exhaustive dialectical analysis-based semantics of P-DeLP and the recursive-based semantics of RP-DeLP, and analyses a non-monotonic inference operator for RP-DeLP which models the expansion of a given program by adding new weighted facts associated with warranted conclusions. Given the recursive-based semantics of RP-DeLP, we have also implemented an argumentation framework for RP-DeLP that is able to compute not only the output of warranted and blocked conclusions, but also explain the reasons behind the status of each conclusion. We have developed this framework as a stand-alone application with a simple text-based input/output interface to be able to use it as part of other artificial intelligence systemsThis research was partially supported by the Spanish projects EdeTRI (TIN2012-39348-C02-01) and AT (CONSOLIDER- INGENIO 2010, CSD2007-00022)

An Implementation of Splitting for Dung Style Argumentation Frameworks

Argumentation and reasoning have been an area of research in such disciplines as philosophy, logic and artificial intelligence for quite some time now. In the area of AI, knowledge needed for reasoning can be represented using various kinds of representation systems. The natural problem posed by this fact is that of possible incompatibility between heterogeneous systems as far as communication between them is concerned. This imposes a limitation on the possibility of extending smaller knowledge bases to larger ones. In order to facilitate a common platform for exchange across the systems unified formalisms for the different approaches to knowledge representation are required. This was the motivation for Dung [11] to propose in his 1995 paper an approach that later came to be known as an abstract argumentation framework. Roughly speaking, Dung's arguments are abstract entities which are related to each other by the means of conflicts between them. An intuitive graphical representation of Dung style framework is a graph whose nodes stand for arguments and whose edges stand for conflicts. A framework postulated this way is on one hand too general to be used on its own, but on the other hand it is general enough as to allow for varied extensions increasing its expressiveness, which indeed have been proposed. They include value-based argumentation frameworks by Bench-Capon et al. [6], preference-based argumentation frameworks by Amgoud and Cayrol [1] and bipolar argumentation frameworks by Brewka and Woltran [7], to name a few. The present thesis is concerned with yet another variation of Dung's framework: the concept of splitting. It was developed by Baumann [4] with one of the underlying purposes being that the computation time in frameworks which have been split into two parts and then computed separately may show some improvement in comparison to their variant without splitting. It was one of the main tasks of my work to develop an efficient algorithm for the splitting operation based on the theoretical framework given in [4]. On the other hand I hoped to confirm the expectation that splitting can indeed make a computation perform better