Applying Recent Argumentation Methods to Some Ancient Examples of Plausible Reasoning

Plausible (eikotic) reasoning known from ancient Greek (late Academic) skeptical philosophy is shown to be a clear notion that can be analyzed by argu- mentation methods, and that is important for argumentation studies. It is shown how there is a continuous thread running from the Sophists to the skeptical philosopher Carneades, through remarks of Locke and Bentham on the subject, to recent research in artificial intelligence. Eleven characteristics of plausible reasoning are specified by analyzing key examples of it recognized as important in ancient Greek skeptical philosophy using an artificial intelligence model called the Carneades Argumentation System (CAS). By applying CAS to ancient examples it is shown how plausible reasoning is especially useful for gaining a better understanding of evidential reasoning in law, and argued that it can also be applied to everyday argumentation. Our analysis of the snake and rope example of Carneades is also used to point out some ways CAS needs to be extended if it is to more fully model the views of this ancient philosopher on argumentation

Challenges for a CBR framework for argumentation in open MAS

[EN] Nowadays, Multi-Agent Systems (MAS) are broadening their applications to open environments, where heterogeneous agents could enter into the system, form agents’ organizations and interact. The high dynamism of open MAS gives rise to potential conflicts between agents and thus, to a need for a mechanism to reach agreements. Argumentation is a natural way of harmonizing conflicts of opinion that has been applied to many disciplines, such as Case-Based Reasoning (CBR) and MAS. Some approaches that apply CBR to manage argumentation in MAS have been proposed in the literature. These improve agents’ argumentation skills by allowing them to reason and learn from experiences. In this paper, we have reviewed these approaches and identified the current contributions of the CBR methodology in this area. As a result of this work, we have proposed several open issues that must be taken into consideration to develop a CBR framework that provides the agents of an open MAS with arguing and learning capabilities.This work was partially supported by CONSOLIDER-INGENIO 2010 under grant CSD2007-00022 and by the Spanish government and FEDER funds under TIN2006-14630-C0301 project.Heras Barberá, SM.; Botti Navarro, VJ.; Julian Inglada, VJ. (2009). Challenges for a CBR framework for argumentation in open MAS. Knowledge Engineering Review. 24(4):327-352. https://doi.org/10.1017/S0269888909990178S327352244Willmott S. , Vreeswijk G. , Chesñevar C. , South M. , McGinnis J. , Modgil S. , Rahwan I. , Reed C. , Simari G. 2006. Towards an argument interchange format for multi-agent systems. In Proceedings of the AAMAS International Workshop on Argumentation in Multi-Agent Systems, ArgMAS-06, 17–34.Sycara, K. P. (1990). Persuasive argumentation in negotiation. 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Visualization tools, argumentation schemes and expert opinion evidence in law

Presented at the workshop on "Graphic and visual representations of evidence and inference in legal settings" at Cardozo School of Law, New York City, 28th -29th January 2007. New models of evidential reasoning have been closely tied in with the development of visualization tools in artificial intelligence, especially automated systems for argument diagramming. Surveying several models and visualization tools recently developed in artificial intelligence, this paper argues that any discussion of visualization methods or tools of this sort should focus on their suitability for visualizing argumentation schemes, including critical questions. The classic scheme, used in this paper to illustrate how schemes need to be a vital part of advancing argumentation technology in tools for evidence visualization in law, is that for argument from expert opinion. The visualization of argumentation schemes is illustrated using a new version of the scheme, which takes into consideration Supreme Court rulings on the admissibility of expert witness testimony

Argumentation for machine learning: a survey

Existing approaches using argumentation to aid or improve machine learning differ in the type of machine learning technique they consider, in their use of argumentation and in their choice of argumentation framework and semantics. This paper presents a survey of this relatively young field highlighting, in particular, its achievements to date, the applications it has been used for as well as the benefits brought about by the use of argumentation, with an eye towards its future

Recognizing cited facts and principles in legal judgements

In common law jurisdictions, legal professionals cite facts and legal principles from precedent cases to support their arguments before the court for their intended outcome in a current case. This practice stems from the doctrine of stare decisis, where cases that have similar facts should receive similar decisions with respect to the principles. It is essential for legal professionals to identify such facts and principles in precedent cases, though this is a highly time intensive task. In this paper, we present studies that demonstrate that human annotators can achieve reasonable agreement on which sentences in legal judgements contain cited facts and principles (respectively, κ=0.65 and κ=0.95 for inter- and intra-annotator agreement). We further demonstrate that it is feasible to automatically annotate sentences containing such legal facts and principles in a supervised machine learning framework based on linguistic features, reporting per category precision and recall figures of between 0.79 and 0.89 for classifying sentences in legal judgements as cited facts, principles or neither using a Bayesian classifier, with an overall κ of 0.72 with the human-annotated gold standard

Parsing Argumentation Structures in Persuasive Essays

In this article, we present a novel approach for parsing argumentation structures. We identify argument components using sequence labeling at the token level and apply a new joint model for detecting argumentation structures. The proposed model globally optimizes argument component types and argumentative relations using integer linear programming. We show that our model considerably improves the performance of base classifiers and significantly outperforms challenging heuristic baselines. Moreover, we introduce a novel corpus of persuasive essays annotated with argumentation structures. We show that our annotation scheme and annotation guidelines successfully guide human annotators to substantial agreement. This corpus and the annotation guidelines are freely available for ensuring reproducibility and to encourage future research in computational argumentation.Comment: Under review in Computational Linguistics. First submission: 26 October 2015. Revised submission: 15 July 201

A Labelling Framework for Probabilistic Argumentation

The combination of argumentation and probability paves the way to new accounts of qualitative and quantitative uncertainty, thereby offering new theoretical and applicative opportunities. Due to a variety of interests, probabilistic argumentation is approached in the literature with different frameworks, pertaining to structured and abstract argumentation, and with respect to diverse types of uncertainty, in particular the uncertainty on the credibility of the premises, the uncertainty about which arguments to consider, and the uncertainty on the acceptance status of arguments or statements. Towards a general framework for probabilistic argumentation, we investigate a labelling-oriented framework encompassing a basic setting for rule-based argumentation and its (semi-) abstract account, along with diverse types of uncertainty. Our framework provides a systematic treatment of various kinds of uncertainty and of their relationships and allows us to back or question assertions from the literature