Adaptive flexibility and maladaptive routines in selecting fast and frugal decision strategies.

Decision routines unburden the cognitive capacity of the decision maker. In changing environments, however, routines may become maladaptive. In 2 experiments with a hypothetical stock market game (n = 241), the authors tested whether decision routines tend to persist at the level of decision strategies rather than at the level of options in strategy selection. The payoff structure of the task was changed after 80 decision trials, rendering a new strategy optimal with respect to expected payoff. Whereas most participants detected the appropriate strategy at the beginning of the task, they tended to retain it even when it was no longer optimal. A hint about a possible change had only a small influence on this maladaptive routine; a monetary incentive had none. Switching to a similar but not identical task relaxed the routine, but not much. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

利用模糊评判方法确定张东构造油藏储量计算的可靠程度

张巨河油田张东构造地处大港滩海交接处,海域部分探井少.目前用油藏描述方法计算的探明储量,其落实程度备受关注.储量落实程度的评价是一个多因素综合评判问题,而且这些因素还分属不同层次.模糊综合评判是应用较为广泛的模糊数学方法之一.该方法建立的综合评判涉及三个要素,即因素集、评判集和单因素评判,由此所构造的模糊评判关系是模糊综合评判的基础.本文用该方法对张东构造沙二段油藏的探明储量落实程度作了尝试性评价.     

堆底熔穿事故的场外后果及其对策

在压水堆核电站应急计划和应急响应中，堆底熔穿事故占有重要位置。本文用法国核电厂事故源项Ｓ３（对应于堆底熔穿事故）计算了在典型气象条件下的场外放射学的后果，根据一般防护决策原则和我国颁布的干预水平，提出了保护公众的应急防护措施的建议。     

用面向对象编程实现问题求解自动化

本文从面向对象方法基本原理和面向对象编程的技术特征出发，阐明所建应用软件系统中的对象，类及相应类层次结构和类组合结构，使系统具有很强的表现真空世界复杂系统结构的能力，系统通过消息传递在程序执行中实现对操作的调用机制，使之在面向用户问题选择和执行求解策略方面有很强的适应性，这一点对于强调人－机交互和解题协作的智能决策支持系统设计至关重要，文章给出了用Ｃ＋＋实现的模型对象系统及消息传递机制。     

Transit smart card data mining for passenger origin information extraction

The automated fare collection(AFC) system,also known as the transit smart card(SC) system,has gained more and more popularity among transit agencies worldwide.Compared with the conventional manual fare collection system,an AFC system has its inherent advantages in low labor cost and high efficiency for fare collection and transaction data archival.Although it is possible to collect highly valuable data from transit SC transactions,substantial efforts and methodologies are needed for extracting such data because most AFC systems are not initially designed for data collection.This is true especially for the Beijing AFC system,where a passenger’s boarding stop(origin) on a flat-rate bus is not recorded on the check-in scan.To extract passengers’ origin data from recorded SC transaction information,a Markov chain based Bayesian decision tree algorithm is developed in this study.Using the time invariance property of the Markov chain,the algorithm is further optimized and simplified to have a linear computational complexity.This algorithm is verified with transit vehicles equipped with global positioning system(GPS) data loggers.Our verification results demonstrated that the proposed algorithm is effective in extracting transit passengers’ origin information from SC transactions with a relatively high accuracy.Such transit origin data are highly valuable for transit system planning and route optimization.     

船舶抗沉辅助决策系统的研究与设计

抗沉辅助决策系统是船舶常规和紧急状态下进行损管行动的关键性依据。目前抗沉计算分析系统,可对船舶基础状态、浮稳性等信息进行管理,并进行仿真和辅助决策,但大多存在算法效率低,决策生成速度和精度不足、人机交互接口脱离实际等问题。本文从船舶抗沉操作的实际执行过程出发,制定抗沉算法,根据实时装载和破损情况,对液舱采用注入水或排水的损管操作,生成调整措施,使船舶恢复良好的浮态与稳性。系统基于面向对象过程控制平台设计和实现,仿真结果验证了系统的有效性和高效性。     

考虑多样化需求的诊疗设备维护和患者调度联合优化研究

针对诊疗设备维护和住院患者候诊难以有效协调的问题,建立了联合优化模型。首先,假设设备具有离散的劣化状态,将设备的劣化过程建模为连续时间马尔可夫链;其次,考虑到患者对诊疗设备的不同功能频率需求,以及不同劣化状态对患者治疗时间、费用的影响,以患者就诊顺序、检查策略、修复策略为决策变量建立了设备维护和患者调度模型;最后,采用改进后的非支配排序遗传算法对多目标问题进行了求解。实验结果验证了设备维护与患者调度联合优化模型的有效性。改进后的算法提高了整体和局部的搜索能力,且具有鲁棒性。     

基于IFMOP的玉环市区域水资源配置

引入无概率分布的区间数表示不确定性影响范围,选取经济效益、供需水效率和COD排放量分别构建经济目标、社会目标和环境目标优化函数,构建了玉环市不确定性模糊多目标规划(IFMOP)水资源配置模型,并计算了不确定性解集。结果表明:IFMOP配置计算成果可实现玉环市区域水量水质协调高效利用和社会-经济-生态系统均衡发展;在75%来水保证率下各行业配置总水量不确定性范围为14 375万~15 154万m³。IFMOP水资源配置模型在信息不充分的条件下能够全面使用各类数据信息进行水资源配置,更好地满足水资源在各行业配置过程中的动态管理需求。     

Probabilistic heuristic estimates

Though they constitute the major knowledge source in problem-solving systems, no unified theory of heuristics has emerged. Pearl [15] defines heuristics as criteria, methods, or principles for deciding which among several alternative courses of action promises to be the most effective in order to achieve some goal. The absence of a more precise definition has impeded our efforts to understand, utilize, and discover heuristics. Another consequence is that problem-solving techniques which rely on heuristic knowledge cannot be relied upon to act rationally — in the sense of the normative theory of rationality.To provide a sound basis for BPS, the Bayesian Problem-Solver, we have developed a simple formal theory of heuristics, which is general enough to subsume traditional heuristic functions as well as other forms of problem-solving knowledge, and to straddle disparate problem domains. Probabilistic heuristic estimates represent a probabilistic association of sensations with prior experience — specifically, a mapping from observations directly to subjective probabilities which enables the use of theoretically principled mechanisms for coherent inference and decision making during problem-solving. This paper discusses some of the implications of this theory, and describes its successful application in BPS.This research was made possible by support from Heuristicrats, the National Aeronautics and Space Administration, and the Rand Corporation.     

An intelligent hybrid approach for hepatitis disease diagnosis: Combining enhanced k‐means clustering and improved ensemble learning

In real world, the automatic detection of liver disease is a challenging problem among medical practitioners. The intent of this work is to propose an intelligent hybrid approach for the diagnosis of hepatitis disease. The diagnosis is performed with the combination of k‐means clustering and improved ensemble‐driven learning. To avoid clinical experience and to reduce the evaluation time, ensemble learning is deployed, which constructs a set of hypotheses by using multiple learners to solve a liver disease problem. The performance analysis of the proposed integrated hybrid system is compared in terms of accuracy, true positive rate, precision, f‐measure, kappa statistic, mean absolute error, and root mean squared error. Simulation results showed that the enhanced k‐means clustering and improved ensemble learning with enhanced adaptive boosting, bagged decision tree, and J48 decision tree‐based intelligent hybrid approach achieved better prediction outcomes than other existing individual and integrated methods.