997 research outputs found
Analysis of Dialogical Argumentation via Finite State Machines
Dialogical argumentation is an important cognitive activity by which agents
exchange arguments and counterarguments as part of some process such as
discussion, debate, persuasion and negotiation. Whilst numerous formal systems
have been proposed, there is a lack of frameworks for implementing and
evaluating these proposals. First-order executable logic has been proposed as a
general framework for specifying and analysing dialogical argumentation. In
this paper, we investigate how we can implement systems for dialogical
argumentation using propositional executable logic. Our approach is to present
and evaluate an algorithm that generates a finite state machine that reflects a
propositional executable logic specification for a dialogical argumentation
together with an initial state. We also consider how the finite state machines
can be analysed, with the minimax strategy being used as an illustration of the
kinds of empirical analysis that can be undertaken.Comment: 10 page
Towards a framework for computational persuasion with applications in behaviour change
Persuasion is an activity that involves one party trying to induce another party to believe something or to do something. It is an important and multifaceted human facility. Obviously, sales and marketing is heavily dependent on persuasion. But many other activities involve persuasion such as a doctor persuading a patient to drink less alcohol, a road safety expert persuading drivers to not text while driving, or an online safety expert persuading users of social media sites to not reveal too much personal information online. As computing becomes involved in every sphere of life, so too is persuasion a target for applying computer-based solutions. An automated persuasion system (APS) is a system that can engage in a dialogue with a user (the persuadee) in order to persuade the persuadee to do (or not do) some action or to believe (or not believe) something. To do this, an APS aims to use convincing arguments in order to persuade the persuadee. Computational persuasion is the study of formal models of dialogues involving arguments and counterarguments, of user models, and strategies, for APSs. A promising application area for computational persuasion is in behaviour change. Within healthcare organizations, government agencies, and non-governmental agencies, there is much interest in changing behaviour of particular groups of people away from actions that are harmful to themselves and/or to others around them
Assessing relevance
This paper advances an approach to relevance grounded on patterns of material inference called argumentation schemes, which can account for the reconstruction and the evaluation of relevance relations. In order to account for relevance in different types of dialogical contexts, pursuing also non-cognitive goals, and measuring the scalar strength of relevance, communicative acts are conceived as dialogue moves, whose coherence with the previous ones or the context is represented as the conclusion of steps of material inferences. Such inferences are described using argumentation schemes and are evaluated by considering 1) their defeasibility, and 2) the acceptability of the implicit premises on which they are based. The assessment of both the relevance of an utterance and the strength thereof depends on the evaluation of three interrelated factors: 1) number of inferential steps required; 2) the types of argumentation schemes involved; and 3) the implicit premises required
Argumentación y educación: apuntes para un debate
This is an Author's Original Manuscript of an article published by Taylor & Francis in Infancia y Aprendizaje on 02/01/2016 available online at http://www.tandfonline.com/10.1080/02103702.2015.1111607The objective of the present article is twofold. On the one hand, it aims to analyse the relationship between argumentation and education with a special emphasis on the difficulties that occur when defining and assessing argumentative skills. These difficulties are related to the thinking patterns underlying the argumentation models and, at the same time, are reflected in the educational models used to train and to assess students’ argumentative skills. On the other hand, this article presents and discusses common and distinctive aspects of the papers selected for this monographEl artÃculo que se presenta tiene un doble objetivo. Por un lado, pretende analizar cuáles son las relaciones entre argumentación y educación, poniendo énfasis en las dificultades para definir en qué consisten las competencias argumentativas y en los debates que esta indefinición ocasiona. Estas dificultades se relacionan con los modelos normativos de pensamiento que subyacen más o menos explÃcitamente a los modelos de argumentación y, al mismo tiempo, se reflejan en los modelos educativos que quieren formar a los
estudiantes en las competencias argumentativas o que analizan las habilidades
de estos estudiantes. Por otro lado, en este artÃculo se presentan y comentan los aspectos comunes y diferenciadores de los artÃculos seleccionados en la convocatoria ‘Argumentación y Educación’ y que constituyen este número de la revistaWe would like to express our gratitude to the general editors who helped us in all of the decision making processes and to Anna Sala, for her technical help. This research was funded by the Ministerio Español de EconomÃa y Competitividad [EDU2013-47593-C2-1-P], y [EDU2013-47593-C2-2-P
Computationally viable handling of beliefs in arguments for persuasion
Computational models of argument are being developed to capture aspects of how persuasion is undertaken. Recent proposals suggest that in a persuasion dialogue between some agents, it is valuable for each agent to model how arguments are believed by the other agents. Beliefs in arguments can be captured by a joint belief distribution over the arguments and updated as the dialogue progresses. This information can be used by the agent to make more intelligent choices of move in the dialogue. Whilst these proposals indicate the value of modelling the beliefs of other agents, there is a question of the computational viability of using a belief distribution over all the arguments. We address this problem in this paper by presenting how probabilistic independence can be leveraged to split this joint distribution into an equivalent set of distributions of smaller size. Experiments show that updating the belief on the split distribution is more efficient than performing updates on the joint distribution
Argumentation Mining in User-Generated Web Discourse
The goal of argumentation mining, an evolving research field in computational
linguistics, is to design methods capable of analyzing people's argumentation.
In this article, we go beyond the state of the art in several ways. (i) We deal
with actual Web data and take up the challenges given by the variety of
registers, multiple domains, and unrestricted noisy user-generated Web
discourse. (ii) We bridge the gap between normative argumentation theories and
argumentation phenomena encountered in actual data by adapting an argumentation
model tested in an extensive annotation study. (iii) We create a new gold
standard corpus (90k tokens in 340 documents) and experiment with several
machine learning methods to identify argument components. We offer the data,
source codes, and annotation guidelines to the community under free licenses.
Our findings show that argumentation mining in user-generated Web discourse is
a feasible but challenging task.Comment: Cite as: Habernal, I. & Gurevych, I. (2017). Argumentation Mining in
User-Generated Web Discourse. Computational Linguistics 43(1), pp. 125-17
Strategic argumentation dialogues for persuasion: Framework and experiments based on modelling the beliefs and concerns of the persuadee
Persuasion is an important and yet complex aspect of human intelligence. When undertaken through dialogue, the deployment of good arguments, and therefore counterarguments, clearly has a significant effect on the ability to be successful in persuasion. Two key dimensions for determining whether an argument is 'good' in a particular dialogue are the degree to which the intended audience believes the argument and counterarguments, and the impact that the argument has on the concerns of the intended audience. In this paper, we present a framework for modelling persuadees in terms of their beliefs and concerns, and for harnessing these models in optimizing the choice of move in persuasion dialogues. Our approach is based on the Monte Carlo Tree Search which allows optimization in real-time. We provide empirical results of a study with human participants that compares an automated persuasion system based on this technology with a baseline system that does not take the beliefs and concerns into account in its strategy
Empirical Evaluation of Abstract Argumentation: Supporting the Need for Bipolar and Probabilistic Approaches
In dialogical argumentation it is often assumed that the involved parties
always correctly identify the intended statements posited by each other,
realize all of the associated relations, conform to the three acceptability
states (accepted, rejected, undecided), adjust their views when new and correct
information comes in, and that a framework handling only attack relations is
sufficient to represent their opinions. Although it is natural to make these
assumptions as a starting point for further research, removing them or even
acknowledging that such removal should happen is more challenging for some of
these concepts than for others. Probabilistic argumentation is one of the
approaches that can be harnessed for more accurate user modelling. The
epistemic approach allows us to represent how much a given argument is believed
by a given person, offering us the possibility to express more than just three
agreement states. It is equipped with a wide range of postulates, including
those that do not make any restrictions concerning how initial arguments should
be viewed, thus potentially being more adequate for handling beliefs of the
people that have not fully disclosed their opinions in comparison to Dung's
semantics. The constellation approach can be used to represent the views of
different people concerning the structure of the framework we are dealing with,
including cases in which not all relations are acknowledged or when they are
seen differently than intended. Finally, bipolar argumentation frameworks can
be used to express both positive and negative relations between arguments. In
this paper we describe the results of an experiment in which participants
judged dialogues in terms of agreement and structure. We compare our findings
with the aforementioned assumptions as well as with the constellation and
epistemic approaches to probabilistic argumentation and bipolar argumentation
Strategic Argumentation Dialogues for Persuasion: Framework and Experiments Based on Modelling the Beliefs and Concerns of the Persuadee
Persuasion is an important and yet complex aspect of human intelligence. When
undertaken through dialogue, the deployment of good arguments, and therefore
counterarguments, clearly has a significant effect on the ability to be
successful in persuasion. Two key dimensions for determining whether an
argument is good in a particular dialogue are the degree to which the intended
audience believes the argument and counterarguments, and the impact that the
argument has on the concerns of the intended audience. In this paper, we
present a framework for modelling persuadees in terms of their beliefs and
concerns, and for harnessing these models in optimizing the choice of move in
persuasion dialogues. Our approach is based on the Monte Carlo Tree Search
which allows optimization in real-time. We provide empirical results of a study
with human participants showing that our automated persuasion system based on
this technology is superior to a baseline system that does not take the beliefs
and concerns into account in its strategy.Comment: The Data Appendix containing the arguments, argument graphs,
assignment of concerns to arguments, preferences over concerns, and
assignment of beliefs to arguments, is available at the link
http://www0.cs.ucl.ac.uk/staff/a.hunter/papers/unistudydata.zip The code is
available at https://github.com/ComputationalPersuasion/MCC
An agent model for business relationships
Relationships are fundamental to all but the most impersonal forms of interaction in business. An agent aims to secure projected needs by attempting to build a set of (business) relationships with other agents. A relationship is built by exchanging private information, and is characterised by its intimacy — degree of closeness — and balance — degree of fairness. Each argumentative interaction between two agents then has two goals: to satisfy some immediate need, and to do so in a way that develops the relationship in a desired direction. An agent’s desire to develop each relationship in a particular way then places constraints on the argumentative utterances. This paper describes argumentative interaction constrained by a desire to develop such relationships.Peer Reviewe
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