从用户需求语句建立问题可拓模型的研究

准确地建立待解决问题的可拓模型是可拓策略生成的关键步骤。目前的可拓策略生成系统在建立可拓模型时因自然语言理解的困难,未能充分理解用户需求,所以较难自动建立问题的可拓模型。提出了解析用户自然语言需求语句、并自动建立可拓模型的方法。该方法的核心包括4步:1)对用户需求语句进行组块分析得到短语序列;2)对短语序列进行分类;3)使用匹配规则抽取分类后的短语,得到便于计算机处理的需求信息;4)结合数据库技术进行可拓模型的建立。以租房问题为案例,实现了该方法。实验结果表明,该方法能较好地理解用户需求信息并成功建立租房问题可拓模型。     

可拓策略生成系统与Agent结合的研究

为了提高可拓策略生成系统的能力,探讨了可拓策略生成系统与Agent技术结合的问题.介绍了Agent在可拓策略生成中可以结合的几个方面,重点介绍Agent控制可拓变换发散与收敛过程的作用,以克服计算中容易出现的"组合爆炸"困难,使可拓策略生成系统得到效率较高的策略输出.     

基于可拓学和HowNet的策略生成系统研究进展

在已有可拓策略生成系统框架基础上,利用可拓信息-知识-策略形式化表示体系、HowNet的知识系统描述语言(KDML)和Agent的智能引导,建立了基于可拓学和HowNet的策略生成系统的基本流程和系统框架结构,增强了可拓策略生成系统解决矛盾问题的能力,改善了知识资源缺乏问题,提高了其问题模型建立的准确性和知识处理能力。初步的应用显示了该研究的优越性。把可拓学和HowNet这2个中国原创的理论和应用工具相结合研究策略生成系统,将使这项有别于传统研究思路的基础研究取得重要突破,产生具有完全自主知识产权的成果。这也是为将来能实现矛盾问题的智能化处理作基础性的工作。     

可拓策略生成系统的构件化设计与实现

将可拓学的基本理论和可拓工程的方法运用到防治空气污染的策略生成当中,并通过计算机实现了关于地区性二氧化硫浓度矛盾问题的可拓策略生成系统.系统利用面向构件的开发技术,通过Java EE 5架构,把JSP、Servlet、EJB等技术相结合,大大增加了系统的可重用性和可维护性.     

可拓数据挖掘方法在解决矛盾问题中的研究

阐述了可拓数据挖掘概念和矛盾问题的形式化模型,研究了可拓数据挖掘方法过程及解决矛盾问题的方法,并通过实例进行了说明,得出应用可拓数据挖掘解决矛盾问题的策略可以为技术实现策略生成探索出一条可行之路。     

可拓信息-知识-智能形式化体系研究

利用可拓论和可拓方法，把信息、知识和智能统一在一个形式化体系中，用可拓推理和可拓变换，去建立生成策略的推理规则，把可拓集合和关联函数作为策略生成和策略评价的定量化工具，探讨建立“可拓信息-知识-智能形式化体系”．给出了建立该体系的框架和主要功能模块．这一研究为利用计算机辅助解决矛盾问题提供可行的工具，为提高计算机的智能化水平创造基础条件．     

精神疲劳识别的可拓模型与策略生成

精神疲劳识别中普遍存在着方法的侵扰性、实时性与识别准确率之间相矛盾的问题.为此,引入可拓理论和方法来建立问题的可拓模型,针对矛盾主体建立关联函数和策略优度函数.结合领域知识,通过拓展分析、可拓变换对矛盾进行转化,生成多种同时满足非侵扰性、实时性和识别准确率的特征和识别策略,并对策略优度进行计算和分析.实验研究验证了本方法的有效性.本研究为计算机模拟人类思维进行算法研究和创新奠定了基础.     

基于可拓方法的智能策略生成器的研究

提出了解决矛盾问题的智能型集成化可拓策略生成器的设计思路和总体结构框架。系统设计基于综合集成方法的基本思想,遵循信息组织和可拓决策知识的特点,将物元分析法、专家系统、决策支持系统、神经网络有机地组织集成起来,从而快速、灵活地为决策者提供从知识获取、判断推理到创造性思维、策略生成的智能决策支持环境。     

可拓分类知识挖掘系统的设计与实现

针对决策者处理矛盾问题时需要动态分类知识作为参考依据的需求,研制可拓分类知识挖掘系统。系统采用B/S结构,利用jQuery技术实现Web前端开发,通过MVC框架模式实现后台开发。此外,系统增强了数据预处理能力,提出且实现了挖掘八类可拓分类知识以及动态生成信息元库和知识库。并给出系统在教师科研考核评价中的具体应用,为科研管理者找出适合促进教师科研工作的策略提供科学的依据。     

基于可拓方法的网络购物策略生成问题

针对用户在购物过程中对不同价格、性能以及喜好程度等不尽人意的矛盾问题,以可拓理论为基础,建立问题的可拓模型。通过对物元的特征进行拓展分析及可拓变换生成解决相关矛盾问题的策略集,并根据价格以及性能为综合评价指数,对策略进行优度评价,生成可拓策略集供用户和决策者参考。     

Measuring computer software reliability

Under certain conditions, traditional hypothesis-testing techniques may be used as a management tool by software developers or software purchasers who wish to insure that their packages have some specified reliability level. These conditions are: (1) the existence of independent collections of test data, (2) a way of determining the correctness of processing of these collections, and (3) a way of randomly selecting test data.Two basic approaches have been described. In a fixed sample size test, the user decides on the reliability desired. He can then determine the number of test cases which must be examined and the acceptance/rejection criteria. In a sequential test, the desired reliability level is again pre-determined, but samples are tested one at a time until an accept/reject decision can be made.Experiments with a large amount of error data derived from six separate systems indicate that reliability results derived from these models are consistent with actual reliability figures.Most current acceptance procedures are based on a naive assumption that a large program can be exhaustively tested and delivered in an error-free condition. Because these expectations cannot be fulfilled, the manager of a software development project or the purchaser of a software product is provided with no quantitative information on which to base an acceptance decision and is thus forced to make these decisions based mostly on intuition and his own experience in similar situations. These models allow one to replace these intuition-based decisions with quantitatively-based decisions and thus constitute an important contribution to the science of management of software development efforts.     

Parametric Evaluation of Routing Algorithms in Network on Chip Architecture

Considering that routing algorithms for the Network on Chip (NoC) architecture is one of the key issues that determine its ultimate performance, several things have to be considered for developing new routing algorithms. This includes examining the strengths, capabilities, and weaknesses of the commonly proposed algorithms as a starting point for developing new ones.
Because most of the algorithms presented are based on the well-known algorithms that are studied and evaluated in this research. Finally, according to the results produced under different conditions, better decisions can be made when using the aforementioned algorithms as well as when presenting new routing algorithms. In this research, we first describe the existing algorithms include: XY, YX, Odd- Even and DyAD. We then evaluate each of the routing algorithms which naturally have their own strengths and weaknesses under different conditions. In the first scenario, based on the criteria of average latency, average throughput and average energy consumption in determining the final performance of the network on the chip, we show the algorithms in terms of their performance by deterministic and adaptive routing algorithms. In the second scenario, we evaluate the algorithms based on the network size and the number of cores on the chip. As a result, these algorithms can make better decisions when using these algorithms as well as when presenting new routing algorithms, considering the results produced under different condition.     

Group‐buying coupons considering consumers’ perceived ease of use

This paper examines the design of the online discount coupon, which is a popular marketing tool that offers consumers group‐buying (GB) discounts when they prepay for participating firms’ goods and services. We develop a two‐stage model for a market in which consumers are heterogeneous in their valuation of a product. In our setup, consumers make purchase decisions at the first stage and update their perceptions of the product. As a result, they adjust their repurchase decisions at the second stage. Through the analysis of price discrimination effect and advertising effect, we demonstrate that consumers make their purchase decisions based on not only the discount rate but also the degree of perceived ease of use of the coupons. We then examine both single‐time and double‐time GB mechanisms, and recommend the optimal design for the firm to increase its profitability. Our model also accommodates uncertainty of the degree of consumers' perceived ease of use and shows that the conditions for the optimal GB mechanism are robust.     

A simulation test-bed for mobile adaptive architectures

Existing mobile systems are typically highly constrained with regards to their run-time resources: CPU, memory, communication bandwidth, screen real-estate, battery, and so forth. In current mobile systems, resource allocation decisions are almost always fixed at the time of system creation. However, this situation is arguably changing as mobile systems are becoming more powerful and as the demands being placed upon them are also increasing dramatically. For this reason, such systems need effective methods to manage and control their resources at run-time, particularly in the face of changing environmental conditions and user needs. This paper presents a simulation test-bed for experimenting with architectural design decisions such as communication and negotiation strategies among components, scheduling algorithms, and usability considerations. One significant area that we have begun to experiment with is the use of user-defined “utility” as a means of making dynamic resource allocation decisions. We will discuss the use of utility as a guide for scheduling, describe the test-bed, and present some examples of the results that we have derived, comparing utility-based scheduling with traditional scheduling methods.     

Dynamic scheduling for multi-site companies: a decisional approach based on reinforcement multi-agent learning

In recent years, most companies have resorted to multi-site or supply-chain organization in order to improve their competitiveness and adapt to existing real conditions. In this article, a model for adaptive scheduling in multi-site companies is proposed. To do this, a multi-agent approach is adopted in which intelligent agents have reactive learning capabilities based on reinforcement learning. This reactive learning technique allows the agents to make accurate short-term decisions and to adapt these decisions to environmental fluctuations. The proposed model is implemented on a 3-tier architecture that ensures the security of the data exchanged between the various company sites. The proposed approach is compared to a genetic algorithm and a mixed integer linear program algorithm to prove its feasibility and especially, its reactivity. Experimentations on a real case study demonstrate the applicability and the effectiveness of the model in terms of both optimality and reactivity.     

Social learning with heterogeneous agents and sequential decision making

In this paper, we consider the problem of social learning in a network of agents where the agents make decisions sequentially by choosing one of two hypotheses on the state of nature. Each agent observes a signal generated according to one of the hypotheses and knows the decisions of all the previous agents in the network. The network contains two types of agents: rational and irrational. A rational agent makes a decision by not only using its private observation but also the decisions of each of the agents which already made decisions. To that end, the agent employs Bayesian theory. An irrational agent makes a decision by ignoring the available information and by randomly choosing the hypothesis. We analyze the asymptotic performance of a system with rational and irrational agents where we study rational agents that use either a deterministic or random decision making policies. We propose a specific random decision making policy that is based on the social belief and the private signals of the agents. We prove that under mild conditions the expected social belief in the true state of nature tends to one if the rational agents use the proposed random policy. In a network with rational agents that use deterministic policy, the conditions for convergence are stricter. We provide simulation results on the studied systems and compare their performance.     

Learning and Herding Using Case-Based Decisions With Local Interactions

We evaluate repeated decisions of individuals using a variant of the case-based decision theory (CBDT), where individuals base their decisions on their own past experience and the experience of neighboring individuals. Looking at a range of scenarios to determine the successful outcome of a decision, we find that for learning to occur, agents must have a sufficient number of neighbors to learn from and access to sufficiently independent information. If these conditions are not fulfilled, we can easily observe herding in cases where no best decision exists.     

Methods for computing Nash equilibria of a location–quantity game

A two-stage model is described where firms take decisions on where to locate their facility and on how much to supply to which market. In such models in literature, typically the market price reacts linearly on supply. Often two competing suppliers are assumed or several that are homogeneous, i.e., their cost structure is assumed to be identical. The focus of this paper is on developing methods to compute equilibria of the model where more than two suppliers are competing that each have their own cost structure, i.e., they are heterogeneous. Analytical results are presented with respect to optimality conditions for the Nash equilibria in the two stages. Based on these analytical results, an enumeration algorithm and a local search algorithm are developed to find equilibria. Numerical cases are used to illustrate the results and the viability of the algorithms. The methods find an improvement of a result reported in literature.