计算机生成兵力中的人工智能技术

开发CGF的难点和重点在于CGF实体行为的生成,尤其是人类智能行为的实现。人类行为建模最重要的三个方面是知识获取、知识表示和决策机制,都与人工智能技术相关。该文主要介绍人类行为描述的知识表示和知识获取问题,讨论了人工智能技术在这两个方面的应用,并对将来的研究工作做了一个简单的展望。     

Coding distributive lattices with Edge Firing Games

In this note, we show that any distributive lattice is isomorphic to the set of reachable configurations of an Edge Firing Game. Together with the result of James Propp, saying that the set of reachable configurations of any Edge Firing Game is always a distributive lattice, this shows that the two concepts are equivalent.     

Automated Support for Building and Extending Expert Models

Building a knowledge-based system is like developing a scientific theory. Although a knowledge base does not constitute a theory of some natural phenomenon, it does represent a theory of how a class of professionals approaches an application task. As when scientists develop a natural theory, builders of expert systems first must formulate a model of the behavior that they wish to understand and then must corroborate and extend that model with the aid of specific examples. Thus there are two interrelated phases of knowledge-base construction: (1) model building and (2) model extension. Computer-based tools can assist developers with both phases of the knowledge-acquisition process. Workers in the area of knowledge acquisition have developed computer-based tools that emphasize either the building of new models or the extension of existing models. The PROTÉGÉ knowledge-acquisition system addresses these two activities individually and facilitates the construction of expert systems when the same general model can be applied to a variety of application tasks.     

示例学习的广义扩张矩阵算法及其实现

本文对扩张矩阵理论加以扩充，提出关于公式的扩张矩阵的概念，并据此实现一个广义扩张矩阵算法叫做ＡＥ９，本文还将ＡＥ９和ＡＱ１５应用于几个实际领域的学习问题，如睡眠状态的分类，手写数字识别等，结果都表明ＡＥ９比ＡＱ１５分类精度更高。     

NP问题的最优轮复杂性知识的零知识证明

NP问题已有的知识的(黑箱)零知识证明都是非常数轮的,因此,在标准的复杂性假设下,NP问题是否存在常数轮的(黑箱)知识的零知识证明是一个有意义的问题.本文对该问题进行了研究,在一定的假设下给出了HC问题的两个常数轮知识的零知识证明系统.根据Katz最近的研究结果,在多项式分层不坍塌的条件下,本文基于claw-free陷门置换给出的HC问题的5轮知识的零知识证明系统具有最优的轮复杂性.     

A new intelligent prediction system model-the compound pyramid model

A current development trend in research on intelligent systems is to optimize a general intelligent prediction system into an individuation intelligent prediction system that is applied in specialized fields. Protein structure prediction is a challenging international issue. In this paper, we propose a new intelligent prediction system model, designed as a multi-layer compound pyramid model, for predicting secondary protein structure. The model comprises four independent intelligent interfaces and several knowledge discovery methods. The model penetrates throughout the domain knowledge, with the effective attributes chosen by Causal Cellular Automata. Furthermore, a high pure structure database is constructed for training. On the RS126 dataset, the overall state per-residue accuracy, Q3, reached 83.99%, while on the CB513 dataset, Q3 reached 85.58%. Meanwhile, on the CASP8 sequences, the results are superior to those produced by other methods, such as Psipred, Jpred, APSSP2 and BehairPred. These results confirm that our method has a strong generalization ability, and that it provides a model for the construction of other intelligent systems.     

C++程序设计教学中的技能培养

本文阐述了知识与技能的关系,以及C++程序设计教学中知识和技能的双重性要求。以选择语句为例,演示了"先知识后技能"的教学过程。实践证明,这样的教学安排行之有效。     

基于知识图谱的互补项目推荐

在缺乏用户交互互补项目方面数据的情况下,将用户对项目的偏好融合到只考虑项目关系的互补项目推荐中,提高推荐模型的性能。提出一种基于知识图谱的互补项目推荐方法,在用户历史交互项目集中推测用户交互的互补项目,基于知识图谱提取用户对互补项目的偏好,利用图像与文本学习项目之间的互补关系,最后基于神经网络实现二者的共同学习。提出的方法在Amazon数据集上与次优的基线方法相比,ACC提升了7%,precision提升了3%,这说明提出的方法性能优异。该算法共同学习用户对项目的偏好与项目之间的互补关系,提升了推荐性能。     

基于ECC-ZKP的农产品供应链可控身份管理与认证模型

基于区块链的农产品供应链中,当参与主体数量规模越来越大时,节点与企业之间的1：1匹配关系将造成节点数量众多,从而造成网络开销大、共识效率低等问题。针对这些问题,引入中间件使得节点与企业之间1：1关系变为1：n的关系,对所涉及到的企业身份管理以及相互之间的认证,提出一种基于ECC-ZKP（elliptic curve cryptosystem-zero—knowledge proof）的可控身份管理与认证模型,实现身份的可控管理和完成可信交易前所需要的身份认证。通过模型分析和仿真实验分析结果表明,该模型能够提供较高的安全性,能够有效进行身份的管理和认证,且节点与企业之间1：n关系相比较于1：1关系减少了网络开销并提高了共识效率。     

概念表示增强的知识追踪模型

知识追踪模型以学习者的历史学习行为数据作为输入,通过概念表示来描述学习者的概念掌握状态,从而预测学习者未来的学习表现。然而在概念的外延表示方面,当前知识追踪研究的概念外延信息被限制在一阶相关的范畴内,无法表征概念的一阶以上外延信息。为了解决这一问题,提出方法首先使用图结构描述概念内涵信息及其相互关系;其次使用图神经网络的池化操作等提取概念的外延表示,这保证了概念的外延信息来源于多阶相关关系;再与概念的内涵表示进行融合;最后预测学习者未来的答题情况。为了验证该模型的有效性和效率,选取了四个主流知识追踪模型作为对比模型,在四个常用的知识追踪数据集上进行实验。结果表明,提出模型在若干评价指标上均取得了一定的优势,说明了它的有效性;在模型性能方面,提出模型达到最优评价指标所需的迭代次数最少,说明了它的效率;在实际应用方面,以该模型为基础实现了一个智能学习平台,在三门线下课程的教学过程中判断和预测学习者未来答题情况,取得了优于其他知识追踪模型的表现。