基于范例推理的船舶避碰决策支持系统

根据船舶避碰的特点，将范例推理（Case-based Reasoning，CBR）方法引入到船舶避碰决策支持系统的设计中．为提高决策系统的性能，优化范例表示方法；并在分析阶段先对碰撞局面进行分类；再通过搜索树进行范例匹配；最后，讨论该方法的可行性．     

基于实例的智能工艺设计系统

针对传统智能工艺设计系统的缺陷与不足，结合基于实例推理（Case-Based Reasoning,CBR）和基于规则推理（Rule-Based Reasoning,RBR)的方法，设计了一个基于实例的智能工艺设计系统，给出了工艺实例一个完整清晰的形式化描述，阐述了新零件与实例进行比较和匹配的策略和算法，在检索出相符的实例后，调用RBR方法对实例进行修正，最终完成复杂的工艺设计任务。     

改进双延迟深度确定性策略梯度的多船协调避碰决策

目前，多数海上避碰模型都是将船舶作为单智能体进行避碰决策，未考虑船舶间的协调避让，在多船会遇场景下仅靠单船进行避碰操作会导致避让效果不佳。为此，提出了一种改进双延迟深度确定性策略梯度算法(TD3)的Softmax深层双确定性策略梯度(SD3)多船协调避碰模型。从考虑船舶航行安全的时空因素出发构建时间碰撞模型、空间碰撞模型，对船舶碰撞风险进行定量分析，在此基础上采用根据会遇态势和船速矢量动态变化的船域模型对船舶碰撞风险进行定性分析。综合船舶目标导向、航向角改变、航向保持、碰撞风险和《国际海上避碰规则》(COLREGS)的约束设计奖励函数，结合COLREGS中的典型相遇情况构造对遇、追越和交叉相遇多局面共存的会遇场景进行避碰模拟仿真。消融实验显示softmax运算符提升了SD3算法的性能，使其在船舶协调避碰中拥有更好的决策效果，并与其他强化学习算法进行学习效率和学习效果的比较。实验结果表明，SD3算法在多局面共存的复杂场景下能高效做出准确的避碰决策，并且性能优于其他强化学习算法。     

船舶拟人智能避碰决策理论的集成机器学习策略

简述了研究船舶拟人智能避碰决策(PIDVCA)的意义、目标及其实现方法,提出了基于机器学习构建动态避碰知识库的关键技术及PIDVCA理论的机器学习机制,并结合避碰仿真实例,着重讨论了PIDVCA理论的集成机器学习策略以及获取动态避碰知识的机理.     

基于案例推理和规则推理的公安突发事件辅助决策算法

为了提高突发事件发生时公安指挥部门处置决策方案的及时性和科学性,本文提出基于案例推理(Case-Based Reasoning, CBR)和规则推理(Rule-Based Reasoning, RBR)的公安突发事件辅助决策算法。算法根据突发事件的级别、类型和突发事件中的具体数据,如伤亡人数等,通过CBR检索出案例库中同级别同类型的最相似案例,再通过RBR对检索案例的结果进行修正优化使之更适用于突发事件的实际情况。最后通过实例成功地验证了该算法。该算法能够为公安应急预案与辅助决策平台的建设提供参考。     

基于案例推理的工作流异常处理研究

对工作流的异常和案例推理(Case-Based Reasoning,简称CBR)的机制进行了介绍,给出了一个应用CBR技术进行异常处理的工作流模型,并研究了应用CBR方法处理工作流异常的关键技术。     

一种新型船舶避碰决策支持系统的设计与实现

该文介绍了一种新型的船舶避碰决策支持系统——基于Multi-Agent的协商避碰决策支持系统的设计与实现。该系统是一个开放的、智能化的系统,它基于系统论的思想,把协商决策引入船舶避碰,是一个包括避碰决策Agent、协商决策Agent、网络通信Agent和法律仲裁Agent等的Multi-Agent系统,该系统可以最大限度地减少避碰决策中的不确定性,具有良好的用户界面,能提供网络环境下智能化的决策支持,可以更好地解决船舶避碰问题。     

基于案例推理的工作流异常处理研究

对工作流的异常和案例推理(Case-Based Reasoning,简称CBR)的机制进行了介绍,给出了一个应用CBR技术进行异常处理的工作流模型,并研究了应用CBR方法处理工作流异常的机制。     

框架系统中的近似推理

本文在概述 L.A.Zadeh 提出的近似推理理论 AR(Approximate Reasoning)中的知识表示及推理规则的基础上,着重讨论了 AR 理论在框架系统中的应用,对如何运用 AR 理论处理默认知识(default knowledge)也作了较为详细的介绍。     

铁路行车事故救援决策方法研究

传统的铁路行车事故救援多采用人工方式给出救援方案,但事故受多方面因素的影响,救援人员很难及时的给出科学合理的救援方案.针对已有救援知识不完备、不系统的特点,提出规则推理(Rule-based Reasoning,RBR)和案例推理(Case-Based Reasoning,CBR)相结合的两级分层推理框架,给出了系统流程图,说明了RBR与CBR的具体实现方法,并将自组织特征映射网络(Self-Organizing Feature Map,SOFM)应用到事例检索中,有效地提高了检索的效率.仿真实验结果表明系统取得了良好的效果.克服了单一推理的缺点,实现了对救援理论和经验的复用,提高了系统的效率和综合推理能力,并使系统具有了学习能力.研究结果为进一步应用奠定了基础.     

An Exception Handling of Rule-Based Reasoning Using Case-Based Reasoning

In this paper, we propose the CCAR (Combining Case-based And Rule-based reasoning) model for an exception handling of Rule-based Reasoning using Case-based Reasoning. The central idea of the model proposed in this paper is to represent the exception of a rule as a case, and to utilize the case for a solution to a problem, and then to search the case memory to retrieve a case which violates the conclusion of a rule. If the similarity between a target problem and the selected case is high, the conclusion of a case is applied. Otherwise, the conclusion of rule-based reasoning is applied.     

Multi-modal diagnosis combining case-based and model-based reasoning: a formal and experimental analysis

Integrating different reasoning modes in the construction of an intelligent system is one of the most interesting and challenging aspects of modern AI. Exploiting the complementarity and the synergy of different approaches is one of the main motivations that led several researchers to investigate the possibilities of building multi-modal reasoning systems, where different reasoning modalities and different knowledge representation formalisms are integrated and combined. Case-Based Reasoning (CBR) is often considered a fundamental modality in several multi-modal reasoning systems; CBR integration has been shown very useful and practical in several domains and tasks. The right way of devising a CBR integration is however very complex and a principled way of combining different modalities is needed to gain the maximum effectiveness and efficiency for a particular task. In this paper we present results (both theoretical and experimental) concerning architectures integrating CBR and Model-Based Reasoning (MBR) in the context of diagnostic problem solving. We first show that both the MBR and CBR approaches to diagnosis may suffer from computational intractability, and therefore a careful combination of the two approaches may be useful to reduce the computational cost in the average case. The most important contribution of the paper is the analysis of the different facets that may influence the entire performance of a multi-modal reasoning system, namely computational complexity, system competence in problem solving and the quality of the sets of produced solutions. We show that an opportunistic and flexible architecture able to estimate the right cooperation among modalities can exhibit a satisfactory behavior with respect to every performance aspect. An analysis of different ways of integrating CBR is performed both at the experimental and at the analytical level. On the analytical side, a cost model and a competence model able to analyze a multi-modal architecture through the analysis of its individual components are introduced and discussed. On the experimental side, a very detailed set of experiments has been carried out, showing that a flexible and opportunistic integration can provide significant advantages in the use of a multi-modal architecture.     

基于案例推理的在线私人旅行助理系统

传统的旅游电子商务网站所提供的旅行服务无法满足旅行者个性化的需求,该文提出用基于案例推理的方法来制定私人旅行计划的思想,并提出了基于案例推理的在线私人旅行助理系统CBROPTAS(Case—beasoning for Online Private Travel Assisrant System)的原型,研究了CBROPTAS中的关键技术：案例模型、案例检索和交互式查询管理等。文中最后总结了CBROPTAS在提供私人旅行服务方面具有的优势,并给出了该原型系统目前存在的不足。     

基于案例推理(CBR)的鱼病诊断模型研究

对基于案例的推理方法进行了系统的研究,提出了一种利用遗传算法来完成特征项权空间的直接搜索的算法,由遗传算法进行特征权值的优化。提出了将案例知识与规则相结合的方法,并将其应用于鱼病诊断系统中,可以有效地解决鱼病的诊断问题,并提高了系统的运行效率。     

蛋鸡饲料配方专家系统:ICMIX——一种基于范例推理的实现

介绍基于范例推理实现的蛋鸡饲料配方专家系统ICMIX。讨论了ICMIX的范例、范例组织和检索、范例修改和修补问题。基于范例推理比传统的基于规则推理更适用于蛋鸡饲料配方这一要求,考虑多种制约因素、需要丰富实践经验的问题域。     

Necessary Conditions of Two-Level Uncertainty Reasoning Model (URM) and the Improvement on It

Based on the previous work,some necessary conditions of the two-level Uncertainty Reasoning Model(URM) are proposed and an improvement on the two-level URM is made that can describe and process the deviation.In additon,the paper presents two theorems for specifying the correctness about the improvement.Finally,the application of the two-level URM is discussed.     

A model of legal reasoning with cases incorporating theories and values

Reasoning with cases has been a primary focus of those working in AI and law who have attempted to model legal reasoning. In this paper we put forward a formal model of reasoning with cases which captures many of the insights from that previous work. We begin by stating our view of reasoning with cases as a process of constructing, evaluating and applying a theory. Central to our model is a view of the relationship between cases, rules based on cases, and the social values which justify those rules. Having given our view of these relationships, we present our formal model of them, and explain how theories can be constructed, compared and evaluated. We then show how previous work can be described in terms of our model, and discuss extensions to the basic model to accommodate particular features of previous work. We conclude by identifying some directions for future work.     

语义Web中的本体推理研究

从语义Web的基本概念开始,介绍了语义Web的层次结构;介绍了本体的基本概念以及用于本体描述的几种语言。用W3C推荐的本体描述语言OWL描述了一个本体实例,通过此实例对本体推理在本体建立中的冲突消解、描述优化、本体的合并和实例归类中的应用进行了研究,说明了本体推理在本体建立及本体应用中的作用。本体技术是语义Web的核心技术,所以建立和维护本体是语义Web中的主要工作之一,而基于本体的推理可以帮助建立和维护本体。     

A logical approach to representing and reasoning about space

The need for a formal language in which to express and reason about spatial concepts is of crucial importance in many areas of AI and visual systems. For the last five years, spatial reasoning research by the Qualitative Spatial Reasoning Group, University of Leeds, has centred on the development and application of such a language — the RCC spatial logic. Below, we briefly describe the work of the group in this area.     

DMRFNet: Deep Multimodal Reasoning and Fusion for Visual Question Answering and explanation generation

Visual Question Answering (VQA), which aims to answer questions in natural language according to the content of image, has attracted extensive attention from artificial intelligence community. Multimodal reasoning and fusion is a central component in recent VQA models. However, most existing VQA models are still insufficient to reason and fuse clues from multiple modalities. Furthermore, they are lack of interpretability since they disregard the explanations. We argue that reasoning and fusing multiple relations implied in multimodalities contributes to more accurate answers and explanations. In this paper, we design an effective multimodal reasoning and fusion model to achieve fine-grained multimodal reasoning and fusion. Specifically, we propose Multi-Graph Reasoning and Fusion (MGRF) layer, which adopts pre-trained semantic relation embeddings, to reason complex spatial and semantic relations between visual objects and fuse these two kinds of relations adaptively. The MGRF layers can be further stacked in depth to form Deep Multimodal Reasoning and Fusion Network (DMRFNet) to sufficiently reason and fuse multimodal relations. Furthermore, an explanation generation module is designed to justify the predicted answer. This justification reveals the motive of the model’s decision and enhances the model’s interpretability. Quantitative and qualitative experimental results on VQA 2.0, and VQA-E datasets show DMRFNet’s effectiveness.