分层法求最小权值强规划解

在不确定规划领域中,以往对强规划解的研究侧重于解本身,很少考虑不确定转移系统执行动作所需的代价;而已有的研究最小权值强规划解的算法效率不高。针对这一问题,引入模型检测的强规划分层方法,设计了一种快速求解最小权值强规划解的算法。该算法首先将不确定规划问题中的状态进行强规划分层,然后利用分层信息反向搜索最小权值强规划解;且在搜索的过程中,根据算法策略,实时更新所需搜索层数的上界和下界,从而避免了大量的无用搜索,提高了搜索效率。实验表明:所设计的算法能快速求解出最小权值强规划解,求解效率比已有的直接求解最小权值强规划解的算法高;且分层数和动作数越大,优势越明显。     

正向搜索方法求强规划解

以规划领域中的不确定状态转移系统作为研究对象,设计了求强规划解问题中的状态分层方法,状态被分层以后,求强规划解只需要从初始状态出发,逐次向下层状态转移到目标状态就可以了,在此基础上,设计了用正向搜索方法求强规划解的算法。实验结果表明：所设计的算法比用反向搜索方法求强规划解算法的效率高。     

不确定规划中一种观察信息高效约简算法

在不确定规划中,可通过观察周围的信息来区分多个状态,但周围的观察信息较多,因此如何从大量的观察信息中筛选必须的信息非常重要。以往算法是在直接搜索过程中增加一些剪枝条件来达到优化的目的,存在一定的局限性。在对观察信息约简研究中,为提高搜索效率,设计一种高效的不确定规划中观察信息约筒算法。该算法将规划问题转化为求解O—l矩阵的覆盖问题,使用数据结构十字链表来表示O-1矩阵,通过维护十字链表并采用启发式函数来加速求解一个最小观察变量集。实验结果表明,该算法不仅能够找最小观察变量集,而且运行速度超过同类算法。     

不确定规划中的多Agent带权值强规化算法

在智能规划领域中,以往对不确定规划问题的研究主要集中于单个Agent,而对多Agent规划的研究则侧重于确定规划。针对该问题,提出基于多Agent的带权值不确定规划问题,对所求解的强规划解,设计使其所需动作权值总和近似最小的算法。根据基于模型检测的强规划分层方法,对每个Agent进行强规划分层,合并所有Agent的分层信息,并在合并的过程中得到同层状态之间的冲突表。在保证冲突最小的情况下,以最小动作权值优先的贪心方法,求出强规划解。实验结果表明,该算法能较快地求解出使所选择的动作权值总和近似最小的强规划解。     

一种求解最小权值强规划的方法

以规划领域中的不确定状态转移系统作为研究对象,给出最小权值强规划解的概念,提出一种求最小权值强规划解的方法.该方法可以求解与动作代价相关的数值规划问题,在不确定状态转移系统的执行动作上增加权值来表示动作的代价,在此基础上设计求解最小权值强规划解的算法.实验结果表明,该算法能有效求解最小权值强规划解,且比用反向搜索方法求...     

不确定规划领域中带权值的观察信息约简

现实的规划问题中,观察信息的获取所需的代价是不同的,并且在规划解执行过程中,并非所有的观察信息都是有意义的,因此为了减少执行过程中的开销而对大量的带权值的观察信息进行约简就显得十分重要。首次针对带权值的观察信息约简问题做出研究,定义了带权值的最优观察集的概念,设计了SOWOS算法。该算法找出所有需要区分的状态对,用贪心的思想使搜索按指定顺序选择观察变量,并在搜索的过程中增加剪枝,减少了大量不必要的搜索,最终求得总花费最小的观察集,达到了减少执行成本的目的。实验结果表明,SOWOS算法可以高效地求得带权值的最优观察集,对减少规划执行中的开销贡献明显。     

分层法求强循环规划解

设计了一种求解强循环规划问题的状态分层算法。从目标状态开始,首先进行强规划分层,然后对剩余状态进行弱规划分层,并记录相应信息,最后用该信息作启发因子,在弱规划分层结果中搜索强循环规划分层。分层结束后利用分层时记录的信息可以直接得到强循环规划解。所设计的算法在求解状态动作较多的强循环规划问题时有较高的效率;且当强规划解存在时,求解效率更高,并能保证得到质量更优的强循环规划解——强规划解。实验表明,所设计的算法能够以较少的重复搜索得到强循环规划解,求解效率比反向搜索高。     

强循环规划的观察信息约简

在现实的规划领域中,观察信息的获得需要花费一定的代价,然而对于一个规划解来说,一些观察信息在执行规划解的过程中是不会被用到的,所以如何把这些不必要的观察信息去除从而节省实际过程中的成本是非常有必要的。针对强循环规划的观察信息展开研究,提出了一个对强循环规划观察信息进行约简的算法,该算法能够找出在完全可观察条件下强循环规划的最小观察变量集合,通过使用最小观察变量集合,可以进一步提高执行强循环规划解的效率。     

信息传递法求不确定系统中的状态可达关系

在不确定规划领域中,在求规划问题的解时,由于缺少引导信息,会导致许多无用状态和动作被搜索,造成冗余计算。所以在求规划解之前,找到不确定状态转移系统中状态之间的可达关系是很有意义的。以往的算法是通过矩阵相乘来模拟状态转移,但该类算法对于规模较大的系统开销较大。因此,提出了用信息传递法来求解可达关系,用矩阵来模拟不确定状态转移系统。其中每个状态记录了其他状态到达该状态的可达信息,通过状态之间的可达信息的传递,求得不确定系统的状态可达关系,以避免大量的矩阵运算。通过实验对比表明,当不确定系统规模较大时,所设计的算法优于矩阵相乘的算法。     

不确定规划中状态循环可达关系的求解方法

在不确定规划领域中, 不确定状态转移系统求规划解常常会搜索大量无用的状态和动作, 造成冗余计算。获得不确定状态转移系统的状态可达关系可以避免无用搜索、减少冗余计算, 为系统提供引导信息。以非循环可达关系为基础, 定义矩阵的计算规则, 使用系统的邻接矩阵来计算可达矩阵。同时首次提出了循环可达关系的分类、二可达关系等, 并设计了求循环可达关系的算法, 且以实例证明了算法的有效性和正确性。在不确定规划中获得状态之间的可达性关系, 在求规划解的过程中可以删除大量无用的状态动作序偶, 降低问题规模, 提高求解规划问题的效率。     

CBPOP: A Domain-Independent Multi-Case Reuse Planner

The reuse of multiple cases to solve a single planning problem presents a promise of better utilization of past experience over single-reuse planning, which can lead to better planning performance. In this paper, we present the theory and implementation of CBPOP, and show how it addresses the multi-reuse planning problems. In particular, we present novel approaches to retrieval and refitting. We also explore the difficult issue of when to retrieve in multi-reuse scenarios, and we empirically compare the results of several solutions we propose. Results from our experiments show that the best ranking function for pure generative planning is not necessarily the best ranking function for multi-reuse planning. The surprising result in the reuse scenarios is that the single-goal case library performed better than larger case libraries consisting of solutions to multi-goal problems.     

A survey on coverage path planning for robotics

Coverage Path Planning (CPP) is the task of determining a path that passes over all points of an area or volume of interest while avoiding obstacles. This task is integral to many robotic applications, such as vacuum cleaning robots, painter robots, autonomous underwater vehicles creating image mosaics, demining robots, lawn mowers, automated harvesters, window cleaners and inspection of complex structures, just to name a few. A considerable body of research has addressed the CPP problem. However, no updated surveys on CPP reflecting recent advances in the field have been presented in the past ten years. In this paper, we present a review of the most successful CPP methods, focusing on the achievements made in the past decade. Furthermore, we discuss reported field applications of the described CPP methods. This work aims to become a starting point for researchers who are initiating their endeavors in CPP. Likewise, this work aims to present a comprehensive review of the recent breakthroughs in the field, providing links to the most interesting and successful works.     

Complete and Rapid Regrasp Planning with Look-up Table

A pick-and-place operation in a 3-dimensional environment is a basic operation for humans and multi-purpose manipulators. However, there may be a difficult problem for such manipulators. Especially, if the object cannot be moved with a single grasp, regrasping, which can be a time-consuming process, should be carried out. Regrasping, given initial and final poses of the target object, is a construction of sequential transition of object poses that are compatible with two poses in the point of grasp configuration. This paper presents a novel approach for solving the regrasp problem. The approach consists of a preprocessing and a planning stage. Preprocessing, which is done only once for a given robot, generates a look-up table which has information on kinematically feasible task space of the end-effector throughout the entire workspace. Then, using this table, the planning automatically determines a possible intermediate location, pose and regrasp sequence leading from the pick-up to put-down grasp. With a redundant robot, it is shown experimentally that the presented method is complete in the entire workspace and can be implemented in real-time applications due to rapid regrasp planning time. The regrasp planner was combined with an existing path.     

A privacy-preserving model for multi-agent propositional planning

Abstract

Over the last years, the planning community has formalised several models and approaches to multi-agent (MA) propositional planning. One of the main motivations in MA planning is that some or all agents have private knowledge that cannot be communicated to other agents during the planning process and the plan execution. We argue that the existing models of the multi-agent planning task do not maintain the agents’ privacy when a (strict) subset of the involved agents share confidential knowledge, or when the identity/existence of at least one agent is confidential. In this paper, first we propose a model of the MA-planning tasks that preserves the privacy of the involved agents when this happens. Then we investigate an algorithm based on best first search for our model that uses some new heuristics providing a trade-off between accuracy and agents’ privacy. Finally, an experimental study compares the effectiveness of using the proposed heuristics.     

Successful strategic information systems planning

Abstract. Strategic information systems planning (SISP) is the process of ensuring alignment between business plans and objectives and information systems plans and objectives and/or the process of identifying information systems which will provide the organization with a competitive edge. This paper distinguishes SISP from both tactical and operational information systems planning, discusses the reasons for management concern with SISP, summarizes problems which may be encountered with the planning process and resultant plant implementation and then focuses on the issue of SISP success. Specifically, success is considered not only in terms of how it may be achieved but also in terms of how it can be defined (i.e. what constitutes SISP success) and how SISP success can be measured. Specific research questions addressing the measurement of SISP success are identified.     

不确定可逆规划的强循环规划解

动作的执行在理想情况下是确定的,但现实生活中常常因为意外情况的发生而造成了不确定性,并产生不利影响.针对这种情况,建立了一种新的不确定规划模型,在不确定规划中增加了两个约束:1)所有动作的执行是可逆的;2)若一个状态在理想情况下不能达到目标,那么它不能企图在执行一个动作时发生意外而接近或达到目标.在该模型下设计了求解强循环规划的算法,首先只考虑所有动作的执行是在理想情况下发生的,这时可以将规划子图转换为规划子树并求出规划子树中每个状态的可达性;接下来考虑所有动作执行意外的情况,若动作被意外执行之后不能到达目标状态,则删除这个动作并更新规划子图和规划子树,最后通过遍历规划子图和规划子树求强循环规划解.考虑到有些意外的发生并不可预知,该算法能够在意外发生时只对部分失效的规划解进行更新而不需要重新求规划解.实验结果证明该算法能够快速地更新规划解且与问题的规模大小无关.     

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.     

Optimum motion planning in joint space for robots using genetic algorithms

The optimum motion planning in joint space (OMPJS) for robots, which generally consists of two subproblems, optimum path planning and optimum trajectory planning, was considered as a whole in the paper. A new method for optimum motion planning problem based on an improved genetic algorithm is proposed, which is more general, flexible and effective. This approach incorporates kinematics constraints, dynamics constraints, and control constraints of robotic manipulator. The simulation results for a two and a three degrees of freedom robots are presented and discussed. The simulations are based on genetic algorithm class library WGAClass 1.0 developed by us with Borland C++ 3.1.     

过度规划、部分可满足规划及简单偏好综述*

智能规划是人工智能的重要分支,它从某个特定问题的初始状态出发,寻找达到解决该问题的目标状态的动作序列,但是在许多真实问题中,往往没有足够的资源来实现所有目标。因此,智能规划中对软目标的研究包括过度规划、部分可满足规划和简单偏好,已成为近年来的热点。将对过度规划、部分可满足规划和简单偏好的主要研究成果进行综述,包括这几类问题的差异比较、主要解决方案以及对将来的展望。