698 research outputs found
Working on the Argument Pipeline: Through Flow Issues between Natural Language Argument, Instantiated Arguments, and Argumentation Frameworks
In many domains of public discourse such as arguments about public policy, there is an abundance of knowledge to store, query, and reason with. To use this knowledge, we must address two key general problems: first, the problem of the knowledge acquisition bottleneck between forms in which the knowledge is usually expressed, e.g., natural language, and forms which can be automatically processed; second, reasoning with the uncertainties and inconsistencies of the knowledge. Given such complexities, it is labour and knowledge intensive to conduct policy consultations, where participants contribute statements to the policy discourse. Yet, from such a consultation, we want to derive policy positions, where each position is a set of consistent statements, but where positions may be mutually inconsistent. To address these problems and support policy-making consultations, we consider recent automated techniques in natural language processing, instantiating arguments, and reasoning with the arguments in argumentation frameworks. We discuss application and “bridge” issues between these techniques, outlining a pipeline of technologies whereby: expressions in a controlled natural language are parsed and translated into a logic (a literals and rules knowledge base), from which we generate instantiated arguments and their relationships using a logic-based formalism (an argument knowledge base), which is then input to an implemented argumentation framework that calculates extensions of arguments (an argument extensions knowledge base), and finally, we extract consistent sets of expressions (policy positions). The paper reports progress towards reasoning with web-based, distributed, collaborative, incomplete, and inconsistent knowledge bases expressed in natural language
Warsaw Argumentation Week (Waw 2018) Organised by the Polish School of Argumentation and Our Colleagues from Germany and the UK, 6th-16th September 2018
In September 2018, the ArgDiaP association, along with colleagues from Germany and the UK, organised one of the longest and most interdisciplinary series of events ever dedicated to argumentation - Warsaw Argumentation Week, WAW 2018. The eleven-day ‘week’ featured a five day graduate school on computational and linguistic perspectives on argumentation (3rd SSA school); five workshops: on systems and algorithms for formal argumentation (2nd SAFA), argumentation in relation to society (1st ArgSoc), philosophical approaches to argumentation (1st ArgPhil), legal argumentation (2ndMET-ARG) and argumentation in rhetoric (1st MET-RhET); and two conferences: on computational models of argumentation (7th COMMA conference) and on argumentation and corpus linguistics (16th ArgDiaP conference). WAW hosted twelve tutorials and eight invited talks as well as welcoming over 130 participants. All the conferences and workshops publish pre- or post-proceedings in the top journals and book series in the field
Improving argumentation-based recommender systems through context-adaptable selection criteria
Recommender Systems based on argumentation represent an important proposal where the recommendation is supported by qualitative information. In these systems, the role of the comparison criterion used to decide between competing arguments is paramount and the possibility of using the most appropriate for a given domain becomes a central issue; therefore, an argumentative recommender system that offers an interchangeable argument comparison criterion provides a significant ability that can be exploited by the user. However, in most of current recommender systems, the argument comparison criterion is either fixed, or codified within the arguments. In this work we propose a formalization of context-adaptable selection criteria that enhances the argumentative reasoning mechanism. Thus, we do not propose of a new type of recommender system; instead we present a mechanism that expand the capabilities of existing argumentation-based recommender systems. More precisely, our proposal is to provide a way of specifying how to select and use the most appropriate argument comparison criterion effecting the selection on the user´s preferences, giving the possibility of programming, by the use of conditional expressions, which argument preference criterion has to be used in each particular situation.Fil: Teze, Juan Carlos Lionel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación; Argentina. Universidad Nacional de Entre Ríos; ArgentinaFil: Gottifredi, Sebastián. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación; ArgentinaFil: García, Alejandro Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación; ArgentinaFil: Simari, Guillermo Ricardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación; Argentin
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
Interactive Explanations by Conflict Resolution via Argumentative Exchanges
As the field of explainable AI (XAI) is maturing, calls for interactive
explanations for (the outputs of) AI models are growing, but the
state-of-the-art predominantly focuses on static explanations. In this paper,
we focus instead on interactive explanations framed as conflict resolution
between agents (i.e. AI models and/or humans) by leveraging on computational
argumentation. Specifically, we define Argumentative eXchanges (AXs) for
dynamically sharing, in multi-agent systems, information harboured in
individual agents' quantitative bipolar argumentation frameworks towards
resolving conflicts amongst the agents. We then deploy AXs in the XAI setting
in which a machine and a human interact about the machine's predictions. We
identify and assess several theoretical properties characterising AXs that are
suitable for XAI. Finally, we instantiate AXs for XAI by defining various agent
behaviours, e.g. capturing counterfactual patterns of reasoning in machines and
highlighting the effects of cognitive biases in humans. We show experimentally
(in a simulated environment) the comparative advantages of these behaviours in
terms of conflict resolution, and show that the strongest argument may not
always be the most effective.Comment: 14 pages, 2 figure
Inferring Attack Relations for Gradual Semantics
Peer reviewedPublisher PD
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