A computational argumentation methodology for capturing and analyzing design rationale arising from multiple perspectives

Sigman, Scott; Liu, Xiaoqing Frank

doi:10.1016/s0950-5849(02)00187-8

Cited by 13 publications

(4 citation statements)

References 6 publications

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“…Figure 2 visualizes this reduction. For a more in-depth explanation of the fuzzy logic engine and argument reduction method, refer to [3,[29][30][31]53].…”

Section: Deriving Viewpoint Vectors Using Icasmentioning

confidence: 99%

Cross-issue correlation based opinion prediction in cyber argumentation

Rahman

Liu

Sirrianni

et al. 2022

AAC

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One of the challenging problems in large scale cyber-argumentation platforms is that users often engage and focus only on a few issues and leave other issues under-discussed and under-acknowledged. This kind of non-uniform participation obstructs the argumentation analysis models to retrieve collective intelligence from the underlying discussion. To resolve this problem, we developed an innovative opinion prediction model for a multi-issue cyber-argumentation environment. Our model predicts users’ opinions on the non-participated issues from similar users’ opinions on related issues using intelligent argumentation techniques and a collaborative filtering method. Based on our detailed experimental results on an empirical dataset collected using our cyber-argumentation platform, our model is 21.7% more accurate, handles data sparsity better than other popular opinion prediction methods. Our model can also predict opinions on multiple issues simultaneously with reasonable accuracy. Contrary to existing opinion prediction models, which only predict whether a user agrees on an issue, our model predicts how much a user agrees on the issue. To our knowledge, this is the first research to attempt multi-issue opinion prediction with the partial agreement in the cyber-argumentation platform. With additional data on non-participated issues, our opinion prediction model can help the collective intelligence analysis models to analyze social phenomena more effectively and accurately in the cyber argumentation platform.

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“…Figure 2 visualizes this reduction. For a more in-depth explanation of the fuzzy logic engine and argument reduction method, refer to [3,[29][30][31]53].…”

Section: Deriving Viewpoint Vectors Using Icasmentioning

confidence: 99%

Cross-issue correlation based opinion prediction in cyber argumentation

Rahman

Liu

Sirrianni

et al. 2022

AAC

View full text Add to dashboard Cite

show abstract

“…A quasi-automated generation and representation of arguments or decision points are seen as the major challenge for computational reasoning. In addition, recognizing the sufficiency of argumentation and maintenance of the validity of the arguments are additional challenges [39]. In particular generating informal proofs that are not directly driven by logical rules or physical causalities is problematic [40].…”

Section: Achieving System-level Holism In Reasoning and Decision Makingmentioning

confidence: 99%

A Computational Framework for Procedural Abduction Done by Smart Cyber-Physical Systems

Horváth

2018

Designs

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To be able to provide appropriate services in social and human application contexts, smart cyber-physical systems (S-CPSs) need ampliative reasoning and decision-making (ARDM) mechanisms. As one option, procedural abduction (PA) is suggested for self-managing S-CPSs. PA is a knowledge-based computation and learning mechanism. The objective of this article is to provide a comprehensive description of the computational framework proposed for PA. Towards this end, first the essence of smart cyber-physical systems is discussed. Then, the main recent research results related to computational abduction and ampliative reasoning are discussed. PA facilitates beliefs-driven contemplation of the momentary performance of S-CPSs, including a ‘best option’-based setting of the servicing objective and realization of any demanded adaptation. The computational framework of PA includes eight clusters of computational activities: (i) run-time extraction of signals and data by sensing, (ii) recognition of events, (iii) inferring about existing situations, (iv) building awareness of the state and circumstances of operation, (v) devising alternative performance enhancement strategies, (vi) deciding on the best system adaptation, (vii) devising and scheduling the implied interventions, and (viii) actuating effectors and controls. Several cognitive algorithms and computational actions are used to implement PA in a compositional manner. PA necessitates not only a synergic interoperation of the algorithms, but also an objective-dependent fusion of the pre-programmed and the run time acquired chunks of knowledge. A fully fledged implementation of PA is underway, which will make verification and validation possible in the context of various smart CPSs.

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“…Tang et al (2002) and Karsak (Karsak 2004) develop systems that enhance Quality Function Deployment (QFD) with fuzzy linguistic terms to represent design requirements and which enable assessment of the extent to which design requirements are met. Design of products to meet personal requirements such as look, feel and taste represents an interesting use of fuzzy logic that is explored by Cai et al (2003) for product appearance, by Park and Han (2004) for the design of office chairs, by Hanson (Hanson et al 2003) for design of car interiors and by Sigman and Liu (2003) for modelling non-functional requirements for software design and development.…”

Section: Fuzzy Logic and Designmentioning

confidence: 99%

AI and OR in management of operations: history and trends

Kobbacy

Vadera

Rasmy

2007

Journal of the Operational Research Society

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The last decade has seen a considerable growth in the use of AI for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The papers are categorised into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorised in terms of the AI techniques used: genetic algorithms, case based reasoning, knowledge based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested.

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A computational argumentation methodology for capturing and analyzing design rationale arising from multiple perspectives

Cited by 13 publications

References 6 publications

Cross-issue correlation based opinion prediction in cyber argumentation

Cross-issue correlation based opinion prediction in cyber argumentation

A Computational Framework for Procedural Abduction Done by Smart Cyber-Physical Systems

AI and OR in management of operations: history and trends

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