A fuzzy logic-based computational recognition-primed decision model

The recognition-primed decision (RPD) model is a primary naturalistic decision-making approach which seeks to explicitly recognize how human decision makers handle complex tasks and environment based on their experience. Motivated by the need for quantitative computer modeling and simulation of human decision processes in various application domains, including medicine, we have developed a general-purpose computational fuzzy RPD model that utilizes fuzzy sets, fuzzy rules, and fuzzy reasoning to represent, interpret, and compute imprecise and subjective information in every aspect of the model. Experiences acquired by solicitation with experts are stored in experience knowledge bases. New local and global similarity measures have been developed to identify the experience that is most applicable to the current situation in a specific decision-making context. Furthermore, an action evaluation strategy has been developed to select the workable course of action. The proposed fuzzy RPD model has been preliminarily validated by using it to calculate the extent of causality between a drug (Cisapride, withdrawn by the FDA from the market in 2000) and some of its adverse effects for 100 hypothetical patients. The simulated patients were created based on the profiles of over 1000 actual patients treated with the drug at our medical center before its withdrawal. The model validity was demonstrated by comparing the decisions made by the proposed model and those by two independent internists. The levels of agreement were established by the weighted Kappa statistic and the results suggested good to excellent agreement.     

A multi-agent model system for land-use change simulation

This paper presents a multi-agent model system to characterize land-use change dynamics. The replicable parameterization process should be useful to the development of simulation frameworks, important to environmental policy makers to analyze different scenarios during decision making process. The methodological two-fold approach intends to form a solid backbone based on: (i) the systematic and structured empirical characterization of the model; and (ii) the conceptual structure definition according to the agent-based model documentation protocol – Overview, Design concepts and Details. A multi-agent system for land-use change simulation was developed to validate the model, which is illustrated with a case study of the Brazilian Cerrado using LANDSAT ETM images. The simulation results prove the model importance with a figure of merit greater than 50%, what means the amount of correctly predicted change is larger than the sum of any type of error. The results are very good compared with nine popular peer-reviewed land change models.     

基于MapX的泄漏事故救援决策系统

详细介绍了以地理信息系统(GIS)为基础的危险化学品泄漏事故的仿真及救援决策支持系统的设计结构与应用。本系统以MapX为开发工具,VB．NET为开发平台,实现了不同环境条件下的危险化学品泄漏扩散事故的仿真及其后果预测。该系统同时还具有计算不同应急救援部门的最短救援路径和查询应急资源信息的功能,为事故状态下的救援决策和应急管理提供了有力的支持。     

A computational model for recognition of multifont word images

A computational model for the recognition of multifont machine-printed word images of highly variable quality is given. The model integrates three word-recognition algorithms, each of which utilizes a different form of shape and context information. The approaches are character-recognition-based, segmentation-based, and word-shape-analysis based. The model overcomes limitations of previous solutions that focus on isolated characters. In an experiment using a lexicon of 33,850 words and a test set of 1,671 highly variable word images, the algorithm achieved a correct rate of 89% at the top choice and 95% in the top ten choices.     

A multi-agent model for decision making on environmental regulation in urban agglomeration

The Journal of Supercomputing - The incomplete enforcement of environmental regulation by local governments will lead to environmental degradation. While the strategy selection for local...     

Macro-Dataflow Computational Model and Its Simulation

This paper discusses the relationship between parallelism granularity and system overhead of dataflow computer systems,and indicates that a trade-off between them should be determined to obtain optimal efficiency of the overall system.On the basis of this discussion,a macro-dataflow computational model is established to exploit the task-level parallelism.Working as a macro-dataflow computer,an Experimental Distributed Dataflow Simulation System(EDDSS)is developed to examine the effectiveness of the macro-dataflow computational model.     

多代理仿真中自适应模型融合研究

多代理仿真是计算机仿真迈向智能化的核心技术,仿真模型的适配是确保仿真有效性、科学性以及实时性的基础,如何确保在仿真过程中数据的解算与仿真模型的无缝融合,是解决各类仿真环境与仿真应用系统集成的关键问题之一。提出了一种自适应的模型融合机制,利用多代理自身的灵活特性,采用基于关联度的模型关联机制,为分布式仿真研究提供智能化、自动化的模型适配手段。通过与某仿真引擎自有的模型管理机制进行性能对比,实验结果表明基于关联度的模型调度机制优于分布式仿真引擎的模型管理策略。     

A feature binding computational model for multi-class object categorization and recognition

We propose a feature binding computational model based on the cognitive research findings. Feature integration theory is widely approved on the principles of the binding problem, which supplies the roadmap for our computational model. We construct the learning procedure to acquire necessary pre-knowledge for the recognition network on reasonable hypothesis–maximum entropy. With the recognition network, we bind the low-level image features with the high-level knowledge. Fundamental concepts and principles of conditional random fields are employed to model the feature binding process. We apply our model to current challenging problems, multi-label image classification and object recognition, and evaluate it on the benchmark image databases to demonstrate that our model is competitive to the state-of-the-art method.     

A realistic benchmark for visual indoor place recognition

An important competence for a mobile robot system is the ability to localize and perform context interpretation. This is required to perform basic navigation and to facilitate local specific services. Recent advances in vision have made this modality a viable alternative to the traditional range sensors, and visual place recognition algorithms emerged as a useful and widely applied tool for obtaining information about robot’s position. Several place recognition methods have been proposed using vision alone or combined with sonar and/or laser. This research calls for standard benchmark datasets for development, evaluation and comparison of solutions. To this end, this paper presents two carefully designed and annotated image databases augmented with an experimental procedure and extensive baseline evaluation. The databases were gathered in an uncontrolled indoor office environment using two mobile robots and a standard camera. The acquisition spanned across a time range of several months and different illumination and weather conditions. Thus, the databases are very well suited for evaluating the robustness of algorithms with respect to a broad range of variations, often occurring in real-world settings. We thoroughly assessed the databases with a purely appearance-based place recognition method based on support vector machines and two types of rich visual features (global and local).     

基于Multi-Agent的复杂网络疾病传播仿真模型

通过复杂网络上的多智能体的建模与仿真,对疾病的传播过程进行了模拟,研究了疾病传播在不同条件下的传播特征。通过设定采取4个仿真过程,得出疾病的传播即使是以极小的概率治愈也能使系统得到良性发展,验证了前期疫苗式免疫的重要性等结论。     

群决策监督模糊模式识别模型

从模糊模式识别概念出发,以群决策成员的经验、偏好为监督,建立一种全体决策成员对所有方案集的全体级别加权广义欧氏权距离平方和最小为目标函数的非线性规划模型.利用该模型可以在确定目标指标和决策人权重的同时确定决策方案相对优属度.为群决策支持系统研究提供了一种新的途径.     

A distributed adverse drug reaction detection system using intelligent agents with a fuzzy recognition‐primed decision model

Discovering unknown adverse drug reactions (ADRs) in postmarketing surveillance as early as possible is highly desirable. Nevertheless, current postmarketing surveillance methods largely rely on spontaneous reports that suffer from serious underreporting, latency, and inconsistent reporting. Thus these methods are not ideal for rapidly identifying rare ADRs. The multiagent systems paradigm is an emerging and effective approach to tackling distributed problems, especially when data sources and knowledge are geographically located in different places and coordination and collaboration are necessary for decision making. In this article, we propose an active, multiagent framework for early detection of ADRs by utilizing electronic patient data distributed across many different sources and locations. In this framework, intelligent agents assist a team of experts based on the well‐known human decision‐making model called Recognition‐Primed Decision (RPD). We generalize the RPD model to a fuzzy RPD model and utilize fuzzy logic technology to not only represent, interpret, and compute imprecise and subjective cues that are commonly encountered in the ADR problem but also to retrieve prior experiences by evaluating the extent of matching between the current situation and a past experience. We describe our preliminary multiagent system design and illustrate its potential benefits for assisting expert teams in early detection of previously unknown ADRs. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 827–845, 2007.     

Team-Soar: a computational model for multilevel decision making

Computational models which symbolically represent the abstraction of reality on computers, have won popularity as research tools in organizational studies. However, very few, if any computational models have been used to test theories in the same ways that human experiments generally do. The author introduces a simulation experiment using a computational model of team called "Team-Soar," which mimics a human team experiment that was performed to test a theory of team decision making. The results support the major propositions of the multilevel theory in the same fashion as the ones of the human team experiment. The simulation experiment displays the Team-Soar model's effectiveness as a theory prover     

A simulation based decision model for a fish processing facility

The single, most important production method in the fish processing industry is quick-freezing. The quick-freezing process in a typical fish processing plant is described. The network simulation model is then developed using the imulation anguage for lternative odeling (SLAM). The results from the model include utilization of machines and workers, in-process inventory levels, and throughput times. Sensitivity analysis is performed on some of the major factors that can affect the efficiency of the system. A front-end interface to the simulation model is developed which facilitates the user in entering input parameters to the model without the need to learn the simulation language. The model provides a methodology for evaluating alternative production strategies for a fish processing facility.     

A general model for fuzzy decision tree and fuzzy random forest

The problem of risk classification and prediction, an essential research direction, aiming to identify and predict risks for various applications, has been researched in this paper. To identify and predict risks, numerous researchers build models on discovering hidden information of a label (positive credit or negative credit). Fuzzy logic is robust in dealing with ambiguous data and, thus, benefits the problem of classification and prediction. However, the way to apply fuzzy logic optimally depends on the characteristics of the data and the objectives, and it is extraordinarily tricky to find such a way. This paper, therefore, proposes a general membership function model for fuzzy sets (GMFMFS) in the fuzzy decision tree and extend it to the fuzzy random forest method. The proposed methods can be applied to identify and predict the credit risks with almost optimal fuzzy sets. In addition, we analyze the feasibility of our GMFMFS and prove our GMFMFS‐based linear membership function can be extended to a nonlinear membership function without a significant increase in computing complex. Our GMFMFS‐based fuzzy decision tree is tested with a real dataset of US credit, Susy dataset of UCI, and synthetic datasets of big data. The results of experiments further demonstrate the effectiveness and potential of our GMFMFS‐based fuzzy decision tree with linear membership function and nonlinear membership function.     

Coalitions in coordinated multi-agent production scheduling: A computational study

This paper studies distributed production scheduling where agents control dispersed information and decentralized decision authority. Using the classical job shop scheduling model, the effects of coalition formation and local communication in an iterative auction are studied. The case is investigated where job agents are allowed to form coalitions, where coalition members share private information and resolve resource conflicts among themselves, while intercoalition communication is limited to bidding. The computational study shows that when the size, type, and timing of the coalitions are properly determined, it is possible to produce a high-quality schedule with a reasonable number of iterations. The results show further improvement in convergence and in solution quality when coalition size and update frequency increase. However, these improvements show diminishing return; thus, it is concluded that a high-quality schedule can be achieved with manageable coalition sizes and a moderate level of information sharing.     

A benchmark for unconstrained online handwritten Uyghur word recognition

International Journal on Document Analysis and Recognition (IJDAR) - Despite some interesting results from different research groups, a public database for Uyghur online handwriting recognition and...     

A general knowledge mediation infrastructure for multi-agent systems

Intelligent decision making needs to be equipped with broader knowledge in order to enhance the decision quality. Knowledge for decision making can be categorized as domain specific and general. Applying domain knowledge in intelligent systems is not new, but applying general knowledge to support business decision making is a possible way to obtain an edge over competitors. For this reason, the paper focuses primarily on designing a general knowledge mediation infrastructure (GKMI) which supports the use of general knowledge from multiple heterogeneous sources, and provides an unified access point for typical multi-agent systems (MAS) to access that knowledge. The finite state automaton (FSA) is used to model and analyze the commonsense inference ability of GKMI. By carrying out two use cases of GKMI for MAS development and operation the effectiveness of this infrastructure is examined.     

A simulation based multi-attribute group decision making technique with decision constraints

The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is a useful technique for solving Multi Attribute Group Decision Making (MAGDM) problems. In MAGDM, the performance scores of the alternatives and the weights of assessment attributes are mostly vague. Therefore, using of deterministic data throughout decision making process may lead to inaccurate results. In order to overcome inherent vagueness and uncertainty, various fuzzy MAGDM techniques were presented in the literature. However, these fuzzy MAGDM techniques are focused on expected and extreme values, which are sometimes insufficient for the precise determination of alternatives’ preference structure. In this paper, in order to eliminate the limitations of deterministic and fuzzy MAGDM methods, we present a probabilistic methodology, which is based on TOPSIS and Monte-Carlo simulation of triangular data. In addition to its straightforward application and thanks to its versatility, simulation enables decision makers to incorporate some decision constraints into decision-making process. Two illustrative examples are also given to show the effectiveness of the proposed methodology. The method is also compared with a fuzzy TOPSIS technique from the literature.