This paper introduces a multi-agent dynamic epistemic logic for abstract argumentation. Its main motivation is to build a general framework for modelling the dynamics of a debate, which entails reasoning about goals, beliefs, as well as policies of communication and information update by the participants. After locating our proposal and introducing the relevant tools from abstract argumentation, we proceed to build a three-tiered logical approach. At the first level, we use the language of propositional logic to encode states of a (...) multi-agent debate. This language allows to specify which arguments any agent is aware of, as well as their subjective justification status. We then extend our language and semantics to that of epistemic logic, in order to model individuals’ beliefs about the state of the debate, which includes uncertainty about the information available to others. As a third step, we introduce a framework of dynamic epistemic logic and its semantics, which is essentially based on so-called event models with factual change. We provide completeness results for a number of systems and show how existing formalisms for argumentation dynamics and unquantified uncertainty can be reduced to their semantics. The resulting framework allows reasoning about subtle epistemic and argumentative updates—such as the effects of different levels of trust in a source—and more in general about the epistemic dimensions of strategic communication. (shrink)

Although argumentation plays an essential role in our lives, there is no integrated area of research on the psychology of argumentation. Instead research on argumentation is conducted in a number of separate research communities that are spread across disciplines and have only limited interaction. With a view to bridging these different strands, we first distinguish between three meanings of the word ?argument?: argument as a reason, argument as a structured sequence of reasons and claims, and argument as a social exchange. (...) All three meanings are integral to a complete understanding of human reasoning and cognition. Cognitive psychological research on argumentation has focused mostly on the first and second of these meanings, so we present perspectives on argumentation from outside of cognitive psychology, which focus on the second and third. Specifically, we give anoverview of the methods, goals, and disciplinary backgrounds of research on the production, the analysis, and the evaluation of arguments. Finally, inintroducing the experimental studies included in this special issue, which were conducted by researchers from a range of theoretical backgrounds, weunderline the breadth of argumentation research as well as stress opportunities for mutual awareness and integration. (shrink)

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. (shrink)

Formal models of argumentation have been investigated in several areas, from multi-agent systems and artificial intelligence (AI) to decision making, philosophy and law. In artificial intelligence, logic-based models have been the standard for the representation of argumentative reasoning. More recently, the standard logic-based models have been shown equivalent to standard connectionist models. This has created a new line of research where (i) neural networks can be used as a parallel computational model for argumentation and (ii) neural networks can be used (...) to combine argumentation, quantitative reasoning and statistical learning. At the same time, non-standard logic models of argumentation started to emerge. In this paper, we propose a connectionist cognitive model of argumentation that accounts for both standard and non-standard forms of argumentation. The model is shown to be an adequate framework for dealing with standard and non-standard argumentation, including joint-attacks, argument support, ordered attacks, disjunctive attacks, meta-level attacks, self-defeating attacks, argument accrual and uncertainty. We show that the neural cognitive approach offers an adequate way of modelling all of these different aspects of argumentation. We have applied the framework to the modelling of a public prosecution charging decision as part of a real legal decision making case study containing many of the above aspects of argumentation. The results show that the model can be a useful tool in the analysis of legal decision making, including the analysis of what-if questions and the analysis of alternative conclusions. The approach opens up two new perspectives in the short-term: the use of neural networks for computing prevailing arguments efficiently through the propagation in parallel of neuronal activations, and the use of the same networks to evolve the structure of the argumentation network through learning (e.g. to learn the strength of arguments from data). (shrink)

Research on reasoning about consequential arguments has been an active but piecemeal enterprise. Previous research considered in depth some subclasses ofconsequential arguments, but further understanding of consequential arguments requires that we address their greater variety, avoiding the risk of over-generalisation from specific examples. Ideally we ought to be able to systematically generate the set of consequential arguments, and then engage in random sampling of stimuli within that set. The current article aims at making steps in that direction, using the theory (...) of utility conditionals as a way to generate a large set of consequential arguments, and offering one study illustrating how the theory can be used for the random sampling of stimuli. Itis expected that further use of this method will bring more diversity to experimental research on consequential arguments, and more robustness to models of argumentation from consequences. (shrink)

(2013). Formal Models of Reasoning in Cognitive Psychology. Argument & Computation: Vol. 4, Formal Models of Reasoning in Cognitive Psychology, pp. 1-3. doi: 10.1080/19462166.2013.767559.

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. (shrink)

From a decision theoretic perspective, arguments stem from decisions made by arguers. Despite some promising results, this approach remains underdeveloped in argumentation theories, mostly because it is assumed to be merely descriptive. This assumption is mistaken: considering arguments as the product of decisions brings into play various normative models of rational choice. The challenge is rather to reconcile strategic rationality with other normative constraints relevant for argumentation, such as inferential validity and dialectical appropriateness.