226 research outputs found
Recommended from our members
Value-based argumentation frameworks as neural-symbolic learning systems
While neural networks have been successfully used in a number of machine learning applications, logical languages have been the standard for the representation of argumentative reasoning. In this paper, we establish a relationship between neural networks and argumentation networks, combining reasoning and learning in the same argumentation framework. We do so by presenting a new neural argumentation algorithm, responsible for translating argumentation networks into standard neural networks. We then show a correspondence between the two networks. The algorithm works not only for acyclic argumentation networks, but also for circular networks, and it enables the accrual of arguments through learning as well as the parallel computation of arguments
Confronting value-based argumentation frameworks with people’s assessment of argument strength
We reported a series of experiments carried out to confront the underlying intuitions of value-based argumentation frameworks (VAFs) with the intuitions of ordinary people. Our goal was twofold. On the one hand, we intended to test VAF as a descriptive theory of human argument evaluations. On the other, we aimed to gain new insights from empirical data that could serve to improve VAF as a normative model. The experiments showed that people's acceptance of arguments deviates from VAF's semantics and is rather correlated with the importance given to the promoted values, independently of the perceptions of argument interactions through attacks and defeats. Furthermore, arguments were often perceived as promoting more than one value with different relative strengths. Individuals' analyses of scenarios were also affected by external factors such as biases and arguments not explicit in the framework. Finally, we confirmed that objective acceptance, that is, the acceptance of arguments under any order of the values, was not a frequent behavior. Instead, participants tended to accept only the arguments that promoted the values they subscribe.Fil: Bodanza, Gustavo Adrian. Consejo Nacional de Investigaciones CientÃficas y Técnicas. Centro CientÃfico Tecnológico Conicet - BahÃa Blanca. Instituto de Investigaciones Económicas y Sociales del Sur. Universidad Nacional del Sur. Departamento de EconomÃa. Instituto de Investigaciones Económicas y Sociales del Sur; ArgentinaFil: Freidin, Esteban. Consejo Nacional de Investigaciones CientÃficas y Técnicas. Centro CientÃfico Tecnológico Conicet - BahÃa Blanca. Instituto de Investigaciones Económicas y Sociales del Sur. Universidad Nacional del Sur. Departamento de EconomÃa. Instituto de Investigaciones Económicas y Sociales del Sur; Argentin
Recommended from our members
Argumentation Neural Networks: Value-based Argumentation Frameworks as Neural-Symbolic Learning Systems
Formalizing value-guided argumentation for ethical systems design
The persuasiveness of an argument depends on the values promoted and demoted by the position defended. This idea, inspired by Perelman’s work on argumentation, has become a prominent theme in artificial intelligence research on argumentation since the work by Hafner and Berman on teleological reasoning in the law, and was further developed by Bench-Capon in his value-based argumentation frameworks. One theme in the study of value-guided argumentation is the comparison of values. Formal models involving value comparison typically use either qualitative or quantitative primitives. In this paper, techniques connecting qualitative and quantitative primitives recently developed for evidential argumentation are applied to value-guided argumentation. By developing the theoretical understanding of intelligent systems guided by embedded values, the paper is a step towards ethical systems design, much needed in these days of ever more pervasive AI techniques. Keywords Argumentation Ethical systems Teleological reasoning Value
Impact of Argument Type and Concerns in Argumentation with a Chatbot
Conversational agents, also known as chatbots, are versatile tools that have
the potential of being used in dialogical argumentation. They could possibly be
deployed in tasks such as persuasion for behaviour change (e.g. persuading
people to eat more fruit, to take regular exercise, etc.) However, to achieve
this, there is a need to develop methods for acquiring appropriate arguments
and counterargument that reflect both sides of the discussion. For instance, to
persuade someone to do regular exercise, the chatbot needs to know
counterarguments that the user might have for not doing exercise. To address
this need, we present methods for acquiring arguments and counterarguments, and
importantly, meta-level information that can be useful for deciding when
arguments can be used during an argumentation dialogue. We evaluate these
methods in studies with participants and show how harnessing these methods in a
chatbot can make it more persuasive
Complexity in value-based argument systems
Abstract. We consider a number of decision problems formulated in value-based argumentation frameworks (VAFs), a development of Dung's argument systems in which arguments have associated abstract values which are considered relative to the orderings induced by the opinions of specific audiences. In the context of a single fixed audience, it is known that those decision questions which are typically computationally hard in the standard setting admit efficient solution methods in the value-based setting. In this paper we show that, in spite of this positive property, there still remain a number of natural questions that arise solely in value-based schemes for which there are unlikely to be efficient decision processes
- …