HUNTBot-第一人称射击游戏中NPC的结构设计

游戏产业的发展迫切需要使用新的技术开发具有智能行为的NPC,Agent技术因其对人类智能的刻画及模拟不失为一种好的选择。同时,电脑游戏也因为其固有的复杂、实时、动态性而吸引了众多Agent研究者的目光。针对第一人称射击游戏——“虚幻竞技场（Unreal Tournament,UT）”设计了HUNTBot作为游戏中的非玩家角色NPC。这种Agent具有混合式结构,使Agent既能对变化的环境迅速做出反应,又能对目标进行实时规划,并具有社会性和学习能力。因此Agent能够适应动态、复杂、实时的游戏环境,使NPC的智能行为更加接近人类玩家。     

HTN规划及其复杂度分析

为了克服经典状态空间规划中的状态空间的指数爆炸问题,研究者们提出了分层任务网络规划（HTN）技术。给出了HTN规划的形式表示,并给出了其操作语义,在此基础上讨论了HTN规划的复杂性,指出了HTN规划技术的优缺点所在。     

Quake2中NPC智能结构分析及行为改进

伴随着计算机游戏行业的发展,游戏智能已经经历了几十年的发展。它一般是通过控制游戏中非玩家角色（non-player character,NPC）的行为动作来表现的,其目的在于给玩家更好的游戏体验。游戏智能常见的问题是NPC的行为不够多样化,容易被有经验的玩家预测。为此,通过对Quake2的AI开发工具Fear对NPC的智能结构进行改造,提供新的NPC行为,用以改善玩家的游戏体验。     

结合行为树与Q-learning优化UT2004中agent行为决策

针对FPS游戏UT2004中的NPC（Non-Player-Character,即非玩家角色）的行为决策不够灵活多变,不够智能等问题,结合行为树与Q-learning强化学习算法,提出了一种预处理与在线学习结合的方式优化NPC行为决策的方法。通过在行为树上的强化学习,NPC行为决策更为灵活、智能,即human-like。实验结果表明了该方法的有效性与可行性。     

一种利用工作流模型的分层任务网络规划领域建模方法

为了有效地获取和利用领域知识,提高规划效率,分析了工作流模型和分层任务网络(HTN)规划领域模型的相似性,提出了一种采用工作流模型进行规划领域建模,对领域知识进行获取和表达的方法.工作流模型中的行动和工作流模式,转换为HTN规划中的行动和任务分解;另外,引入了循环(Loop)工作流模式,转换为HTN规划中的递归调用,扩展了工作流模式对规划领域知识的表达能力.在典型的几个规划领域中,引入领域知识后大大提高了规划器的求解效率,从而验证了应用工作流模型进行规划领域建模的有效性.     

一种适合网络游戏的多NPC协同运动策略

研究群体非玩家控制角色(NPC)如何根据具体游戏环境和剧情因素进行相互联系的协同运动以实现群体行为。在此基础上,提出一种适合网络游戏的多NPC协同运动策略,采用人工势场法描述游戏状态,并利用粒子群算法确定下一时刻每个NPC的最佳运动位置,从而实现个体NPC根据游戏状态自主完成群体作战目标。实验结果表明,该方法能够在网络游戏中保证群体NPC高效、自主地适应环境,并能较好地完成与玩家对抗的游戏任务。     

有限理性模型在游戏智能决策中的研究与应用*

为了让游戏NPC能够学习和模拟玩家在游戏中的策略和行为方式,在基于模型的智能决策方法基础上,结合行为决策理论中的有限理性模型提出了一种新的游戏智能方法.该方法分别从有限理性模型的两个核心原则——有限理性和满意准则来改进过去的方法在感知和决策过程中所面对的问题,从而使得游戏NPC行为决策方式更加人性化.最后,通过在Starcraft平台上与其他方法的对抗性实验来进一步验证该方法的优势.     

电脑游戏杆的工作原理,安装使用与自制方法

随着微机市场上硬件价格不断下降，应用软件越来越丰富多彩，各种IBMPC兼容机日益得到普及，逐步进入家庭。许多家庭购买微机主要用于家庭教育、财务管理及电脑游戏，而游戏杆（JOYSTICK）是玩电脑游戏，尤其是打斗类游戏必不可少的一种输入设备。本文详细介绍电脑游戏杆的工作原理、安装使用及自制方法。工作原理电脑游戏杆是PC上的一个有趣而且很有用的接口，通过插在PC扩展槽上的控制卡，我们可以同时使用两个游戏杆。目前大家所使用的游戏杆均为15脚D型插头，两个游戏杆的输入同时由这个15脚D型插头传送，其原理图见图1。可以看…     

作战班组层次行为建模

战术仿真是作战仿真中重要的一部分,是针对合同战术、班组战术层次的仿真,涉及班组的整体决策和成员的个体行为建模,班组的决策行为通常关注于班组成员间的协同合作,而成员的个体行为易受各种战场要素的影响,关注于个体行为的规划,只在单一层次进行建模会造成行为描述的混乱。为了区分班组行为和个体行为,增强个体行为的表达能力,基于行为树和HTN（Hierarchical Task Network）构建了班组层次行为模型,将战术命令的执行分为班组整体决策和实体具体执行两个层次。班组决策层采用行为树对班组的决策行为进行建模,提出了寻径距离因素和武器威胁因素的战术位置选择评估方法,负责班组成员间的协同决策,为班组成员分配任务;实体执行层采用HTN对作战实体的行为进行建模,根据班组决策层分配的任务利用规划算法规划个体任务计划,提升对实体行为描述的表达能力。并通过搭建仿真对抗环境验证了该层次行为模型的有效性。     

教育游戏在高等院校中的应用研究

教育游戏是教育软件与电脑游戏相结合的产物,它可以为学习者创造一个自由、和谐、有趣的学习环境,有助于学习者更好地掌握相应的知识。文中分析了教育游戏的特点,探讨了将教育游戏引入高等院校的必要性,列举了两个在高校教学中应用教育游戏的实例并进行研究。     

不确定层次任务网络规划研究综述

层次任务网络(Hierarchical task network, HTN)规划作为一项重要的智能规划技术被广泛应用于实际规划问题中, 传统的HTN规划无法处理不确定规划问题.然而, 现实世界不可避免地存在无法确定或无法预测的信息, 这使许多学者开始关注不确定规划问题, 不确定HTN规划研究也成为HTN规划研究的前沿.本文从HTN规划过程出发分析了不确定HTN规划问题中涉及的三类不确定, 即状态不确定、动作效果不确定和任务分解不确定; 总结了系统状态、动作效果和任务分解等不确定需要扩展确定性HTN规划模型的工作, 以此对现有不确定HTN规划的研究工作加以梳理和归类; 最后,对不确定HTN规划研究中仍需要解决的问题和未来的研究方向作了进一步展望.     

Learning Hierarchical Task Models from Input Traces

We describe HTN‐MAKER , an algorithm for learning hierarchical planning knowledge in the form of task‐reduction methods for hierarchical task networks (HTNs). HTN‐MAKER takes as input a set of planning states from a classical planning domain and plans that are applicable to those states, as well as a set of semantically annotated tasks to be accomplished. The algorithm analyzes this semantic information to determine which portion of the input plans accomplishes a particular task and constructs task‐reduction methods based on those analyses. We present theoretical results showing that HTN‐MAKER is sound and complete. Our experiments in five well‐known planning domains confirm the theoretical results and demonstrate convergence toward a set of HTN methods that can be used to solve any problem expressible as a classical planning problem in that domain, relative to a set of goal types for which tasks have been defined. In three of the five domains, HTN planning with the learned methods scales much better than a modern classical planner.     

Automated instructional design for CSCL: A hierarchical task network planning approach

In Computer Supported Collaborative Learning (CSCL), one of the most important tasks for instructional designers is to define scenarios that foster group learning. Such scenarios, defined as Units of Learning (UoLs), comprise different components and are organized according to pedagogical approaches to orchestrate group learning processes. Examples of UoL components are learning objects, student roles, student characteristics (e.g., background, preferences, learning styles, etc.), instructional/learning goals, and activities, among others. Thus, the instructional design (ID) of a proper UoL for CSCL is a complex task that requires practice and experience. This is particularly true when designing, developing, adapting, and customizing UoLs, taking into consideration different instructional/learning goals and individual preferences of students. This paper therefore proposes using a Hierarchical Task Network (HTN) planning approach to automate and optimize the tasks of designers. To accomplish that, we define an initial CSCL scenario as “an ID task” and “a set of information related to students and the domain to be taught.” Then we propose a model that formally describes ID for CSCL as HTN planning, where the initial CSCL scenario is adapted and refined according to student needs. In this model, the ID strategies are defined as hierarchical tasks and methods into a planning domain definition, and the initial CSCL scenario is defined as a planning problem definition. To validate our approach, we develop a CSCL courseware generator that (i) helps designers to set up an initial CSCL scenario; (ii) automatically generates a personalized UoL based on a given initial scenario; and (iii) supports the adaptation of UoLs.     

To Plan or to Simply React? An Experimental Study of Action Planning in a Game Environment

Many contemporary computer games, notably action and role‐playing games, represent an interesting class of navigation‐intensive dynamic real‐time simulations inhabited by autonomous intelligent virtual agents (IVAs). Although higher level reasoning of IVAs in these domains seems suited for action planning, planning is not widely adopted in existing games and similar applications. Moreover, statistically rigorous study measuring performance of planners in decision making in a game‐like domain is missing. Here, five classical planners were connected to the virtual environment of Unreal Development Kit along with a planner for delete‐free domains (only positive preconditions and positive effects). Performance of IVAs employing those planners and IVAs with reactive architecture was measured on a class of game‐inspired test environments of various sizes and under different levels of external interference. The analysis has shown that planning agents outperform reactive agents if (i) the size of the problem is small or if (b) the environment changes are either hostile to the agent or infrequent. In delete‐free domains, specialized approaches are inferior to classical planners because the lower expressivity of delete‐free domains results in lower plan quality. These results can help to determine when planning is advantageous in games and for IVAs control in other dynamic real‐time environments.     

What planner for ambient intelligence applications?

The development of ambient intelligence (AmI) applications that effectively adapt to the needs of the users and environments requires, among other things, the presence of planning mechanisms for goal-oriented behavior. Planning is intended as the ability of an AmI system to build a course of actions that, when carried out by the devices in the environment, achieve a given goal. The problem of planning in AmI has not yet been adequately explored in literature. We propose a planning system for AmI applications, based on the hierarchical task network (HTN) approach and called distributed hierarchical task network (D- HTN), able to find courses of actions to address given goals. The plans produced by D-HTN are flexibly tailored to exploit the capabilities of the devices currently available in the environment in the best way. We discuss both the architecture and the implementation of D-HTN. Moreover, we present some of the experimental results that validated the proposed planner in a realistic application scenario in which an AmI system monitors and answers the needs of a diabetic patient.     

Coordinated Crowd Simulation With Topological Scene Analysis

This paper proposes a new algorithm to produce globally coordinated crowds in an environment with multiple paths and obstacles. Simple greedy crowd control methods easily lead to congestion at bottlenecks within scenes, as the characters do not cooperate with one another. In computer animation, this problem degrades crowd quality especially when ordered behaviour is needed, such as soldiers marching towards a castle. Similarly, in applications such as real‐time strategy games, this often causes player frustration, as the crowd will not move as efficiently as it should. Also, planning of building would usually require visualization of ordered evacuation to maximize the flow. Planning such globally coordinated crowd movement is usually labour intensive. Here, we propose a simple solution that is easy to use and efficient in computation. First, we compute the harmonic field of the environment, taking into account the starting points, goals and obstacles. Based on the field, we represent the topology of the environment using a Reeb Graph, and calculate the maximum capacity for each path in the graph. With the harmonic field and the Reeb Graph, path planning of crowd can be performed using a lightweight algorithm, such that any blocking of one another's paths is minimized. Comparing to previous methods, our system can synthesize globally coordinated crowd with smooth and efficient movement. It also enables control of the crowd with high‐level parameters such as the degree of cooperation and congestion. Finally, the method is scalable to thousands of characters with minimal impact to computation time. It is best applied in interactive crowd synthesis systems such as animation designs and real‐time strategy games.     

LEARNING PRECONDITIONS FOR PLANNING FROM PLAN TRACES AND HTN STRUCTURE

A great challenge in developing planning systems for practical applications is the difficulty of acquiring the domain information needed to guide such systems. This paper describes a way to learn some of that knowledge. More specifically, the following points are discussed. (1) We introduce a theoretical basis for formally defining algorithms that learn preconditions for Hierarchical Task Network (HTN) methods. (2) We describe Candidate Elimination Method Learner ( CaMeL ), a supervised, eager, and incremental learning process for preconditions of HTN methods. We state and prove theorems about CaMeL's soundness, completeness, and convergence properties. (3) We present empirical results about CaMeL's convergence under various conditions. Among other things, CaMeL converges the fastest on the preconditions of the HTN methods that are needed the most often. Thus CaMeL's output can be useful even before it has fully converged.     

基于行为协同和虚拟目标相结合的无人机实时航路规划

针对实时航路规划问题,综合考虑航路最优、平滑性、全局收敛性以及从威胁域的逃逸能力等限制时,还没有有效的规划算法.为此提出了一种基于行为协同和虚拟目标相结合的无人机实时航路规划方法.该方法将无人机的航路规划行为分为局部和全局行为:局部行为采用基于模糊控制的方法,用来实现威胁体规避;全局行为使用全局算法,通过全局目标和虚拟目标的切换实现了全局目标收敛和威胁域边界跟踪,然后通过模糊控制器对两种行为进行协同.最后通过分析、证明以及几种不同情形下的仿真表明该方法具有航路短、平滑和全局收敛的特点.