一种混合Agent体系结构及决策算法

提出的混合Agent体系结构包括反应式结构和慎思结构，反应式结构提高Agent的反应性，慎思结构增强Agent的智能性；为了提高智能体决策的速度和智能性。提出一种新颖的Agent决策算法。该文提出的混合Agent体系结构及决策算法已成功地应用到RoboCup仿真机器人系统中，仿真结果说明了该结构和算法的有效性。     

基于模糊神经网络的机器人足球团队决策研究

机器人足球团队决策依据的信息通常是模糊的、不确定的、瞬息万变的,决策系统必须具有自适应学习能力。本文提出一种基于模糊神经网络的机器人足球团队决策模型,用于建立场上关键信息元素和团队决策之间有效的映射关系。初步的仿真实验结果证明了该团队决策模型的正确性和合理性。     

航站楼枢纽行人微观寻径算法仿真研究

为了提高航站楼等交通枢纽内应急预案和疏散措施的科学性和有效性,需要对其内部行人的行为进行建模分析,从而得出行人活动和在枢纽设施内的寻径规律。研究人员采用邻域决策模型对行人行为建模,通过目的地、障碍物和其他行人对行人的交互影响作用来确定行人的轨迹,同时采用样例对该模型进行仿真。仿真实验结果表明,与传统的基于力的模型等微观行人仿真相比,该模型具有非线性的决策特征,从而有更好的微观仿真效果。     

基于复合Agent的RPD决策模型

结合基于认知主导决策(RPD)的海战仿真作战实体决策模型,借鉴复合Agent思想,将RPD模型的不同功能模块实现为简单的子Agent,构造基于复合Agent的RPD决策模型。对模型中各子Agent的功能及经验知识表示方法进行说明,给出子Agent的结构。应用结果表明,相比基于规则的决策模型,该模型能更好地模拟作战实体的决策行为,具有较好的灵活性和可变性。     

基于Q-学习和行为树的CGF空战行为决策

 空战行为决策的智能性是学术界关注的重要问题之一。提出一种基于Q-学习和行为树的CGF空战行为决策方法。通过构建CGF空战行为树模型,实现CGF智能行为;通过在行为树上的Q-学习,使CGF具有不断进化的能力。仿真结果表明,该算法在与传统算法对抗中,性能优势明显且学习能力较强。     

基于模糊推理的驾驶员车速决策行为建模与仿真

为了在微观交通仿真中更真实地模拟驾驶员的车速决策行为,在综合自由行驶和跟驰两种驾驶行为的基础上,采用模糊推理技术进行建模研究。选取车头时距、期望速度差、前车速度差作为模糊推理系统输入变量,加速度为系统输出,结合实际中加减速行为的不对称性及不同行驶状态下主要行驶意图的不同,构造模糊规则,建立基于模糊推理的车速决策模型。利用模糊系统的输入输出曲面进行了模型的校核,并在考虑驾驶员反应时间的基础上进行了仿真实验。仿真结果表明,车辆能够根据前车行驶状态和自身期望车速进行适当的速度决策,验证了所建模型的合理性。     

基于弯曲距离三支决策的时序相似性算法

动态时间弯曲距离算法(DTW)是目前公认的最有效的时间序列相似性计算方法之一,但是较高的时间复杂度一直是其主要缺点。快速弯曲距离算法(FTW)能有效提高DTW的计算速度,但是该算法对不同粒度时间序列剪枝的行为是典型的二支决策,与人类处理不确定问题时普遍采用的三支判断不同。因此,通过将三支决策理论引入到DTW算法的优化工作中,建立了DTW三支决策模型;然后对DTW三支决策模型中的决策阈值α和β进行了基于误识别率的推导,并且给出了具体求解阈值α和β的模拟退火算法;最后基于上述理论提出了基于弯曲距离三支决策的时序相似性算法(3WD-DTW)。通过对比实验表明,与FTW算法相比,3WD-DTW算法在保持较快的计算速度的前提下明显提升了计算准确度,使其接近DTW的水平。     

基于行为均衡分析的多学科协同决策机制设计

针对求解不确定、动态和分布式的多学科设计优化问题时所面临的挑战,提出了一种基于行为均衡分析的多学科协同决策机制。在行为博弈分析框架下,通过采用折衷决策支持问题建立学科设计团队的基本决策模型,利用理性反应集刻画团队之间的交互作用和耦合关系,结合社会偏好理论、认知演算和前景理论来描述团队之间行为均衡的实现过程,该机制为多学科设计博弈的动态演化过程提供了一个有效的建模工具。     

基于态势评估的足球机器人决策模型

为了提高足球机器人中策略执行的有效性和针对性,提出了一种基于赛场正向态势评估和反向执行效果评估的有限状态自动机分层决策模型。该模型将赛场决策分为战术策略和具体任务,通过正向态势评估信息和任务执行效果的反馈信息进行综合量化,基于有限状态自动机建立策略和任务的转换关系,从而做出适合当前态势的战术策略。经仿真实验表明,该决策模型在很大程度上提高了策略决策的有效性和针对性,使得球队的整体攻防能力得到了相应的改善。     

面向企业集成的群体决策支持系统设计与实现

针对企业集成方案决策的需求，基于AHP和模糊决策方法，设计开发了一个基于Web的群体决策支持系统(EI—GDSS)，该系统的主要特点是：决策模型的构建可通过调用参考评价指标体系库中已定义的建造块进行定制，设计了一种模糊评价算法来计算权值向量，将决策过程分为中层评价和高层决策两个阶段，综合考虑了主、客观指标的不同处理方法和个体／群体一致性判别。     

基于multi-agent的综合系统仿真研究

本文针对复杂系统仿真特点，以降低综合仿真系统的复杂度和研发费用并提供最大程度的辅助决策支持为目的，提出了包括仿真建模结构、控制结构和智能模型的基于multi-agent的复杂系统智能仿真方法．基于multi-agent的综合系统智能仿真体系结构引入了一种新的建模与仿真的思想和方法，从而形成了一套独特的仿真建模体系结构，提高了建模与仿真的通用性、可重用性，互操作性以及计算机仿真技术的辅助决策能力．     

Combining rational and biological factors in virtual agent decision making

To enhance believability of virtual agents, this paper presents an agent-based modelling approach for decision making, which integrates rational reasoning based on means-end analysis with personal psychological and biological aspects. The agent model developed is a combination of a BDI-model and a utility-based decision model in the context of specific desires and beliefs. The approach is illustrated by addressing the behaviour of violent criminals, thereby creating a model for virtual criminals. Within a number of simulation experiments, the model has been tested in the context of a street robbery scenario. In addition, a user study has been performed, which confirms the fact that the model enhances believability of virtual agents.     

A hybrid fuzzy logic/constraint satisfaction problem approach to automatic decision making in simulation game models

Possible techniques for representing automatic decision-making behavior approximating human experts in complex simulation model experiments are of interest. Here, fuzzy logic (FL) and constraint satisfaction problem (CSP) methods are applied in a hybrid design of automatic decision making in simulation game models. The decision processes of a military headquarters are used as a model for the FL/CSP decision agents choice of variables and rulebases. The hybrid decision agent design is applied in two different types of simulation games to test the general applicability of the design. The first application is a two-sided zero-sum sequential resource allocation game with imperfect information interpreted as an air campaign game. The second example is a network flow stochastic board game designed to capture important aspects of land manoeuvre operations. The proposed design is shown to perform well also in this complex game with a very large (billionsize) action set. Training of the automatic FL/CSP decision agents against selected performance measures is also shown and results are presented together with directions for future research.     

Simulation Analysis Using Multi-Agent Systems for Social Norms

With the existence of the social customs or norms, Naylor demonstrates a possibility of stable long-run equilibria of support for a strike in a labor market, and this implies that at least some individuals will behave cooperatively and hence the prisoners’ dilemma could be escaped. In this paper, using an agent-based simulation model in which artificial adaptive agents have mechanisms of decision making and learning based on neural networks and genetic algorithms, we compare the results of our simulation analysis with that of the mathematical model by Naylor. In particular, while Naylor’s model is based on rationality as it relates to individual utility maximization, agents behave adaptively in our agent-based simulation model; agents make decisions by trial and error, and they learn from experiences to make better decisions.      

How cognitive and affective aspects can influence the outcome of the group decision‐making process

Supporting group decision‐making when the decision makers are spread around the world is a complex process. The mechanisms of automated negotiation, such as argumentation, can be used in Ubiquitous Group Decision Support Systems (UbiGDSS) to help decision makers find a solution based on their preferences. However, the decision‐making process is much more than just a simple criteria and alternative analysis. There are many cognitive and affective issues that affect the outcome, and these issues should not be ignored; otherwise, the quality of the decision could be compromised. In this paper, we detail an UbiGDSS architecture and explore 2 cognitive and affective methods that are essential to the group decision‐making process. We explain how agents can reason about self‐expertise and other decision makers' credibility, and how agents can verify and react to tendencies throughout the decision‐making process. We intend agents to achieve higher quality and more consensual decisions. In any simulation environment that we tested, agents that analysed credibility, expertise, and/or analysed tendencies always achieved a higher consensus compared to agents that used neither of the proposed methods. Likewise, agents that used neither of the proposed methods or only performed tendencies analysis obtained the worst average satisfaction levels for each simulation environment.     

The effects of agent activeness and cooperativeness on team decision efficiency: A computational simulation study using Team-Soar

Within an organizational context, an agent type reflects the behavioral tendency that a member might employ across tasks. Until now, however, the impacts of agent types on team performance are not well understood. To address this issue, this study examines the relationships of agent activeness and cooperativeness with team decision efficiency at different degrees of information redundancy by using a team model consisting of four AI agents. This study presents the team model called “Team-Soar” and describes how the model implements agent activeness at two levels (active and passive) and agent cooperativeness at three levels (cooperative, neutral, and selfish). Then a computational simulation experiment is described. Results of the simulation indicate that the impacts of the agent type depend on the amount of information to be processed and active style boosts the effects of agent cooperativeness on team efficiency. The results also indicate that active agents do not always contribute team efficiency more than passive agents.     

A modeling framework for urban traffic systems microscopic simulation

In this paper a modeling framework for urban traffic systems (UTS) is presented. The model, used for agent based micro-simulation, describes both the traffic network and dynamic entities, namely vehicles, traffic lights, and pedestrians. The framework allows defining systematically the necessary components and their behavior of a model oriented to event driven simulation, which can be executed in a distributed way. In the model, the vehicles are conceived as mobile agents with decision making capabilities that interact with the environment and other entities within the traffic network, performing diverse activities according to numerous situations arisen during the simulation. A multi-level Petri net based formalism, named n-LNS is used for describing the structure of the UTS and the components behavior. The first level describes the traffic network; the second level models the behavior of diverse road network users considered as agents, and the third level specifies detailed procedures performed by the agents, namely travel plans, tasks, etc.     

SITUATED CELLULAR AGENTS APPROACH TO CROWD MODELING AND SIMULATION

The article describes a multi-agent approach to crowd modeling and simulation. After a brief introduction of the Situated Cellular Agents model, the guidelines to the crowd modeling approach is introduced as a way to support the communication among the different actors that are part of the simulation project team. The approach is then applied to describe a complex scenario providing a blend of competitive and cooperative behavior for pedestrian agents: an underground station. A module supporting the effective 3D visualization of simulated crowd dynamics is finally introduced, as an instrument for the communication of simulation results to decision makers and nonexperts in crowd phenomena.     

基于多agent的供应链信息协调建模与仿真*

从核心制造企业的信息需求出发,采用基于多agent的供应链建模与仿真方法,研究在订单执行过程中,生产计划和采购计划等决策活动中的信息协调。利用面向agent的系统分析方法,提出了基于多agent的供应链信息协调框架模型,对其中的功能agent进行算法描述。结合Swarm平台构建了供应链仿真模型,对生产计划和采购计划等决策活动中的信息协调进行了模拟和分析,初步验证了模型的有效性。