Strategic Sequences of Arguments for Persuasion Using Decision Trees

Persuasion is an activity that involves one party (the persuader) trying to induce another party (the persuadee) to believe or do something. For this, it can be advantageous forthe persuader to have a model of the persuadee. Recently, some proposals in the field of computational models of argument have been made for probabilistic models of what the persuadee knows about, or believes. However, these developments have not systematically harnessed established notions in decision theory for maximizing the outcome of a dialogue. To address this, we present a general framework for representing persuasion dialogues as a decision tree, and for using decision rules for selecting moves. Furthermore, we provide some empirical results showing how some well-known decision rules perform, and make observations about their general behaviour in the context of dialogues where there is uncertainty about the accuracy of the user model

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

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

Learning and Updating User Models for Subpopulations in Persuasive Argumentation Using Beta Distribution

Persuasion is an activity that involves one party (the persuader) trying to induce another party (the persuadee) to believe or do something. It is an important and multifaceted human facility both in professional life (e.g., a doctor persuading a patient to give up smoking) and everyday life (e.g., some friends persuading another to join them in seeing a film). Recently, some proposals in the field of computational models of argument have been made for probabilistic models of what the persuadee knows about, or believes. However, they cannot efficiently model uncertainty on the belief of individuals and cannot represent populations. We propose to use mixtures of beta distributions and apply them on real data gathered by linguists. We show that we can represent the belief and its uncertainty using beta mixtures and that we can predict the evolution of this belief after an argument is given. We also present examples of how to use the mixtures in practice to replace general belief update functions

Biparty Decision Theory for Dialogical Argumentation

Proposals for strategies for dialogical argumentation often focus on situations where one of the agents wins the dialogue and the other agent loses. Yet in real-world argumentation, it is common for agents to not involve such zero-sum situations. Rather, the agents may enter into a dialogue with divergent but not necessarily opposing views on what is important in the outcomes from the argumentation. In order to model this kind of situation, we investigate a decision-theoretic approach that allows different participants to have different utility evaluations of a dialogue, and for the proponent to model the opponent's utility evaluation in order to optimize the choice of move in the dialogue

Towards Computational Persuasion via Natural Language Argumentation Dialogues

Computational persuasion aims to capture the human ability to persuade through argumentation for applications such as behaviour change in healthcare (e.g. persuading people to take more exercise or eat more healthily). In this paper, we review research in computational persuasion that incorporates domain modelling (capturing arguments and counterarguments that can appear in a persuasion dialogues), user modelling (capturing the beliefs and concerns of the persuadee), and dialogue strategies (choosing the best moves for the persuader to maximize the chances that the persuadee is persuaded). We discuss evaluation of prototype systems that get the user’s counterarguments by allowing them to select them from a menu. Then we consider how this work might be enhanced by incorporating a natural language interface in the form of an argumentative chatbot

Comfort or safety? Gathering and using the concerns of a participant for better persuasion

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. A key dimension for determining whether an argument is good is the impact that it has on the concerns of the intended audience of the argument (e.g., the other participant(s) in the dialogue). In this paper, we investigate how we can acquire and represent concerns of a participant, and her preferences over them, and we show how this can be used for selecting good moves in a persuasion dialogue. We provide results from empirical studies showing that: (1) we can gather preferences over types of concern; (2) there is a common understanding of what is meant by concerns; (3) participants tend to make moves according to their preferences; and (4) the persuader can use these preferences to improve the persuasiveness of a dialogue

PHYTOBS v2.3 : Outil de comptage du phytoplancton en laboratoire et de calcul de l'IPLAC. Version 2.3. Application JAVA

AutresL'outil de comptage PHYTOBS permet le comptage du phytoplancton au microscope. Il est conforme à la norme Utermöhl (NF-EN 15204, 2006) relative au comptage du phytoplancton en microscopie inversée. De même il respecte le protocole standardisé d'échantillonnage du phytoplancton en plan d'eau dans le cadre de la DCE (Laplace-Treyture & al., 2009) et peut de ce fait servir aussi dans cet optique. Différentes options et fonctions permettent de synthétiser et d'exporter les résultats en vue de la bancarisation, de calculer des biovolumes. Un des boutons permet d'accéder à la liste taxinomique de référence des algues potentiellement rencontrées en France avec leur phylogénie et pour les noms considérés comme à jour la codification SANDRE. un nouveau module permet le calcul de l'indice Phytoplancton Lacustre (IPLAC)