A neural cognitive model of argumentation with application to legal inference and decision making

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)

Neurons and symbols: a manifesto

We discuss the purpose of neural-symbolic integration including its principles, mechanisms and applications. We outline a cognitive computational model for neural-symbolic integration, position the model in the broader context of multi-agent systems, machine learning and automated reasoning, and list some of the challenges for the area of neural-symbolic computation to achieve the promise of effective integration of robust learning and expressive reasoning under uncertainty

a Review of Instructional Approaches

UIDB/00183/2020 UIDP/00183/2020 DL 57/2016/CP1453/CT0066 PTDC/FER-FIL/28278/2017Over the past 20 years, a broad and diverse research literature has emerged to address how students learn to argue through dialogue in educational contexts. However, the variety of approaches used to study this phenomenon makes it challenging to find coherence in what may otherwise seem to be disparate fields of study. In this integrative review, we propose looking at how learning to argue (LTA) has been operationalized thus far in educational research, focusing on how different scholars have framed and fostered argumentative dialogue, assessed its gains, and applied it in different learning contexts. In total, 143 studies from the broad literature on educational dialogue and argumentation were analysed, including all educational levels (from primary to university). The following patterns for studying how dialogue fosters LTA emerged: whole-class ‘low structure’ framing with a goal of dialogue, small-group ‘high structure’ framing with varied argumentative goals, and studies with one-to-one dialectic framing with a goal of persuasive deliberation. The affordances and limitations of these different instructional approaches to LTA research and practice are discussed. We conclude with a discussion of complementarity of the approaches that emerged from our analysis in terms of the pedagogical methods and conditions that promote productive and/or constructive classroom interactions.publishersversionepub_ahead_of_prin

On the use of contexts for representing knowledge in defeasible argumentation

The notion of context and its importance in knowledge representation and nonmonotonic reasoning was first discussed in Artificial Intelligence by John McCarthy. Ever since, contexts have found many applications in developing knowledge-based reasoning systems. Defeasible argumentation has gained wide acceptance within the Al community in the last years. Different argument-based frameworks have been proposed. In this respect, MTDR (Simari & Loui, 1992) has come to be one of the most successful. However, even though the formalism is theoretically sound, there exist sorne dialectical considerations involving argument construction and the inference mechanism, which impose a rather procedural approach, tightly interlocked with the system's logic. This paper discusses different uses of contexts for modelling the process of defeasible argumentation. We present an alternative view of MTDR using contexts. Our approach will allow us to discuss novel issues in MTDR, such as defining a set of moves and introducting an arbiter for regulating inference. As a result, protocols for argument generation as well as some technical considerations for speeding up inference will be kept apart from the logical machinery underlying MTDR.Eje: 2do. Workshop sobre aspectos teóricos de la inteligencia artificialRed de Universidades con Carreras en Informática (RedUNCI

Burden of Persuasion: A Meta-argumentation Approach

This work defines a burden of persuasion meta-argumentation model interpreting burden as a set of meta-arguments. Bimodal graphs are exploited to define a meta level (dealing with the burden) and an object level (dealing with standard arguments). A novel technological reification of the model supporting the burden inversion mechanism is presented and discussed

Pragmatism as a Communication-Theoretical Tradition: An Assessment of Craig’s Proposal

Of recent attempts to appropriate pragmatism for communication studies, Robert Craig‘s inclusion of a pragmatist "tradition" in his influential "metamodel" of communication theoriesconstitutes one of the most prominent proposals to date. In this model, pragmatism is principally understood by contrast to other alternatives, such as phenomenology, semiotics, and rhetoric. As a communication-theoretical tradition in Craig‘s sense, the pragmatist approach is expected to provide distinctive articulations of the nature of communication and communication problems, expressed in a particular vocabulary. Useful as such a partitioning may be for analytical and dialogical purposes, the delimitation of pragmatism that emerges from Craig‘s efforts is in many respects problematic. After a summary of the background assumptions and disciplinary aims of Craig‘s pro-ject, this article identifies three serious weaknesses in his account: its neglect of relevant intra-tradition distinctions and debates, its straightforward association of pragmatism with a strongly constitutive approach to communication, and its tendency to disconnect pragmatism from other communication-theoretical positions in ways that are not conducive to his objectives. This discussion highlights the contrast between Craig‘s constructionist instrumentalism and the habit-realism of the classical pragmatisms of Peirce and Dewey.Peer reviewe