基于缩减信念状态的Conformant 规划方法

Conformant 规划问题通常转化为信念状态空间的搜索问题来求解.提出了通过降低信念状态的不确定性来提高规划求解效率的方法.首先给出缩减信念状态的增强爬山算法,在此基础上,提出了基于缩减信念状态的Conformant 规划方法,设计了CFF-Lite 规划系统.该规划器的求解过程包括两次增强爬山过程,分别用于缩减信念状态和搜索目标.首先对初始信念状态作最大程度的缩减,提高启发函数的准确性;然后从缩减后的信念状态开始执行启发式搜索.实验结果表明,CFF-Lite 规划系统通过快速缩减信念状态降低了问题的求解难度,在大多数问题上,求解效率和规划解质量与Conformant-FF 相比,都有显著的提高.     

基于多值表示的并行规划方法

Fast Downward规划系统是第四届国际规划竞赛的冠军.以高效的串行规划系统Fast Downward为基础,设计并实现了并行规划系统Parallel Downward.首先提出4个并行规划的相关定义;之后提出多值规划任务下动作互斥的定义、充要条件,并实现了动作互斥判断算法;在此基础上设计了候选并行动作集的生成算法;然后为提高系统求解质量重新设计了新的搜索控制策略;最后,给出剪枝策略来抑制并行规划状态空间的指数级膨胀.通过对国际规划竞赛测试问题的实验,Parallel Downward表现出良好的规划效率和规划质量,相比Sapa规划系统Parallel Downward具有较好的可扩展性.     

可控网络中多Agent系统信念可达性和收敛速度分析

在可控网络中,利用多agent系统是网络控制的一种重要方法.在可控网络中,多agent系统中所有agent持有的信念必须在决策前达到与网络实际状态一致,即多agent系统的信念应具有可达性,是实现网络合理控制的基础.传统的基于agent行为的信念更新模型建模过程复杂,不适合在网络控制中直接分析多agent系统信念的可达性和收敛速度.基于传统的信念更新模型,提出了信念距离的概念,并在该概念的基础上提出了新的多agent系统信念距离更新模型,并证明了该模型的合理性.该模型对多agent系统信念距离更新过程加以描述,利用线性系统对多agent系统信念收敛过程进行描述,简化了对多agent系统信念可达性和收敛速度分析的复杂性.在该模型基础上,对网络控制中多agent系统信念可达性和收敛速度进行了分析,给出了判断多agent系统信念可达性的充要条件和收敛速度的上限.另外,针对全耦合网络和无标度网络两种复杂网络的特点,分别对两种网络下多agent系统信念可达性和收敛速度进行了讨论.提出的信念距离更新模型具有良好的适应性,为判断多agent系统的信念可达性提供了有力的工具.     

一种实时多目标路径规划方法

提出一种利用实时搜索思想的多目标路径规划方法.首先设计并实现局部路径规划算法,在有限的局部空间内执行启发式搜索,求解所有局部非支配路径;在此基础上,提出实时多目标路径规划方法,设计并实现相应的启发式搜索算法,在线交替执行局部搜索过程、学习过程与移动过程,分别用于求解局部空间内的最优移动路径,完成状态的转移和更新状态的启发信息,最终到达目标状态.研究表明,实时多目标启发式搜索算法通过限制局部搜索空间,避免了大量不必要的计算,提高了搜索效率,能够高效地求解多目标路径规划问题.     

基于自动推理技术的智能规划方法

对几种智能规划方法中利用的逻辑演绎与推理技术予以分析,分别介绍利用命题逻辑的基于可满足性的规划方法与规划系统,利用模态逻辑与析取推理的Conformant规划方法与规划系统,利用非单调逻辑的规划方法和利用模糊描述逻辑的Flexible规划方法,并结合国际规划竞赛和相关论文等的实验结论说明上述方法的有效性和可行性.最后,提出目前基于自动推理技术的智能规划方法所面临的挑战、可能的处理方法以及与之相关的研究热点与趋势.     

基于内发动机机制的移动机器人自主路径规划方法

基于内发动机机制,为移动机器人建立一种新的路径规划方法.将已有内发动机机制中基于状态的好奇心函数扩展为基于动作的好奇心函数,并建立相应的动作选择机制,更符合生物可解释性.设计障碍物分布环境下的移动机器人状态能量函数,用于决定学习的方向.实验结果表明,所建立的方法能够有效地帮助机器人学习环境知识,实现不同初始状态下的避障导航任务.同时,能量函数的设计不依赖于具体环境,即使目标点发生改变,机器人也能通过重新学习到达目标,体现出方法的高度自主性和非任务性.     

基于伪谱法的翼伞系统归航轨迹容错设计

针对翼伞系统在归航过程中,控制电机工作异常致使控制性能发生变化,无法按原有规划轨迹到达目标点的问题,提出一种基于Gauss伪谱法的归航轨迹容错设计方法.首先根据翼伞系统控制特性的不同,分别建立了正常和单电机异常工作状态下的质点模型,并根据伞形参数确定了两种工作状态下的约束条件和目标函数;其次,利用Gauss伪谱法分别对两种工作状态下轨迹规划的最优控制问题求解,获得翼伞系统不同状态下的最优飞行轨迹.仿真结果表明,在约束情况下,翼伞系统无论在正常和单电机异常工作时都可以顺利到达目标点,获得高精度的飞行轨迹.     

不确定规划中非循环可达关系的求解方法

对一个不确定状态转移系统求多个规划问题,那么获得不确定状态转移系统的状态可达关系可以方便求解规划问题,减少冗余计算,建立系统的引导信息。提出一个关于矩阵求不确定领域的状态可达性关系的方法,主要思想是以矩阵乘法来模拟状态转移系统中状态转移,对不确定动作带来的扩散和确定关系带来的聚合进行了统计和处理,从而获得状态可达信息。证明了方法的正确性和有效性。在不确定规划中确定了状态之间的可达性关系,可以在求规划解时删除对规划没有用的状态节点和状态动作序偶;选择能到达目标节点的状态节点和状态动作序偶;进行启发式正向搜索;减少大量冗余计算;提高求解效率。     

智能化战术飞行轨迹规划方法研究

对态势评估与规划系统进行集成,提出了智能化战术飞行轨迹规划系统方案.基于贝叶斯网络和模糊推理技术,实现了战场威胁级别及其相对重要性程度的综合评估.利用模型预测控制的滚动优化和在线校正原理,实现了飞机在线飞行路径规划.建立了路径规划代价函数中加权因子的智能化分配方法,进而实现了威胁评估与路径规划之间的集成,使得路径规划系统能够自适应战场态势的动态变化.最后通过仿真验证了所提出方法的有效性.     

基于深度强化学习求解作业车间机器与 AGV联合调度问题

针对作业车间中自动引导运输车(automated guided vehicle, AGV)与机器联合调度问题,以完工时间最小化为目标,提出一种基于卷积神经网络和深度强化学习的集成算法框架.首先,对含AGV的作业车间调度析取图进行分析,将问题转化为一个序列决策问题,并将其表述为马尔可夫决策过程.接着,针对问题的求解特点,设计一种基于析取图的空间状态与5个直接状态特征;在动作空间的设置上,设计包含工序选择和AGV指派的二维动作空间;根据作业车间中加工时间与有效运输时间为定值这一特点,构造奖励函数来引导智能体进行学习.最后,设计针对二维动作空间的2D-PPO算法进行训练和学习,以快速响应AGV与机器的联合调度决策.通过实例验证,基于2D-PPO算法的调度算法具有较好的学习性能和可扩展性效果.     

Exclusive disjunctive soft sets

Soft sets theory, initiated by Molodtsov, is an emerging tool to deal with uncertain problems and has been studied by scholars in both theory and practice. This paper proposes the notion of exclusive disjunctive soft sets and studies some of its operations, such as, restricted/relaxed AND operations, dependency between exclusive disjunctive soft sets and bijective soft sets, exclusive disjunctive soft decision systems, reduction of exclusive disjunctive soft decision systems, core of exclusive disjunctive soft decision systems, decision rules in exclusive disjunctive decision soft sets. Moreover, this study gives an application of exclusive disjunctive soft sets, which shows that it can be applied to attribute reduction of incomplete information system.     

基于事件的设计与控制技术及其在机器人系统中的应用

基于事件的设计与控制技术已经引起人们的广泛关注,其基本思想就是引入与系统输出相关的运动参考变量(action reference),在任意时刻使系统给定量的输入以及系统的设计都转化为这个参考变量的函数,使系统的设计部分与控制器一样成为系统的决策部件,从而使系统具备处理突发性、不确定性事件的能力,可以根据系统的实际输出来实时、自动地调整系统的输入,实现对系统的实时控制．机器人系统是一个智能控制系统．机器人间的协调协作是其智能性的主要表现．本文首先对基于事件的控制理论及其发展做了回顾,然后介绍了基于事件的控制技术在机器人队形保持、机器制造、多机器人协作等系统智能控制中的应用,实验证明该方法可以取得很好的智能控制效果,提高系统的控制性能．最后探讨了这一研究领域的发展方向．     

Interference probability matrix for disassembly sequence planning under uncertain interference

Disassembly of end-of-life products is a common step in remanufacturing and recycling. Disassembly sequence planning is the process that automatically finds the optimal sequence of components being removed. A key element of disassembly sequence planning is a suitable mathematical representation that describes the interference of any two components in a product. Previous studies on disassembly sequence planning have tended to focused on the interference that is fixed and known. However, the interference may be uncertain due to complex end-of-life conditions such as deformation, corrosion and rust. To deal with uncertain interference, this paper proposes an interference probability matrix as a new mathematical representation that uses probability to indicate uncertainty in the interference, and establishes a multi-threshold planning scheme to generate the optimal disassembly sequence plans. Three case studies are given to demonstrate the use of the proposed approach. It is also tested the performance of four multi-objective optimization algorithms that can be adopted in the proposed multi-threshold planning scheme.     

数据缺失的扩展置信规则库推理方法

数据驱动的扩展置信规则库专家系统能够处理含有定量数据或定性知识的不确定性问题.该方法已被广泛地研究和应用,但仍缺乏在不完整数据问题上的研究.鉴于此,针对不完整数据集上的问题,提出一种新的扩展置信规则库专家系统推理方法.首先提出基于析取范式的扩展规则结构,并通过实验讨论了在新的规则结构下,置信规则前提属性参考值个数对推理...     

Incremental planning using conceptual graphs

Abstract

When performing a planning or design task in many domains it is often difficult to specify in advance what the precise goals are. It is therefore useful to have a system in which the planning process is performed interactively, with the solution approaching the users' intent incrementally through iterations of the planning process. A planning system intended to function in this way must be able to take goal specifications interactively rather than all at once at the beginning of the planning process. The planning process then becomes one of satisfying new goals as they are given by the user, modifying as little as possible the results of previous planning work. Incremental planning is an approach to interactive planning problems that allows a system to create a plan incrementally, modifying a previous plan to satisfy new or more precise goal specifications. In this paper we present an incremental planning system called the general constraint system (GCS) that is based on the conceptual programming environment (CP) developed at New Mexico State University and we show an example of the use of the system for a simple civil engineering design problem     

Temporal Reasoning for a Collaborative Planning Agent in a Dynamic Environment

We present a temporal reasoning mechanism for an individual agent situated in a dynamic environment such as the web and collaborating with other agents while interleaving planning and acting. Building a collaborative agent that can flexibly achieve its goals in changing environments requires a blending of real-time computing and AI technologies. Therefore, our mechanism consists of an Artificial Intelligence (AI) planning subsystem and a Real-Time (RT) scheduling subsystem. The AI planning subsystem is based on a model for collaborative planning. The AI planning subsystem generates a partial order plan dynamically. During the planning it sends the RT scheduling subsystem basic actions and time constraints. The RT scheduling subsystem receives the dynamic basic actions set with associated temporal constraints and inserts these actions into the agent's schedule of activities in such a way that the resulting schedule is feasible and satisfies the temporal constraints. Our mechanism allows the agent to construct its individual schedule independently. The mechanism handles various types of temporal constraints arising from individual activities and its collaborators. In contrast to other works on scheduling in planning systems which are either not appropriate for uncertain and dynamic environments or cannot be expanded for use in multi-agent systems, our mechanism enables the individual agent to determine the time of its activities in uncertain situations and to easily integrate its activities with the activities of other agents. We have proved that under certain conditions temporal reasoning mechanism of the AI planning subsystem is sound and complete. We show the results of several experiments on the system. The results demonstrate that interleave planning and acting in our environment is crucial.     

Autonomous Driving with Concurrent Goals and Multiple Vehicles: Mission Planning and Architecture

We introduce a new distributed planning paradigm, which permits optimal execution and dynamic replanning of complex multi-goal missions. In particular, the approach permits dynamic allocation of goals to vehicles based on the current environment model while maintaining information-optimal route planning for each individual vehicle to individual goals. Complex missions can be specified by using a grammar in which ordering of goals, priorities, and multiple alternatives can be described. We show that the system is able to plan local paths in obstacle fields based on sensor data, to plan and update global paths to goals based on frequent obstacle map updates, and to modify mission execution, e.g., the assignment and ordering of the goals, based on the updated paths to the goals.The multi-vehicle planning system is based on the GRAMMPS planner; the on-board dynamic route planner is based on the D* planner. Experiments were conducted with stereo and high-speed ladar as the to sensors used for obstacle detection. This paper focuses on the multi-vehicle planner and the systems architecture. A companion paper (Brumitt et al., 2001) analyzes experiments with the multi-vehicle system and describes in details the other components of the system.     

Classical negation in logic programs and disjunctive databases

An important limitation of traditional logic programming as a knowledge representation tool, in comparison with classical logic, is that logic programming does not allow us to deal directly with incomplete information. In order to overcome this limitation, we extend the class of general logic programs by including classical negation, in addition to negation-as-failure. The semantics of such extended programs is based on the method of stable models. The concept of a disjunctive database can be extended in a similar way. We show that some facts of commonsense knowledge can be represented by logic programs and disjunctive databases more easily when classical negation is available. Computationally, classical negation can be eliminated from extended programs by a simple preprocessor. Extended programs are identical to a special case of default theories in the sense of Reiter.