Arguments for Qualitative Risk Assessment: The StAR Risk Adviser

Calculating risk is relatively straightforward when there is reliable statistical evidence on which to base a judgment. However, novel technologies are often characterised by a lack of such historical data, which creates a problem for risk assessment. In fact, numerical risk assessments can be positively misleading in such situations. We describe a decision support system – StAR – that gives quantitative assessments where appropriate, but which is also able to provide qualitative risk assessments based on arguments for and against the presence of risk. The user is presented with a summary statement of risk, together with the arguments that underlie this assessment. Furthermore, the user is able to search beyond these top-level arguments in order to discover more about the available evidence. Here we suggest that this approach is well-suited to the way in which people naturally make decisions, and we show how the StAR approach has been implemented in the domain of toxicological risk assessment.     

A LOGIC OF ARGUMENTATION FOR REASONING UNDER UNCERTAINTY

We present the syntax and proof theory of a logic of argumentation, LA. We also outline the development of a category theoretic semantics for LA. LA is the core of a proof theoretic model for reasoning under uncertainty. In this logic, propositions are labeled with a representation of the arguments which support their validity. Arguments may then be aggregated to collect more information about the potential validity of the propositions of interest. We make the notion of aggregation primitive to the logic, and then define strength mappings from sets of arguments to one of a number of possible dictionaries. This provides a uniform framework which incorporates a number of numerical and symbolic techniques for assigning subjective confidences to propositions on the basis of their supporting arguments. These aggregation techniques are also described with examples.     

一种基于辩论的决策方法研究

为了解决动态开放环境下决策方案可行性论证的难题,将辩论理论与实用推理结合提出了一种新的适用于多属性决策的辩论框架,定义了论据的类型和论据之间可能存在的攻击关系。将基于辩论的多属性决策过程分为用户需求获取、方案可行性论证和可行方案选优3个阶段,通过论据及其攻击关系对方案的可行性进行辩论论证。最后采用一个医疗决策问题对该框架的应用进行了示例说明,结果表明,该框架能够较好的处理信息不完整、不一致条件下的决策问题。     

高大模板工程方案的编制和专家论证及监理

结合具体工程实例,详细地介绍了高大模板工程方案的编制、专家论证及实施监理,得出了该工程在浇筑过程中未出现任何异常情况,高大支模的施工能够在安全和质量保证的前提下顺利完成的结论。     

A recursive genetic framework for evolutionary decision-making in problems with high dynamism

Communication and coordination are the main cores for reaching a constructive agreement among multi-agent systems (MASs). Dividing the overall performance of MAS to individual agents may lead to group learning as opposed to individual learning, which is one of the weak points of MASs. This paper proposes a recursive genetic framework for solving problems with high dynamism. In this framework, a combination of genetic algorithm and multi-agent capabilities is utilised to accelerate team learning and accurate credit assignment. The argumentation feature is used to accomplish agent learning and the negotiation features of MASs are used to achieve a credit assignment. The proposed framework is quite general and its recursive hierarchical structure could be extended. We have dedicated one special controlling module for increasing convergence time. Due to the complexity of blackjack, we have applied it as a possible test bed to evaluate the system’s performance. The learning rate of agents is measured as well as their credit assignment. The analysis of the obtained results led us to believe that our robust framework with the proposed negotiation operator is a promising methodology to solve similar problems in other areas with high dynamism.     

Defeasible Inheritance Through Specialization

Typed substitution provides a means of capturing inheritance in logic deduction systems. However, in the presence of method overriding and multiple inheritance, inheritance is known to be nonmonotonic, and the semantics of programs becomes a problematic issue. This article attempts to provide a general framework, based on Dung's argumentation theoretic framework, for developing a natural semantics for programs with dynamic nonmonotonic inheritance. The relationship between the presented semantics and perfect‐model (with overriding) semantics, proposed by Dobbie and Topor (1995), is investigated. It is shown that for inheritance‐stratified programs, the two semantics coincide. However, the proposed semantics also provides correct skeptical meanings for the programs that are not inheritance‐stratified.     

论辩系统的动态性及其研究进展

论辩系统是一种非单调形式体系,能够支持个体Agent的推理决策和多Agent之间的有效交互.由于个体Agent的知识、观察信息和资源的动态性以及多Agent交互过程的动态性,在各类论辩系统中,论证及其攻击关系的动态性是普遍存在的.作为一个新的研究领域,有关论辩系统动态性的概念、理论和方法远未成熟.在介绍论辩系统相关概念的基础上,阐明论辩系统动态性的两个主要研究方向(正向动态性和逆向动态性),并讨论需要解决的开放性问题.围绕这些问题,简要回顾现有的理论和方法,并分析其特点和不足.