On the input/output behavior of argumentation frameworks

This paper tackles the fundamental questions arising when looking at argumentation frameworks as interacting components, characterized by an Input/Output behavior, rather than as isolated monolithical entities. This modeling stance arises naturally in some application contexts, like multi-agent systems, but, more importantly, has a crucial impact on several general application-independent issues, like argumentation dynamics, argument summarization and explanation, incremental computation, and inter-formalism translation. Pursuing this research direction, the paper introduces a general modeling approach and provides a comprehensive set of theoretical results putting the intuitive notion of Input/Output behavior of argumentation frameworks on a solid formal ground. This is achieved by combining three main ingredients. First, several novel notions are introduced at the representation level, notably those of argumentation framework with input, of argumentation multipole, and of replacement of multipoles within a traditional argumentation framework. Second, several relevant features of argumentation semantics are identified and formally characterized. In particular, the canonical local function provides an input-aware semantics characterization and a suite of decomposability properties are introduced, concerning the correspondences between semantics outcomes at global and local level. The third ingredient glues the former ones, as it consists of the investigation of some semantics-dependent properties of the newly introduced entities, namely S-equivalence of multipoles, S-legitimacy and S-safeness of replacements, and transparency of a semantics with respect to replacements. Altogether they provide the basis and draw the limits of sound interchangeability of multipoles within traditional frameworks. The paper develops an extensive analysis of all the concepts listed above, covering seven well-known literature semantics and taking into account various, more or less constrained, ways of partitioning an argumentation framework. Diverse examples, taken from the literature, are used to illustrate the application of the results obtained and, finally, an extensive discussion of the related literature is provided

Algorithms for Dynamic Argumentation Frameworks: An Incremental SAT-Based Approach

Peer reviewe

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

The Complexity of Repairing, Adjusting, and Aggregating of Extensions in Abstract Argumentation

We study the computational complexity of problems that arise in abstract argumentation in the context of dynamic argumentation, minimal change, and aggregation. In particular, we consider the following problems where always an argumentation framework F and a small positive integer k are given. - The Repair problem asks whether a given set of arguments can be modified into an extension by at most k elementary changes (i.e., the extension is of distance k from the given set). - The Adjust problem asks whether a given extension can be modified by at most k elementary changes into an extension that contains a specified argument. - The Center problem asks whether, given two extensions of distance k, whether there is a "center" extension that is a distance at most (k-1) from both given extensions. We study these problems in the framework of parameterized complexity, and take the distance k as the parameter. Our results covers several different semantics, including admissible, complete, preferred, semi-stable and stable semantics

Harnessing Incremental Answer Set Solving for Reasoning in Assumption-Based Argumentation

Peer reviewe

SAT for argumentation

Peer reviewe

μ-toksia: An Efficient Abstract Argumentation Reasoner

Peer reviewe

Explain what you see:argumentation-based learning and robotic vision

In this thesis, we have introduced new techniques for the problems of open-ended learning, online incremental learning, and explainable learning. These methods have applications in the classification of tabular data, 3D object category recognition, and 3D object parts segmentation. We have utilized argumentation theory and probability theory to develop these methods. The first proposed open-ended online incremental learning approach is Argumentation-Based online incremental Learning (ABL). ABL works with tabular data and can learn with a small number of learning instances using an abstract argumentation framework and bipolar argumentation framework. It has a higher learning speed than state-of-the-art online incremental techniques. However, it has high computational complexity. We have addressed this problem by introducing Accelerated Argumentation-Based Learning (AABL). AABL uses only an abstract argumentation framework and uses two strategies to accelerate the learning process and reduce the complexity. The second proposed open-ended online incremental learning approach is the Local Hierarchical Dirichlet Process (Local-HDP). Local-HDP aims at addressing two problems of open-ended category recognition of 3D objects and segmenting 3D object parts. We have utilized Local-HDP for the task of object part segmentation in combination with AABL to achieve an interpretable model to explain why a certain 3D object belongs to a certain category. The explanations of this model tell a user that a certain object has specific object parts that look like a set of the typical parts of certain categories. Moreover, integrating AABL and Local-HDP leads to a model that can handle a high degree of occlusion