智能规划中面向简单偏好的高效求解方法

智能规划(AI planning)简称规划,是人工智能领域的一个重要分支,在各领域均有广泛应用,如工厂车间作业调度、物资运输调度、机器人动作规划以及航空航天任务规划等.传统智能规划要求规划解(动作序列)必须最终实现整个目标集合,这种目标一般被称为硬目标(hard goal).然而,许多实际问题中,求解的重点并不只是尽快实现目标以及尽量减少动作序列产生的代价,还需考虑其他因素,如资源消耗或时间约束等.为此,简单偏好(也称软目标soft goal)的概念应运而生.与硬目标相反,简单偏好是可以违背的.本质上,简单偏好用于衡量规划解质量的优劣,而不会影响规划解是否存在.现有关于简单偏好的研究进展缓慢,在规划解质量方面不尽如人意即求得的规划解与最优解的差距较大.提出了一种求解简单偏好的高效规划方法,将简单偏好表达为经典规划(classical planning)模型的一部分,并利用SMT (satisfiability modulo theories)求解器识别多个简单偏好之间的各种关系,从而约简简单偏好集,减轻规划器的求解负担.该方法的主要优势在于:一方面,提前对简单偏好集进行裁剪,在一定程度...     

部分满足问题规划启发式研究

在很多现实规划问题中,Agent通常不具备足够的资源获得所有的目标.因此,需要寻找满足目标子集的规划.本文对部分满足规划问题进行了描述,提出了一种解决部分满足规划问题的新方法.讨论了代价信息在规划图中的传播及如何利用代价信息选择目标子集,并给出了基于代价的启发式来引导回归搜索.采用智能规划中的基准问题对提出的方法进行了实验验证,实验表明该方法可以获得质量更高的规划解,最后提出了今后的改进方向.     

规划问题编码为约束可满足问题的研究

基于约束可满足问题的规划求解是研究智能规划的重要技术方法。把规划问题编码为约束可满足（CSP）问题，是这种规划求解方法的关键技术之一。本文介绍把规划问题编码为约束可满足问题的方法，及一些已有的并且已经用于规划的可满足过程，并对这些编码方法做进一步的研究，主要讨论领域知识在编码方法中的应用，提出在编码求解中加入领域知识的观点。     

智能仓储货位规划与AGV路径规划协同优化算法

智能仓储的优化一般分为货架优化和路径优化两部分.货架优化针对货物与货架两者的关系,对货物摆放位置进行优化;而路径优化主要寻找自动引导小车（Automated Guided Vehicle,AGV）的最优路径规划.目前,大多的智能仓储优化仅对这两部分进行独立研究,在实际仓储应用中只能以线性叠加的方式解决问题,导致问题的求解易陷入局部最优中.本文通过对智能仓储环节中各部分的关系进行耦合分析,提出了货位和AGV路径协同优化数学模型,将货架优化和路径规划归为一个整体;此外,提出了智能仓储协同优化框架的求解算法,包括货品相似度求解算法和改进的路径规划算法;并在以上两种算法的基础上,使用改进的遗传算法,实现了货位路径协同优化.实验结果验证了本文提出的智能仓储协同优化算法的有效性和稳定性.通过使用该算法可有效提高仓储的出货效率,降低运输成本.     

基于二型模糊集推理的优化航路规划方法

航路规划是带约束的多目标优化问题,常用的优化算法是通过加权系数法把多目标优化问题转化为单目标优化问题。该固定的加权系数无法适应战场环境的变化,且无法满足不同专家对优化目标的个人偏好。针对以上问题,提出基于二型模糊集推理的优化航路规划方法。建立航行器复杂约束层次表达模型,采用改进的Per-C方法,利用不同专家对优化目标的偏好信息以及航路约束值实现模糊推理,求取航路模糊代价。将模糊推理应用于A*搜索代价计算过程,最终实现优化的多目标航路规划方法。实验结果表明,该方法能够准确反映各专家对优化目标的偏好,具有较强的灵活性和通用性。     

规划问题编码成SAT问题研究

智能规划问题是一个NP-hard的问题。近年来,由于在可满足问题（SAT）研究领域取得了较大进展,出现了一大批快速的能达到工业级应用的SAT solver求解器的出现,这使得运用可满足技术来求解规划问题的方法越来越得到智能规划研究者们的重视。用可满足技术求解规划问题的首要任务是必须将规划问题“翻译”成可满足问题。讨论了如何将规划问题编码成命题可满足问题的一般技术,并对“直接编码”和“基于规划图的编码”两种编码技术进行了比较,指出了两种编码技术各自的优缺点。在此基础上,深入地分析了各种不同的编码方案之间的异同点以及它们各自的优缺点。最后,指出了用SAT技术求解规划问题中存在的一些问题以及相关改进方法。     

空间飞行器有效载荷规划与调度软件平台

单一的算法已经无法解决大型复杂空间飞行器的有效载荷规划与调度问题,建立具有通用性和灵活性且适用于该问题的软件平台.分析国内外现有研究方法,算法比较和智能规划思想.总结有效载荷规划与调度问题共性,抽象为活动和约束,进一步使用CSOP模型对其进行数学描述.利用智能规划思想建立了有效载荷规划与调度软件平台总体架构,详述各组成结构、功能以及实现方法,描述了高级目标如何转换为具体的约束和活动的流程,并分析了其核心智能规划的一般方法.初步的仿真结果表明,该平台设计方案基本满足设计要求.     

基于协同进化的多智能体机器人路径规划

协同进化是一种新兴的、简单有效的智能优化方法,具有较好的收敛性、鲁棒性和高效性,在多目标优化问题中得到很广泛应用。将其应用到复杂环境下多智能体机器人的路径规划中,并设计适应度评价函数。同时,引入一系列新的变异操作算子,有效地对多智能体机器人规划的路径进行优化,加速了整体的规划速度,避免规划陷入局部最优,从而获得多智能体系统的全局最优或次优解。最后给出了的仿真结果证明方法可行、有效。     

多agent规划综述*

多Agent规划是智能规划和多Agent系统的交叉领域,随着智能规划领域研究范围的不断扩展和多Agent系统领域研究内容的不断深入,多Agent规划受到了越来越多的关注。有鉴于此,本文对多Agent规划的概念和主要方法进行全面综述。具体内容包括智能规划和多Agent系统的背景介绍、多Agent规划的各种形式化描述方式以及基于规划修复、合并或者马尔可夫决策过程的的分布式规划方法。最后,还给出了多Agent规划的发展趋势。     

面向产品装配序列规划的智能优化算法库

针对复杂产品装配规划的组合爆炸和盲目搜索难题,以及单个智能优化算法各自存在的缺点,提出一种用装配序列智能优化算法库解决装配序列规划问题的方法.装配序列规划智能优化算法库主要由算法顾问和算法池构成,算法顾问依据装配规划问题的描述、算法可量化性能的主要参考指标和经验公式,向装配规划人员推荐解决规划任务的最合适算法;算法池包括改进的遗传算法、蚁群算法和模拟退火算法等3种智能优化算法.建立了统一的装配序列规划优化模型和智能算法评价指标体系,并给出了装配序列规划智能优化算法库的具体操作流程.最后结合瓶塞开启机实例,验证了该算法库为装配规划人员推荐的智能优化算法是合理的.     

Adaptive selection of reactive/deliberate planning for a dynamic environment

This paper proposes and evaluates a new real-time reactive planning approach for a dynamic environment. In addition to having the features of conventional real-time reactive planning, which can react in a dynamic environment, our planning can perform deliberate planning appropriately. The proposed planning uses three kinds of agents: behavior agents that control simple behavior, planning agents that make plans to achieve their own goals, and behavior-selection agents that intermediate between behavior agents and planning agents. They coordinate a plan in an emergent way for the planning system as a whole. We confirmed the effectiveness of our planning by means of a simulation. Furthermore, we implemented an active-vision system and used it to verify the real-world efficiency of our planning.     

Argumentation-based negotiation planning for autonomous agents

When we negotiate, the arguments uttered to persuade the opponent are not the result of an isolated analysis, but of an integral view of the problem that we want to agree about. Before the negotiation starts, we have in mind what arguments we can utter, what opponent we can persuade, which negotiation can finish successfully and which cannot. Thus, we plan the negotiation, and in particular, the argumentation. This fact allows us to take decisions in advance and to start the negotiation more confidently. With this in mind, we claim that this planning can be exploited by an autonomous agent. Agents plan the actions that they should execute to achieve their goals. In these plans, some actions are under the agent's control, while some others are not. The latter must be negotiated with other agents. Negotiation is usually carried out during the plan execution. In our opinion, however, negotiation can be considered during the planning stage, as in real life. In this paper, we present a novel approach to integrate argumentation-based negotiation planning into the general planning process of an autonomous agent. This integration allows the agent to take key decisions in advance. We evaluated this proposal in a multiagent scenario by comparing the performance of agents that plan the argumentation and agents that do not. These evaluations demonstrated that performance improves when the argumentation is planned, specially, when the negotiation alternatives increase.     

Information systems planning: Lessons from strategic planning

IS planning has changed considerably since its inception as an operational planning tool, expanding its scope to address key business objectives and forging a link with strategic business planning. A great deal of attention is currently focused on identifying and developing strategic information systems. Strategic planning has also become more broadly-focused over time, as simple forecasting techniques have been replaced by a variety of adaptive approaches that emphasize industry structure analysis and environmental dynamics. However, strategic planning has come under fire recently for failing to live up to its promises. As these two disciplines become more closely aligned, planners can learn from the experiences of strategic planners and, hopefully, avoid a similar crisis of confidence.     

An anchorage planning strategy with safety and utilization considerations

Heavy maritime traffic and the subsequent increase in vessel density in anchorages have recently become a focal issue in maritime traffic safety. In this study, we consider the problem of determining the optimal berth locations of incoming vessels in an anchorage area with the goals of maximizing utilization and minimizing the risk of accidents. We introduce novel performance metrics aimed at measuring achievement of these two goals. In this context, we propose a multi-objective optimization strategy and benchmark it against current state-of-the-art anchorage planning algorithms using real-world data as well as Monte Carlo simulations. Our results indicate that the proposed strategy yields much safer berth locations while maintaining similar utilization levels.     

Manipulation planning using learned symbolic state abstractions

We present an approach for planning robotic manipulation tasks that uses a learned mapping between geometric states and logical predicates. Manipulation planning, because it requires task-level and geometric reasoning, requires such a mapping to convert between the two. Consider a robot tasked with putting several cups on a tray. The robot needs to find positions for all the objects, and may need to nest one cup inside another to get them all on the tray. This requires translating back and forth between symbolic states that the planner uses, such as stacked (cup1,cup2), and geometric states representing the positions and poses of the objects. We learn the mapping from labelled examples, and importantly learn a representation that can be used in both the forward (from geometric to symbolic) and reverse directions. This enables us to build symbolic representations of scenes the robot observes, but also to translate a desired symbolic state from a plan into a geometric state that the robot can achieve through manipulation. We also show how such a mapping can be used for efficient manipulation planning: the planner first plans symbolically, then applies the mapping to generate geometric positions that are then sent to a path planner.     

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.     

基于改进粒子群算法的移动机器人多目标点路径规划

针对移动机器人遍历多个目标点的路径规划问题,提出了一种基于改进粒子群算法和蚁群算法相结合的路径规划新方法。该方法将目标点的选择转化为旅行商问题,并利用蚁群算法进行优化,定义了每两个目标点之间的路径规划目标函数,利用粒子群算法对其进行优化。针对粒子群算法存在的早熟现象,将反向学习策略引入粒子群算法,并对粒子群算法的惯性权重和学习因子进行改进。性能测试结果表明,改进的粒子群算法能有效避免粒子早熟现象,提高粒子群算法的寻优能力及稳定性。仿真实验结果验证了新方法能有效地实现机器人的多目标点无碰撞路径规划。真实环境下的实验结果证明了新方法在机器人多目标点路径规划的实际应用中也具有有效性。     

A model for integrating process planning and production planning and control in machining processes

The goal of process planning is to propose the routing of a previously designed part and results in a sequence of operations and their parameters. It concerns and requires detailed information about the process. The goal of production planning, on the other hand, is to schedule, sequence and launch the orders introduced on the routing sheet into the job-shop according to the enterprise's strategic goal and the actual conditions of the production plant. The goals, information and decisions taken in process planning and production planning and control are often very different and, because of that, it is very difficult to integrate them.

The objective of this work is to develop a model that can be applied in the future to the development of an integrated process planning and scheduling tool using an integrated definition (IDEF) methodology to design an activity model, which integrates process and production planning in metal removal processes. An activity model will be used to develop a system that allows the user to plan the process and the production at the same time in collaborative engineering work. To design the activity model, a wide range of parts were evaluated and processed in an actual job-shop factory. Several activities were developed in detail to be tested in real cases, and an example of one of them is introduced in this article.     

A knowledge-based inspection process planning system for coordinate measuring machines

The coordinate measuring machine (CMM) is one of the most effective geometry inspection facilities used in manufacturing industry. To fully utilize its capabilities in a computer-integrated manufacturing (CIM) environment, we should integrate CMM with other systems and facilities. This paper presents the development of a knowledge-based inspection planner based on the fundamental principles of AI planning to integrate computer-aided design systems and CMMs. The issues involved in CAD-directed inspection process planning are examined; the task of inspection process planning is decomposed into a number of sub-tasks. According to the task decomposition, a knowledge-based planning system was designed with several modules. Each of these modules consists of a knowledge base, a control operator, a context and a communication interface. The knowledge base is the local knowledge source for problem solving; the control operator determines when and where the knowledge is applied; the context contains the initial planning state which is essential input part information, the intermediate planning states which result from the tentative decision made by the modules, and the goal state. The module interfacing was realized by directly calling procedures defined in other modules to pass the planning tasks and decisions. Examples are included to explain the planning knowledge and strategy.