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.     

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.     

The garbling decision maker: A model of bounded rationality

A model is constructed of a garbling decision maker-one who randomizes his decision in response to the quantity of information he uses to make his decision. This model is supposed to capture the effects of bounded rationality on decision making and is imbedded in a team problem to model the organization designer's problem. A few results are derived from the linear-quadratic-Gaussian static team formulation developed here.     

Capturing the high-risk environment of the transition economy in Bulgaria—a simulation-based DSS

Eastern European countries have undergone a transition from centralized economic planning to more open economic systems. Hard data based upon past experience are inappropriate for decision making in this radically changed environment. A team of Bulgarian and U.S. researchers utilized system dynamics simulation to model the microeconomic environment of a Bulgarian winery expanding into regional and international markets. Expert opinion was provided for both micro- and macroeconomic factors. Given the uncertainty of the data and the ambiguity in the experts' opinions, fuzzy logic was used to model the transitional economic firm's decision making.     

On the complexity of decentralized decision making and detection problems

We study the computational complexity of the discrete versions of some simple but basic decentralized decision problems. These problems are variations of the classical "team decision problem" and include the problem of decentralized detection whereby a central processor is to select one of two hypotheses, based on l-bit messages from two noncommunicating sensors. Our results point to the inherent difficulty of decentralized decision making and suggest that optimality may be an elusive goal.     

Exploring team sensemaking in air traffic control (ATC): insights from a field study in low visibility operations

Sensemaking helps teams coordinate their efforts to understand and anticipate events in uncertain situations. While it is recognized that breakdowns in team sensemaking can lead to incidents, next generation air traffic management (ATM) projects have not paid serious attention to this research topic. This article presents findings from an exploratory field study of team sensemaking in air traffic control for low visibility operations. The study uses the critical decision method and the data/frame model of sensemaking (Klein et al. in Expertise out of context: proceedings of the 6th international conference on naturalistic decision making. Erlbaum, Mahwah, 2007) as a theoretical basis for examining Tower and Approach operations that will be transformed by next generation ATM projects. The findings concern the elicitation of explanatory frameworks for making sense of low visibility operations, the identification of domain-specific strategies that shape sensemaking and the presentation of emergent requirements for team sensemaking. Implications are made for embedding operational experience into future ATM systems to improve collaborative decision making.     

Microcomputer based multiple criteria decision support system for strategic planning

There are two major approaches currently used for developing Decision support Systems (DSS) for strategic planning, especially in the objective formulation stage. Several mathematical models have been developed to abstract the decision situation. However, they do not take into account either behavioral aspects of decision making or the presence of multiple and conflicting objectives. A second approach is to consider the several qualitative factors that go into decision making; such considerations are normally situation-dependent and hence it is difficult to provide a system for general managerial situations.The Multiple Criteria Decision Making (MCDM) approach combines the advantages of both the approaches and, therefore, is an excellent alternative for designing DSS. This paper develops an MCDM approach to strategic planning. The model is applied to such a problem in a simulated environment and the problem is solved interactively. Our experience shows that the proposed methodology is a viable approach for solving practical decision problems in strategic planning.     

基于认知主导决策模型的陆军合同战术指挥员Agent建模策略

作战模拟系统中陆军指挥员模型建模十分重要,其决策不确定性强,功能实现复杂。针对这一问题,提出了基于认知主导决策模型的建模思想,并针对其计算机实现面临的决策者经验态势结构化描述、经验态势与当前态势自动匹配以及指挥员行为建模等重点难点问题提出了解决策略。     

Symbiotic organisms search and two solution representations for solving the capacitated vehicle routing problem

This paper presents the symbiotic organisms search (SOS) heuristic for solving the capacitated vehicle routing problem (CVRP), which is a well-known discrete optimization problem. The objective of CVRP is to decide the routes for a set of vehicles to serve a set of demand points while minimizing the total routing cost. SOS is a simple and powerful metaheuristic that simulates the symbiotic interaction strategies adopted by an organism for surviving in an ecosystem. As SOS is originally developed for solving continuous optimization problems, we therefore apply two solution representations, SR-1 and SR-2, to transform SOS into an applicable solution approach for CVRP and then apply a local search strategy to improve the solution quality of SOS. The original SOS uses three interaction strategies, mutualism, commensalism, and parasitism, to improve a candidate solution. In this improved version, we propose two new interaction strategies, namely competition and amensalism. We develop six versions of SOS for solving CVRP. The first version, SOS_Canonical, utilizes a commonly used continuous to discrete solution representation transformation procedure. The second version is an improvement of canonical SOS with a local search strategy, denoted as SOS_Basic. The third and fourth versions use SR-1 and SR-2 with a local search strategy, denoted as SOS_SR-1 and SOS_SR-2. The fifth and sixth versions, denoted as ISOS_SR-1 and ISOS_SR-2, improve the implementation of SOS_SR-1 and SOS_SR-2 by adding the newly proposed competition and amensalism interaction strategies. The performances of SOS_Canonical, SOS_Basic, SOS_SR-1, and SOS_SR-2 are evaluated on two sets of benchmark problems. First, the results of the four versions of SOS are compared, showing that the preferable result was obtained from SOS_SR-1 and SOS_SR-2. The performances of SOS_SR-1, SOS_SR-2, ISOS_SR-1, and ISOS_SR-2 are then compared, presenting that ISOS_SR-1 and ISOS_SR-2 offer a better performance. Next, the ISOS_SR-1 and ISOS_SR-2 results are compared to the best-known solutions. The results show that ISOS_SR-1 and ISOS_SR-2 produce good VRP solutions under a reasonable computational time, indicating that each of them is a good alternative algorithm for solving the capacitated vehicle routing problem.     

Embedding robotic surgery into routine practice and impacts on communication and decision making: a review of the experience of surgical teams

While an increasing number of healthcare providers are purchasing surgical robots because of anticipated improvements in patient outcomes, their implementation into practice is highly variable. In robotic surgery, the surgeon is physically separated from the patient and the rest of the team with the potential to impact communication and decision making in the operating theatre and subsequently patient safety. Drawing on the approach of realist evaluation, in this article we review reports of the experience of surgical teams that have introduced robotic surgery to identify how and in what contexts robotic surgery is successfully integrated into practice and how and in what contexts it affects communication and decision making. Our analysis indicates that, while robotic surgery might bring about a number of benefits, it also creates new challenges. Robotic surgery is associated with increased operation duration, which has implications for patient safety, but strategies to reduce it can be effective with appropriate support from hospital administration and nursing management. The separation of the surgeon from the team can compromise communication but may be overcome through use of standardised communication. While surgeon situation awareness may be affected by the separation, the ergonomic benefits of robotic surgery may reduce stress and tiredness and enhance surgeon decision making. Our review adds to the existing literature by revealing strategies to support the introduction of robotic surgery and contextual factors that need to be in place for these to be effective.     

Training teams for emergency management

Emergency management (EM), the decision making involved in directing the relief operation after a disaster or otherwise catastrophic accident is an issue of great public and private concern because of the high stakes involved. Due to the nature of emergencies, and especially mass emergencies, EM teams are faced with decision making in stressful situations, information ambiguity and overload, and a significant level of uncertainty, whereby non-routine problem solving of a knowledge-based nature is required. An important characteristic of EM is that it is a team of teams; multiple teams that come from different organizations, with different organizational goals and different organizational cultures, that work together to minimize the negative effects of the emergency. As a consequence, EM requires good coordination and communication not just within, but also among the various teams involved. Coordination among teams should, therefore, be a key focus for training. This paper describes EM within the context of naturalistic decision making, and proposes a framework, an event-based approach to training, together with a number of team training strategies that may be applicable to EM. A brief assessment of available training technologies and recommendations for future research are discussed.     

Unanimously acceptable agreements for negotiation teams in unpredictable domains

A negotiation team is a set of agents with common and possibly also conflicting preferences that forms one of the parties of a negotiation. A negotiation team is involved in two decision making processes simultaneously, a negotiation with the opponents, and an intra-team process to decide on the moves to make in the negotiation. This article focuses on negotiation team decision making for circumstances that require unanimity of team decisions. Existing agent-based approaches only guarantee unanimity in teams negotiating in domains exclusively composed of predictable and compatible issues. This article presents a model for negotiation teams that guarantees unanimous team decisions in domains consisting of predictable and compatible, and alsounpredictable issues. Moreover, the article explores the influence of using opponent, and team member models in the proposing strategies that team members use. Experimental results show that the team benefits if team members employ Bayesian learning to model their teammates’ preferences.     

小组软件过程仿真模型及其算法

论文建立了一种小组软件过程(TeamSoftwareProcess)的仿真模型,它是根据TSP的特点,将离散和连续建模思想结合形成的一种混合模型。并根据TSP的多阶段性质,将动态规划算法应用到模型中,在TSP各个阶段最佳地配置人员、资金和时间三种资源,以取得软件质量最优化。     

Ultrafuzzy team decisions in a dynamic environment

In this paper, the concept of fuzzy sets of type 2 is introduced and the team decision processes which contain ultrafuzzy states, ultrafuzzy information structures, ultrafuzzy information signals, ultrafuzzy decision rules, and ultrafuzzy actions are formulated. After some definitions of fuzzy relations of type 2, fuzzy sets of type 2 induced by mappings, composition, Cartesian product, ultrafuzzy mappings, and ultrafuzzy functions, a model of ultrafuzzy team decision in a dynamic environment is proposed. The team decision problem under ultrafuzzy constraints is dealt with. Some results on fuzzy sets of type 2 induced by mappings are given.     

Ontology development for run-time safety management methodology in Smart Work Environments using ambient knowledge

This paper presents the development of a decision support system for run-time safety management in Smart Work Environments (SWEs). Our approach consists of four main phases: (i) definition of the basic steps of a methodology for run-time safety management; (ii) development of an ontological knowledge-base of safety in work environments; (iii) definition of constraints on the ontology based on organizations’ safety protocols; (iv) communication of relevant information to each actor in the safety management team. We propose a generic ontological model of safety expertise, based on Occupational Safety and Health Regulations (OSHA), that is employed as Knowledge required in our safety management methodology based on the MAPE-K (Monitor–Analyze–Plan–Execute and Knowledge) loop. We present the RAMIRES (Risk-Adaptive Management in Resilient Environments with Security) tool, implementing this methodology. RAMIRES is developed as a dashboard, supporting the safety management team in understanding the risk and its consequences, and to support decision making in risk treatment. RAMIRES interacts with the SWE and the safety management team (actors) in order to: (i) communicate the risks and preventive strategies to actors; (ii) obtain more data about the observed areas to understand the risk and its consequences; and (iii) execute the automatic preventive strategies and support actors in the execution of human-operated preventive strategies. In this paper, we show the details on concepts designed in the safety ontology and illustrate how these concepts can be extended to provide an abstract model of a specific use case. Furthermore, we propose the definition of constraints on the ontology using logic-based rules. Finally, we discuss the advantages and limitations of the proposed methodology regarding the resilience of the environment.     

Rule sets based bilevel decision model and algorithm

Bilevel decision addresses the problem in which two levels of decision makers, each tries to optimize their individual objectives under certain constraints, act and react in an uncooperative, sequential manner. As bilevel decision making often involves many uncertain factors in real world problems, it is hard to formulate the objective functions and constraints of the leader and the follower in modelling a real bilevel decision problem. This study explores a new approach that uses rule sets to formulate a bilevel decision problem. It first develops related theories to prove the feasibility to model a bilevel decision problem by rule sets. It then proposes an algorithm to describe the modelling process. A case study is discussed to illustrate the functions and effectiveness of the proposed rule sets based bilevel decision modelling algorithm.     

基于区间粗糙数的多属性决策方法及应用

针对传统多属性决策模型中大量指标的复杂性和模糊性导致实际决策结果不准确的问题,提出了一种基于区间粗糙数的改进DEMATEL-VIKOR多属性优化决策模型。首先,在区间粗糙数的基础上对传统的DEMATEL方法和VIKOR方法分别进行改进;其次将一种新的归一化加权几何Bonferroni平均算子运用在改进的DEMATEL-VIKOR混合模型中,根据量化结果对各准则进行影响因素分析进而计算折中的可行解;最后,分别从敏感性分析、理论分析及方法比较三种角度对备选方案进行排序,从而得到最优备选方案,应用城市滨水区景观后期综合评价的算例结果表明,该改进算法在不确定和不完备信息环境下的决策结果更具可行性和有效性。     

Optimization of decision networks in structured task environments

This paper considers the problem of determining the optimal distributed decision strategy for a team of decision-makers (DMs) arranged in an arbitrary acyclic organizational structure and controlling a complex structured process. Each DM has access to uncertain and partial information about the task environment and can control only a portion of it. We present an influence diagram model of the joint task-organization system and formulate the optimal team strategy in terms of a set of coupled hypothesis testing tasks at DMs. Specifically, we show that the scope of a local decision task is determined by the interaction of the task structure (what can be measured) and of the information access structure of the organization (who can measure what); while the control structure (who can influence what event) has an impact on the locally perceived costs associated with the decision options available. Theoretical results are illustrated via a numerical example, and connections to existing decision models are discussed     

基于改进Q-学习算法的多阶段群体决策模型

多阶段群体决策问题是一类典型的动态群体决策问题,主要针对离散的确定状态下的最优群体决策问题求解.但由于现实环境面临的大部分是不确定状态空间,甚至是未知环境空间(例如状态转移概率矩阵完全未知),为了寻求具有较高共识度的多阶段群体最优策略,决策者需要通过对环境的动态交互来获得进一步的信息.针对该问题,利用强化学习技术,提出一种求解多阶段群体决策的最优决策算法,以解决在不确定状态空间下的多阶段群体决策问题.结合强化学习中的Q-学习算法,建立多阶段群体决策Q-学习基本算法模型,并改进该算法的迭代过程,从中学习得到群体最优策略.同时证明基于Q-学习得到的多阶段群体最优策略也是群体共识度最高的策略.最后,通过一个计算实例说明算法的合理性及可行性.