An Improved Algorithm for Interactive Dynamic Influence Diagrams

交互式动态影响图(I-dIdS)是基于概率图形理论的多智能体动态交互决策的图模型.为缓解该模型状态空间随时间片增加呈指数级增长的趋势,文中基于行为等价的基本思想压缩状态空间,提出构建EPSIlOn行为等价类的方法:利用有向无环图表示其它AgEnT可能的信度和行为,把信度在空间上接近的模型聚为一类,实现自顶向下合并行为等价模型.该过程避免求解状态空间中的所有候选模型,节省了存储空间和计算时间.模型实例上的仿真结果显示了该算法的有效性.Interactive Dynamic Influence Diagrams(I-DIDs), as graphic models based on probabilistic graphical theory, are proposed to represent, the sequential decision-making problem over multiple time steps in the presence of other interacting agents.The algorithms for solving I-DIDs are haunted by the challenge of an exponentially growing space of candidate models ascribed to other agents over time.In this paper, in order to reduce the candidate model space according the behaviorally equivalent theory, a more efficient way to construct Epsilon behavior equivalence classes is discussed that using belief-behavior graph (BBG).A method of solving I-DIDs approximately is presented, which avoids solving all candidate models by clustering models with beliefs that are spatially close and selecting a representative one from each cluster.The simulation results show the validity of the improved algorithm.国家自然科学基金资助项目(60975052

Modeling and optimization of RGV system based on improved QPSO

为提高自动小车存取系统中轨道导引小车系统的出入库作业效率,提出了一种基于改进量子微粒群的优化方法。分析了轨道导引小车系统出入库作业任务队列特征,建立了数学模型。在此基础上利用量子微粒群算法进行优化调度,并在该算法中引入高斯变异算子,克服了其容易陷入局部最优的缺点。通过仿真实验表明了方法的可行性和有效性。To improve the performance of Rail-Guided Vehicles System(RGVS) in Automatic Vehicle Storage and Retrieval Systems(AVS/RS),an optimization method based on improved Quantum Particle Swarm Optimization(QPSO) was proposed.Firstly,sequencing characteristics of tasks in RGVS were analyzed,and a mathematical model was established.Then,a QPSO algorithm was proposed to solve the scheduling problem.Meanwhile,the Gaussian mutation operator was introduced into this algorithm to overcome its shortcoming of falling into local convergence.Finally,feasibility and effectiveness of the presented method was shown by experimental results.国家自然科学基金资助项目(60975052);厦门大学国家“211三期工程建设”资助项目(0630-E62000)---

熊胆对二乙基亚硝胺诱发大鼠肝癌的影响

探讨熊胆对二乙基亚硝胺(diethylnitrosamine,DEN)诱发大鼠肝癌的影响。将大鼠随机分为正常组、模型组及熊胆两个剂量组(200、400 mg.kg 1,灌胃18周,每周5次)。DEN诱发肝癌,用熊胆粉干预。实验观察了体重等一般情况,检测肝功能,HE染色观察肝组织病理改变,免疫组化检测肝组织中增殖细胞核抗原(PCNA)和平滑肌动蛋白(α-SMA)。结果显示:4周后正常组体重高于其他各组(P<0.01),熊胆两组均高于模型组(P<0.05);除正常组外,其余各组谷丙转氨酶、总胆红素显著升高,但熊胆两组显著低于模型组;模型组呈典型肝硬化、肝癌改变,熊胆两组肝硬化程度较轻、癌组织分化较好;模型组高表达PCNA和α-SMA,熊胆两组较模型组弱,α-SMA减少具有显著意义。实验提示:熊胆在一定程度上抑制了DEN诱发肝癌的进程,可能与抑制肝星状细胞活化并减轻肝损害、肝硬化有关

Research on Decision Making for Autonomous Vehicle Storage and Retrieval Systems under Dynamical and Uncertainty Environment

自动小车存取系统(AutonomousVehicleStorageandRetrievalSystems，AVS/Rs)是一种集自动化、集成化、信息化、智能化为一体的新型自动存取系统(AutomatedStorageandRetrievalSystem，AS/Rs)。目前对于AVS/Rs系统的研究大都建立在假设系统运行环境固定的基础上，主要利用排队论以及Petri网等动态建模方法进行分析与仿真，而忽略了系统环境的动态不确定性。因此本文在研究了多Agent序贯决策理论的基础上，以AVS/Rs系统为研究对象，提出应用多Agent交互式动态影响图(InteractiveDynamicInfluenc...Autonomous vehicle storage and retrieval systems (AVS/Rs) represent a relatively new style of automated storage and retrieval systems (AS/Rs)combined with more automation,integration, information and intelligence. Most previous study on AVS/Rs bases on the assumption that the system operates in a fixed and static environment by using the dynamic modeling method, such as queuing theory and Petri ne...学位：工学博士院系专业：信息科学与技术学院_系统工程学号：2322009015371

Approximate solving-solution of interactive dynamic influence diagrams

提出一种基于行为等价原理分段处理交互式动态影响图(I-dId)的近似算法:先将底层I-dId模型分解成包含若干时间片的子片段,求解首片段,获得各模型的策略树,并依行为等价原理合并策略树,形成策略图,其结果作为下一片段的初始模型,再进行求解.重复这个过程,直到最后片段结束,获得完全策略图,用来指导AgEnT是否进行模型更新.最后,针对多AgEnT老虎问题进行试验和算法比较,试验结果从模型解的质量和模型空间大小2个方面验证了所提算法的有效性.An approximate solution was presented based on the principle of behaviorally equivalent for interactive dynamic influence diagrams(I-DID).The amount of calculation was reduced by decomposing the I-DID model into more than one fragment and compressing the space of other agents′ candidate models.First,the model of I-DID or DID at bottom level was split into sub-segments that include a number of time slices,then the solution of the first segment for the initial models was obtained,and the policy graph could be gotten by merging policy trees based on the principle of behaviorally equivalent.Continue to solve the next I-DID or DID,the output of the previous fragment was regarded as the input for the subsequent fragment,until the last fragment,and the whole policy graph was available,which identifying whether the model needed to be updated.Experiment results,which on the quality of solution and the magnitude of model space for multi-agent tiger problem,show the validity of the approximate method.国家自然科学基金资助项目(60975052

Progress for Application and Preparation of Interactive Dynamic Influence Diagrams

交互式动态影响图(I-dIdS)是不确定环境下多AgEnT序贯决策的图模型.该模型突破传统基于公共知识的纳什均衡点的假设,使得该模型具备解决大规模动态决策问题的能力,具有更为广泛的应用前景.在总结、分析动态影响图研究成果的基础上,针对多AgEnT的I-dIdS状态空间随时间片的增长呈指数级增长问题,提出了基于行为等价和动作等价的两种新型算法并进行算法分析;此外,探讨应用I-dIdS模型解决自动小车存取系统中的路径规划、避免环路死锁问题,扩展了I-dIdS的应用范围.Interactive dynamic influence diagrams(I-DIDs) are graphic models that offer a clear representation for multi-agent decision-making under uncertainty.The solution of I-DIDs is different from the traditional multi-agent solution based on game theory,it breaks through the assumption of traditional Nash equilibrium based on public knowledge,which makes I-DIDs with wide range applications.First of all,this paper discusses the research progress of DID;And then it propose two new algorithms based on behavioral equivalence and actions equivalence to solve the problems of that the state spaces of I-DIDs growth over time exponentially;Moreover,this paper attempts to use the I-DIDs model to solve the problems of path planning and loop-deadlock controlling in automatic vehicle storage and retrieval system,it extends the application scope of I-DIDs.国家自然科学基金项目(60975052

2005～2014年CERN地下水位数据集

地下水是一个地区重要的自然资源,地下水位数据可为研究地下水的长期变化提供重要的参考资料。本数据集收集整理了中国生态系统研究网络(CERN)34个台站采用人工或自动记录方法观测的2005～2014年地下水位深度数据。重新整理后的数据格式更加规范,质量也有所提高。此外,为了便于用户了解台站地下水位的概况(如平均深度及其变化),我们还计算了各台站地下水位深度的平均值及其标准差

2004–2016年中国生态系统研究网络水体酸碱度和总溶解性固体数据集

水体的酸碱度(pH)和总溶解性固体(TDS)是中国生态系统研究网络(CERN)的重要监测指标,可为生态系统水体质量长期变化研究提供重要数据。降水pH可以表征其是否为酸沉降,地表水和地下水的pH则关系到水质是否对植物生长和动物饮用存在危害等。TDS是表征水体溶解性固体总含量的指标,同样影响到植物根系的水分吸收和动物的生存分布。本数据集收集整理了CERN农田、森林、荒漠、草原、沼泽5种典型生态系统34个生态站2004–2016年降水、地表水、地下水pH和TDS数据。本数据集可为分析降水、地表水、地下水的酸碱度和TDS的时间变化和空间格局提供数据,可为研究中国典型生态系统水质酸碱度和盐碱化的长期变化提供数据支撑