基于粗糙集和多Agent系统的知识挖掘

通过优化知识表达系统中条件属性对决策属性的依赖度,深入研究了粗糙集并与多Agent系统相结合。利用离散粒子群算法,提出一种基于粒子群优化的粗糙集知识约简算法,该算法解决了启发式算法无法全局搜索进行约简的问题。最后通过在矿井中调度信息的应用验证了有效性。

基于启发式节点增强策略的PRM路径规划方法

针对一般的PRM方法用于移动机器人对复杂地形路径搜索存在的缺陷 ,本文对PRM方法进行了改进 ,提出了一套基于启发式的节点增强的策略 ,提高了PRM方法节点增强阶段对环境的适应性 .此外 ,本文建立了相应的仿真实验系统对策略的有效性进行了实验与分析

港口业船舶优化调度模型设计

以港口船舶计划调度为研究背景 ,分析了港口业计划调度的特征 ,提出了生产计划调度的系统框架 ,并在此基础上建立了以船舶拖期惩罚费用为最小 ,多种因素约束下的调度模型 ,将人工智能技术应用到实际生产调度中 ,实现分层次、分级研究多种资源约束条件下的计划调度和优化

一个用于机器人自然语言理解的英语句法分析系统

在低挡微机中速度较慢的串行处理硬设备条件下，利用本文提出的启发式概念，分层搜索和匹配策略以及设置最大搜索长度等方法，可使推理速度提高一个数量级以上．此外，通过引入语义信息，分阶段消除歧义，自顶向下与自底向上相结合，以及把一般疑问句一律变成相应陈述句的方法，解决了自动英语句法分析中的一系列难题，缩小了知识库的规模。

考虑负荷的制造单元划分方法

制造单元的划分是实施单元化生产的关键途径。本文提出了一种基于工件制造工艺的并考虑机床负荷的单元划分算法。首先根据工件的加工特征及机床负荷定义了同类实体之间相似性。然后根据启发式规则选择工件族及制造单元的种子元素,并以聚类块内的离散程度作为评价标准,对系统内的工件和机床进行了聚类。

CIMS优化排序的度量空间分析法

本文建立了排序的度量空间,并且在该空间上建立了映射概念.在此基础上,应用采样方法,讨论了采样次数与优化的关系,并提出了均匀采样的启发式方法.

数控自动编程专家系统

本文简要地介绍了数控自动编程专家系统.其中包括:专家系统知识表示的形式;分层次的黑板结构;前向推理求解策略和相应的解释功能;系统针对不同类型的曲线组合,采用不同的独立的知识源(KS)进行处理.由于在知识的处理上采用编码技术,在前向推理求解策略中使用启发信息和“剪技”技术,提高了系统的时空效率.系统中的规划程序能自动规划切削路径.输出供数控车床使用的 NC 代码,并可在显示屏上进行图形显示和切削仿真.目前原型系统已经在 IBM-PC 和 Sun3/60计算机上利用FORTRAN 语言实现.

强化学习算法中启发式回报函数的设计及其收敛性分析

回报函数设计的好与坏对学习系统性能有着重要作用,按回报值在状态-动作空间中的分布情况,将回报函数的构建分为两种形式:密集函数和稀疏函数,分析了密集函数和稀疏函数的特点.提出启发式回报函数的基本设计思路,利用基于保守势函数差分形式的附加回报函数,给学习系统提供更多的启发式信息,并对算法的最优策略不变性和迭代收敛性进行了证明.启发式回报函数能够引导学习,加快学习进程,从而可以实现强化学习在实际大型复杂系统应用中的实时控制和调度.

成批生产计划调度的集成建模与优化

针对多品种批量生产类型,建立了调度约束的生产计划与调度集成优化模型。模型的目标函数是使总调整费用、库存费用及生产费用之和最小,约束函数包括库存平衡约束和生产能力约束,同时考虑了调度约束,即工序顺序约束和工件在单机上的加工能力约束,保证了计划可行性。该模型为两层混合整数规划模型,对其求解综合运用了遗传算法和启发式规则,提出了混合启发式求解算法。最后,针对某机床厂多品种批量生产类型车间进行了实例应用,对车间零件月份作业计划进行分解,得到各工段单元零件周作业计划,确定了零件各周生产批量与投产顺序。

学习不良儿童的元认知偏差及矫正

Metacognitive illusions or metacognitive bias is a concept that is a homologous with metacognitve monitor accuracy. In the dissertation, metacognitive illusions mainly refers to the absolute differences between judgment of learning (JOL) and recall because individuals are misguided by some invalid cues or information. JOL is one kind of metacognitive judgments, which is the prediction about the future performance of learned materials. Its mechanism and accuracy are the key issues in the study of JOL. Cue-utilization framework proposed by Koriat (1997) summarized the previous findings and provided a significant advance in understanding how people make JOL. However, the model is not able to explain individual differences in the accuracy of JOL. From the perspective of people’s cognitive bound, our study use posterior associative word pairs easy to produce metacognitive bias to explore the deeper psychological mechanism of metacontive bias. Moreover, we plan to investigate the cause to result in higher metacognitive illusions of children with LD. Based on these, the study tries to look for the method of mending metacognitive illusions. At the same time, we will summarize the findings of this study and previous literatures, and propose a revesied theory for explaining children’s with LD cue selection and utilization according to Koriat’s cue-utilization model. The results of the present study indicated that: (1) Children showed stable metacognitive illusions for the weak associative and posterior associative word pairs, it was not true for strong associative word pairs. It was higher metacognitive illusions for children with LD than normal children. And it was significant grade differences for metacognitive illusions. A priori associative strength exerted a weaker effect on JOL than it did on recall. (2) Children with LD mainly utilized retrieval fluency to make JOL across immediate and delay conditions. However, for normal children, it showed some distinction between encoding fluency and retrieval fluency as potential cues for JOL across immediate and delay conditions. Obviously, children with LD lacked certain flexibility for cue selection and utilization. (3)When word pairs were new list, it showed higher metacognitve transfer effects for analytic inferential group than heuristic inferential group for normal children in the second block. And metacognitive relative accuracy got increased for both children with and without LD across the experimental conditions. However, it was significantly improved only for normal children in analytic inferential group.

5-13岁儿童对惯性运动的认知发展

Research on naïve physics investigates children’s intuitive understanding of physical objects, phenomena and processes. Children, and also many adults, were found to have a misconception of inertia, called impetus theory. In order to investigate the development of this naïve concept and the mechanism underlying it, four age groups (5-year-olds, 2nd graders, 5th graders, and 8th graders) were included in this research. Modified experimental tasks were used to explore the effects of daily experience, perceptual cues and general information-processing ability on children’s understanding of inertia. The results of this research are: 1) Five- to thirteen-year-olds’ understanding of inertia problems which were constituted by two ogjects moving at the same spped undergoes an L-shaped developmental trend; Children’s performance became worse as they got older, and their performance in the experiment did not necessarily ascend with the improvement of their cognitive abilities. 2) The L-shaped developmental curve suggests that children in different ages used different strategies to solve inertia problems: Five- to eight-year-olds only used heuristic strategy, while eleven- to thirteen-year-olds solved problems by analyzing the details of inertia motion. 3) The different performance between familiar and unfamiliar problems showed that older children were not able to spontaneously transfer their knowledge and experience from daily action and observation of inertia to unfamiliar, abstract inertia problems. 4) Five- to eight-year-olds showed straight and fragmented pattern, while more eleven- to thirteen-year-olds showed standard impetus theory and revised impetus theory pattern, which showed that younger children were influenced by perceptual cues and their understanding of inertia was fragmented, while older children had coherent impetus theory. 5) When the perceptual cues were controlled, even 40 percent 5 years olds showed the information-processing ability to analyze the distance, speed and time of two objects traveling in two different directions at the same time, demonstrating that they have achieved a necessary level to theorize their naïve concept of inertia.

弈棋问题解决启发式的元策略模型

Problem solving is one of the basic processes of human cognition and heuristic strategy is the key to human problem solving, hence, the studies on heuristic strategy is of great importance in cognitive psychology. Current studies on heuristics in problem solving may be summarized as follows: nature and structure of heuristics, problem structure and representation, expert knowledge and expert intuition, nature and role of image, social cognition and social learning. The present study deals with the nature and structure of heuristics. The Solitaire problem was used in our the experiments. Both traditional experimental method and computer simulation were used to study the nature and structure of heuristics. Through a series of experiments, the knowledge of Solitaire problem solving was summed up, its metastrategy is worked out, and then the the metastrategy by computer simulation and experimental verification are tested.

Automatic Recognition of Tractability in Inference Relations

A procedure is given for recognizing sets of inference rules that generate polynomial time decidable inference relations. The procedure can automatically recognize the tractability of the inference rules underlying congruence closure. The recognition of tractability for that particular rule set constitutes mechanical verification of a theorem originally proved independently by Kozen and Shostak. The procedure is algorithmic, rather than heuristic, and the class of automatically recognizable tractable rule sets can be precisely characterized. A series of examples of rule sets whose tractability is non-trivial, yet machine recognizable, is also given. The technical framework developed here is viewed as a first step toward a general theory of tractable inference relations.

Three Cuts for Accelerated Interval Propagation

This paper addresses the problem of nonlinear multivariate root finding. In an earlier paper we described a system called Newton which finds roots of systems of nonlinear equations using refinements of interval methods. The refinements are inspired by AI constraint propagation techniques. Newton is competative with continuation methods on most benchmarks and can handle a variety of cases that are infeasible for continuation methods. This paper presents three "cuts" which we believe capture the essential theoretical ideas behind the success of Newton. This paper describes the cuts in a concise and abstract manner which, we believe, makes the theoretical content of our work more apparent. Any implementation will need to adopt some heuristic control mechanism. Heuristic control of the cuts is only briefly discussed here.

Internet Fish

I have invented "Internet Fish," a novel class of resource-discovery tools designed to help users extract useful information from the Internet. Internet Fish (IFish) are semi-autonomous, persistent information brokers; users deploy individual IFish to gather and refine information related to a particular topic. An IFish will initiate research, continue to discover new sources of information, and keep tabs on new developments in that topic. As part of the information-gathering process the user interacts with his IFish to find out what it has learned, answer questions it has posed, and make suggestions for guidance. Internet Fish differ from other Internet resource discovery systems in that they are persistent, personal and dynamic. As part of the information-gathering process IFish conduct extended, long-term conversations with users as they explore. They incorporate deep structural knowledge of the organization and services of the net, and are also capable of on-the-fly reconfiguration, modification and expansion. Human users may dynamically change the IFish in response to changes in the environment, or IFish may initiate such changes itself. IFish maintain internal state, including models of its own structure, behavior, information environment and its user; these models permit an IFish to perform meta-level reasoning about its own structure. To facilitate rapid assembly of particular IFish I have created the Internet Fish Construction Kit. This system provides enabling technology for the entire class of Internet Fish tools; it facilitates both creation of new IFish as well as additions of new capabilities to existing ones. The Construction Kit includes a collection of encapsulated heuristic knowledge modules that may be combined in mix-and-match fashion to create a particular IFish; interfaces to new services written with the Construction Kit may be immediately added to "live" IFish. Using the Construction Kit I have created a demonstration IFish specialized for finding World-Wide Web documents related to a given group of documents. This "Finder" IFish includes heuristics that describe how to interact with the Web in general, explain how to take advantage of various public indexes and classification schemes, and provide a method for discovering similarity relationships among documents.