Argumentation semantics as formal discussion

In the current chapter, we interpret a number of mainstream argumentation semantics by means of structured discussion. The idea is that an argument is justified according to a particular argumentation semantics iff it is possible to win a discussion of a particular type. Hence, different argumentation semantics correspond to different types of discussion. Our aim is to provide an overview of what these discussions look like, and their formal correspondence to argumentation semantics

Ranking Semantics Based on Subgraphs Analysis

An abstract argumentation framework [15] consists of a direct graph where nodes represent arguments and arrows represent an attack relation among arguments. A semantics is used to evaluate arguments’ acceptability. In the labelling approach [7], this evaluation is done by assigning to each argument a label in, out or undec, meaning that the argument is considered consistently acceptable, non-acceptable or undecided (i.e. no decision can be taken on arguments’ acceptability)

Algorithms for argument systems

Argument systems are computational models that enable an artificial intelligent agent to reason via argumentation. Basically, the computations in argument systems can be viewed as search problems. In general, for a wide range of such problems existing algorithms lack five important features. Firstly, there is no comprehensive study that shows which algorithm among existing others is the most efficient in solving a particular problem. Secondly, there is no work that establishes the use of cost-effective heuristics leading to more efficient algorithms. Thirdly, mechanisms for pruning the search space are understudied, and hence, further pruning techniques might be neglected. Fourthly, diverse decision problems, for extended models of argument systems, are left without dedicated algorithms fine-tuned to the specific requirements of the respective extended model. Fifthly, some existing algorithms are presented in a high level that leaves some aspects of the computations unspecified, and therefore, implementations are rendered open to different interpretations. The work presented in this thesis tries to address all these concerns. Concisely, the presented work is centered around a widely studied view of what computationally defines an argument system. According to this view, an argument system is a pair: a set of abstract arguments and a binary relation that captures the conflicting arguments. Then, to resolve an instance of argument systems the acceptable arguments must be decided according to a set of criteria that collectively define the argumentation semantics. For different motivations there are various argumentation semantics. Equally, several proposals in the literature present extended models that stretch the basic two components of an argument system usually by incorporating more elements and/or broadening the nature of the existing components. This work designs algorithms that solve decision problems in the basic form of argument systems as well as in some other extended models. Likewise, new algorithms are developed that deal with different argumentation semantics. We evaluate our algorithms against existing algorithms experimentally where sufficient indications highlight that the new algorithms are superior with respect to their running time

Strong admissibility revisited: Theory and applications

In the current paper, we re-examine the concept of strong admissibility, as was originally introduced by Baroni and Giacomin. We examine the formal properties of strong admissibility, both in its extension-based and in its labelling-based form, and analyse the computational complexity of the relevant decision problems. Moreover, we show that strong admissibility plays a vital role in discussion-based proof procedures for grounded semantics. In particular it allows one to compare the performance of alternative dialectical proof procedures for grounded semantics, and obtain some remarkable differences between the Standard Grounded Game and the Grounded Discussion Game

A Purely Defeasible Argumentation Framework

Argumentation theory is concerned with the way that intelligent agents discuss whether some statement holds. It is a claim-based theory that is widely used in many areas, such as law, linguistics and computer science. In the past few years, formal argumentation frameworks have been heavily studied and applications have been proposed in fields such as natural language processing, the semantic web and multi-agent systems. Studying argumentation provides results which help in developing tools and applications in these areas. Argumentation is interesting as a logic-based approach to deal with inconsistent information. Arguments are constructed using a process like logical inference, with inconsistencies giving rise to conflicts between arguments. These conflicts can then be handled by well-founded means, giving a consistent set of well-justified arguments and conclusions. Dung\u27s seminal work tells us how to handle the conflicts between arguments. However, it says nothing about the structure of arguments, or how to construct arguments and attack relationships from a knowledge base. ASPIC+ is one of the most widely used systems for structured arguments. However, there are some limitations on ASPIC+ if it is to satisfy widely accepted standards of rationality. Since most of these limitations are due to the use of strict rules, it is worth considering using a purely defeasible subset of ASPIC+. The main contribution of this dissertation is the purely defeasible argumentation framework ASPIC+D. There are three research questions related to this topic which are investigated here: (1) Do we lose anything in removing the strict elements? (2) Do purely defeasible version of theories generate the same results as the original theories? (3) What do we gain by removing the strict elements? I show that using ASPIC+D, it is possible, in a well-defined sense, to capture the same information as using ASPIC+ with strict rules. In particular, I prove that under some reasonable assumptions, it is possible to take a well-defined theory in ASPIC+, that is one with a consistent set of conclusions, and translate it into ASPIC+D such that, under the grounded semantics, we obtain the same set of justified conclusions. I also show that, under some additional assumptions, the same is true under any complete-based semantics. Furthermore, I formally characterize the situations in which translating an ASPIC+ theory that is ill-defined into ASPIC+D will lead to the same sets of justified conclusions. In doing this I deal both with ASPIC+ theories that are not closed under transposition and theories that are axiom inconsistent. At last, I analyze the two systems in the context of the non-monotonic axioms. I show that ASPIC+ and ASPIC+D satisfy exactly same axioms under what I call the “argument construction” interpretation and the “justified conclusions” interpretation under the grounded semantics. Furthermore, because of the lack of strict elements, ASPIC+ satisfies more of the non-monotonic axioms than ASPIC+ in the ``justified conclusions\u27\u27 interpretation under the preferred semantic. This means that ASPIC+ and ASPIC+D may not have the same justified conclusions under the preferred semantics

Argumentation semantics as formal discussion

In the current review paper, we provide an overview of how mainstream argumentation semantics can be interpreted in terms of structured discussion. The idea is that an argument is justified according to a particular argumentation semantics iff it is possible to win a discussion of a particular type. Hence, different argumentation semantics correspond to different types of discussion. Our aim is to provide an overview of what these discussions look like, and their formal correspondence to argumentation semantics

Argumentation semantics as formal discussion

In the current chapter, we interpret a number of mainstream argumentation semantics by means of structured discussion. The idea is that an argument is justified according to a particular argumentation semantics iff it is possible to win a discussion of a particular type. Hence, different argumentation semantics correspond to different types of discussion. Our aim is to provide an overview of what these discussions look like, and their formal correspondence to argumentation semantics

Belief revision and computational argumentation: a critical comparison

This paper aims at comparing and relating belief revision and argumentation as approaches to model reasoning processes. Referring to some prominent literature references in both fields, we will discuss their (implicit or explicit) assumptions on the modeled processes and hence commonalities and differences in the forms of reason ing they are suitable to deal with. The intended contribution is on one hand assessing the (not fully explored yet) relationships between two lively research fields in the broad area of defeasible reasoning and on the other hand pointing out open issues and potential directions for future research.info:eu-repo/semantics/publishedVersio

Dynamics in Abstract Argumentation Frameworks with Recursive Attack and Support Relations

Argumentation is an important topic in the field of AI. There is a substantial amount of work about different aspects of Dung's abstract Argumentation Framework (AF). Two relevant aspects considered separately so far are extending the framework to account for recursive attacks and supports, and considering dynamics, i.e., AFs evolving over time. In this paper, we jointly deal with these two aspects.We focus on Attack-Support Argumentation Frameworks (ASAFs) which allow for attack and support relations not only between arguments but also targeting attacks and supports at any level, and propose an approach for the incremental computation of extensions (sets of accepted arguments, attacks and supports) of updated ASAFs. Our approach assumes that an initial ASAF extension is given and uses it for first checking whether updates are irrelevant; for relevant updates, an extension of an updated ASAF is computed by translating the problem to the AF domain and leveraging on AF solvers. We experimentally show our incremental approach outperforms the direct computation of extensions for updated ASAFs.Fil: Alfano, Gianvincenzo. Universita Della Calabria.; ItaliaFil: Cohen, Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaFil: Gottifredi, Sebastian. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaFil: Greco, Sergio. Universita Della Calabria.; ItaliaFil: Parisi, Francesco. Universita Della Calabria.; ItaliaFil: Simari, Guillermo R.. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina24th European Conference on Artificial IntelligenceSantiago de CompostelaEspañaEuropean Association for Artificial IntelligenceUniversidad de Santiago de Compostel