11,254 research outputs found
Management of an intelligent argumentation network for a web-based collaborative engineering design environment
Conflict resolution is one of the most challenging tasks in collaborative engineering design. In the previous research, a web-based intelligent collaborative system was developed to address this challenge based on intelligent computational argumentation. However, two important issues were not resolved in that system: priority of participants and self-conflicting arguments. In this thesis, two methods are developed for incorporating priorities of participants into the computational argumentation network: 1) weighted summation and 2) re-assessment of strengths of arguments based on priority of owners of the argument using fuzzy logic inference. In addition, a method for detection of self-conflicting arguments was developed --Abstract, page iii
Online Handbook of Argumentation for AI: Volume 1
This volume contains revised versions of the papers selected for the first
volume of the Online Handbook of Argumentation for AI (OHAAI). Previously,
formal theories of argument and argument interaction have been proposed and
studied, and this has led to the more recent study of computational models of
argument. Argumentation, as a field within artificial intelligence (AI), is
highly relevant for researchers interested in symbolic representations of
knowledge and defeasible reasoning. The purpose of this handbook is to provide
an open access and curated anthology for the argumentation research community.
OHAAI is designed to serve as a research hub to keep track of the latest and
upcoming PhD-driven research on the theory and application of argumentation in
all areas related to AI.Comment: editor: Federico Castagna and Francesca Mosca and Jack Mumford and
Stefan Sarkadi and Andreas Xydi
Management of an Intelligent Argumentation Network for a Web-Based Collaborative Engineering Design Environment
Conflict resolution is one of the most challenging tasks in collaborative engineering design. In our previous research, a web-based intelligent collaborative system was developed to address this challenge based on intelligent computational argumentation. However, two important issues were not resolved in that system: priority of participants and self-conflicting arguments. In this paper, we develop two methods for incorporating priorities of participants into the computational argumentation network: 1) weighted summation and 2) re-assessment of strengths of arguments based on priority of owners of the argument using fuzzy logic inference. In addition, we develop a method for detection of self-conflicting arguments. Incorporation of priority of participants and detection of self-conflicting arguments have strengthen the capability of managing intelligent argumentation network for the web-based collaborative engineering design system developed in our previous research
Argumentation for Knowledge Representation, Conflict Resolution, Defeasible Inference and Its Integration with Machine Learning
Modern machine Learning is devoted to the construction of algorithms and computational procedures that can automatically improve with experience and learn from data. Defeasible argumentation has emerged as sub-topic of artificial intelligence aimed at formalising common-sense qualitative reasoning. The former is an inductive approach for inference while the latter is deductive, each one having advantages and limitations. A great challenge for theoretical and applied research in AI is their integration. The first aim of this chapter is to provide readers informally with the basic notions of defeasible and non-monotonic reasoning. It then describes argumentation theory, a paradigm for implementing defeasible reasoning in practice as well as the common multi-layer schema upon which argument-based systems are usually built. The second aim is to describe a selection of argument-based applications in the medical and health-care sectors, informed by the multi-layer schema. A summary of the features that emerge from the applications under review is aimed at showing why defeasible argumentation is attractive for knowledge-representation, conflict resolution and inference under uncertainty. Open problems and challenges in the field of argumentation are subsequently described followed by a future outlook in which three points of integration with machine learning are proposed
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
Designing Normative Theories for Ethical and Legal Reasoning: LogiKEy Framework, Methodology, and Tool Support
A framework and methodology---termed LogiKEy---for the design and engineering
of ethical reasoners, normative theories and deontic logics is presented. The
overall motivation is the development of suitable means for the control and
governance of intelligent autonomous systems. LogiKEy's unifying formal
framework is based on semantical embeddings of deontic logics, logic
combinations and ethico-legal domain theories in expressive classic
higher-order logic (HOL). This meta-logical approach enables the provision of
powerful tool support in LogiKEy: off-the-shelf theorem provers and model
finders for HOL are assisting the LogiKEy designer of ethical intelligent
agents to flexibly experiment with underlying logics and their combinations,
with ethico-legal domain theories, and with concrete examples---all at the same
time. Continuous improvements of these off-the-shelf provers, without further
ado, leverage the reasoning performance in LogiKEy. Case studies, in which the
LogiKEy framework and methodology has been applied and tested, give evidence
that HOL's undecidability often does not hinder efficient experimentation.Comment: 50 pages; 10 figure
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