Argumentation Factory: Algorithms and Software for Industrial Strength Inconsistency Tolerance

Argumentation Factory：工业强度不一致容忍度的算法和软件

• 批准号: EP/D078695/1
• 负责人: Anthony Hunter
• 金额: $ 40.64万
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2007
• 资助国家: 英国
• 起止时间: 2007 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FD078695%2F1
• 关键词: Argumentation; Factory; Algorithms; Software; Industrial

Humans constantly deal with conflicting information in their everyday lives, but until recently the problem has been largely avoided in computing. Being based on mathematical thinking, the normal approach to inconsistency in computing is to not tolerate it. This is done either by arbitrary removal of conflicting information or by recourse to human intervention. But as computers are being pushed into more intelligent roles with the need for greater robustness, inconsistency tolerance is an increasingly important topic in many areas of computer science including artificial intelligence, robotics, natural language processing, databases, information systems, and software engineering. Inconsistency is omnipresent in the world. So we need to design systems that can address the problems and the opportunities raised by the widespread existence of inconsistency. Recent developments in the theory of argumentation are suggesting that the development and application of argumentation systems could offer a significant technological advance in the development of robust inconsistency tolerance in a wide range of applications.Argumentation is a vital aspect of intelligent behaviour by humans. Consider diverse professionals such as politicians, journalists, clinicians, scientists, and administrators, who all need to collate and analyse information looking for pros and cons for consequences of importance when attempting to understand problems and make decisions. Hence, the development of argumentation systems for decision-support systems for professionals is a promising area. More generally, argumentation systems are increasingly being considered for applications in developing software engineering tools, for constituting an important component of multi-agent systems for negotiation and problem solving, and for data + knowledge fusion. In these kinds of application there is a need to analyse inconsistent information, find competing viewpoints, and resolve conflicts. By argumentation, we can determine that a certain proposition follows from certain assumptions but that one of these assumptions could be disproved (or 'undercut') by other assumptions in our premises. In this way an argumentation system could help us analyse which assumptions were really giving rise the inconsistency and which assumptions were harmless. Argumentation systems can be used to draw arguments from inconsistent information, and to compare them with counterarguments. The theory of logic-based argumentation is therefore helpful in analysing inconsistency and there have been impressive research advances recently. However, argumentation is computationally expensive, and little consideration has been given to how it can be done efficiently.We therefore have a pressing need to develop algorithms and software for generating constellations of arguments and counterarguments. For this, we need automated reasoning technology. However, existing automated reasoning is not designed for finding arguments: It can be used to find a proof of an inference from a set of premises. But it is not intended for finding minimal consistent sets of formulae for proving some inference. Furthermore, with the generating arguments and counterarguments, there is much inefficient recomputation of consistency checks, and of minimality checks, for the supports of the arguments.To address these shortcomings, we want to explore four inter-connected lines of research: (1) Develop algorithms and prototype implementation of system for harnessing existing automated reasoning technology for providing the entailment relation as part of the process of constructing arguments; (2) Develop algorithms and prototype implementation for contouring (a form of lemma generation) of knowledgebases; (3) Develop algorithms and prototype implementation for compilation of knowledgebases; and (4) Develop algorithms and prototype implementation for approximate argumentation.

人类在日常生活中不断地处理相互矛盾的信息，但直到最近，这个问题在计算中基本上被避免了。基于数学思维，在计算中处理不一致性的通常方法是不容忍它。这可以通过任意删除冲突信息或求助于人为干预来实现。但随着计算机被推向更智能的角色，需要更强的鲁棒性，不一致性容忍度在计算机科学的许多领域都是一个越来越重要的话题，包括人工智能，机器人，自然语言处理，数据库，信息系统和软件工程。不一致性在世界上无处不在。因此，我们需要设计一种制度，能够解决普遍存在的不一致性所带来的问题和机会。论证理论的最新发展表明，论证系统的发展和应用可以为在广泛的应用中发展鲁棒的不一致容忍提供重要的技术进步。论证是人类智能行为的一个重要方面。考虑不同的专业人士，如政治家，记者，临床医生，科学家和管理人员，他们都需要整理和分析信息，寻找利弊的重要后果时，试图了解问题和决策。因此，为专业人士的决策支持系统的论证系统的发展是一个很有前途的领域。更一般地说，论证系统越来越多地被认为是在开发软件工程工具的应用程序，构成一个重要组成部分的多代理系统的谈判和解决问题，并为数据+知识融合。在这些应用程序中，需要分析不一致的信息，找到竞争的观点，并解决冲突。通过论证，我们可以确定一个特定的命题来自于特定的假设，但是这些假设中的一个可以被我们前提中的其他假设反证（或“削弱”）。通过这种方式，论证系统可以帮助我们分析哪些假设真正引起了不一致，哪些假设是无害的。论证系统可以用来从不一致的信息中得出论点，并将它们与反驳论点进行比较。因此，基于逻辑的论证理论有助于分析不一致性，最近有令人印象深刻的研究进展。然而，论证是计算昂贵的，很少考虑到它如何可以有效地完成。因此，我们迫切需要开发算法和软件来生成论点和反论点的星座。为此，我们需要自动推理技术。然而，现有的自动推理不是为寻找论据而设计的：它可以用来从一组前提中找到推理的证明。但它并不是为了寻找证明某些推理的最小相容公式集。此外，在生成论点和反论点的情况下，对于论点的支持，一致性检查和最小性检查的重新计算效率很低。为了解决这些缺点，我们想探索四条相互关联的研究路线：（一）开发系统的算法和原型实现，以利用现有的自动推理技术来提供蕴涵关系，作为过程的一部分构建论点;（2）为知识库的轮廓绘制（一种引理生成形式）开发算法和原型实现;（3）为知识库的汇编开发算法和原型实现;（4）为近似论证开发算法和原型实现。

项目成果

Algortihms for effective argumentation in classical propositional logic

经典命题逻辑中有效论证的算法

• 发表时间: 2008
• 作者: A Hunter

Real Arguments Are Approximate Arguments

• 发表时间: 2007-07
• 作者: A. Hunter

Symbolic and Quantitative Approaches to Reasoning with Uncertainty

不确定性推理的符号和定量方法

• DOI: 10.1007/978-3-642-02906-6_36
• 发表时间: 2009
• 作者: Ma J