基于Conformant Fast-Forward规划系统的析取目标处理方法

将规划系统Conformant Fast-Forward从单目标规划扩展到基于析取目标的不确定规划,设计并实现了新的规划系统Conformant-FF-d.Conformant-FF-d的新功能主要包括:目标状态判断、可达性分析和启发函数.提出一种利用SAT技术进行目标状态判断的高效方法;提出析取目标条件下信念状态的可达性分析方法,有效地删除无法到达目标的信念状态,进而缩小了搜索空间的规模;设计了适用于析取目标的启发函数,有效地指导搜索算法向更有希望到达目标的方向进行.在国际规划竞赛的问题域上对Conformant-FF-d和先进的规划系统POND进行测试和对比分析,实验结果表明:ConformantFF-d的求解效率高而且具有较好的可扩展性.     

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.     

路标计数启发式引导的分解规划方法

路标信息能够准确描述智能规划问题解空间的基本形态.提出由路标信息引导的分解规划方法,求解过程由路标计数启发式引导增强爬山算法向目标方向进行,根据路标的完成情况分段求出规划解.从全局范围上看,爬山过程逐渐实现更多的路标,路标计数启发式估值的降低引发规划任务的分解,当搜索过程遇到估值更低的状态时,提取一段爬山路径.如此反复执行“搜索-提取”过程,直至路标计数启发式的估值降低为0,各段爬山路径构成最终的规划解.采用最新国际通用的标准测试问题进行实验测试,结果表明:由路标计数启发式引导的分解规划方法能够更好地发挥路标信息的优势,实现了搜索范围的压缩,可更快地生成规划解.     

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.     

基于模态逻辑D公理系统的Conformant规划方法

2006年,conformant规划问题成为国际规划竞赛不确定性问题域中的标准测试问题,得到研究人员的广泛关注.目前,conformant规划系统都是将其看成信念状态空间上的启发式搜索问题予以求解.通过分析conformant规划问题的语法和语义,提出新的基于模态逻辑的规划框架.将其转换为模态逻辑D公理系统的一系列定理证明问题.提出2种基于模态逻辑的编码方式.构造相应的公理与推理规则形成模态公式集,保证对于D系统的定理证明过程等同于原问题的规划过程.并通过问题实例验证该方法的有效性.继基于SAT、CSP、线性规划、模型检测等求解技术的规划方法后,该规划框架是基于转换的规划方法的一种新的尝试.     

基于模型检测的领域约束规划

基于模型检测的智能规划是当今通用的智能规划研究的热点,其求解效率比较高.但是,目前基于模型检测的智能规划系统没有考虑到利用领域知识来提高描述能力和求解效率.为此,研究了增加领域约束的基于模型检测的智能规划方法,并据此建立了基于模型检测的领域约束规划系统DCIPS(domain constraints integrated planning system).它主要考虑了领域知识在规划中的应用,将领域知识表示为领域约束添加到规划系统中.根据"规划=动作+状态",DCIPS将领域约束分为3种,即对象约束、过程约束和时序约束,采用对象约束来表达状态中对象之间的关系,采用过程约束来表达动作之间的关系,采用时序约束表达动作与状态中对象之间的关系.通过在2002年智能规划大赛AIPS 2002上关于交通运输领域的3个例子的测试,实验结果表明,利用领域约束的DCIPS可以方便地增加领域知识,更加实用化,其效率也有了相应的提高.     

一种计算动作派生前提的激活集的改进方法

动作的派生前提和动作删除效果的"连锁反应"是处理派生规划问题中的难点问题,基于激活集的方法是一种简单、有效的方法,但是激活集的计算时间往往过多,文中提出一种新的方法来计算激活集.LPG-td规划系统所提出的激活集是与状态有关的并且需要在规则图上反复计算,而文中提出的激活集是与状态无关的,通过规则分裂来对规则集进行"基化",使得寻找激活集的时间逐渐地由指数级降为线性级.实现了一个新的能够处理派生规划问题的规划系统LPGSIAS,通过对基准问题的求解,表明LPGSIAS比LPG-td在大部分情况下更高效.与状态无关的激活集可以方便地转化为与状态有关的激活集,文中通过提出一种求解与状态无关的激活集的改进方法来加快对派生规划问题的求解速度.     

一种高质量的领域无关前向规划剪枝策略

前向启发式搜索和放宽规划方法被很多领域无关的规划器所采用,被认为是一种有效的规划范型.FF规划器利用放宽规划图计算状态的启发式估值,并提取有利动作集合进行前向搜索的剪枝.但过大的有利动作集合造成了过多的消耗.文中提出了一种新的高质量的领域无关剪枝策略.该策略根据放宽规划图的动作层和命题层之间的关系,提取出所谓的直接效用动作集合,此集合之外的其它动作都被剪枝.直接效用动作集合比FF的有利动作集合更加精简,更具启发性,能指导前向搜索集中在那些离目标更近的状态.根据直接效用动作作者开发了一种新的lookahead搜索邻居,并应用在改进后的增强型爬山搜索算法中,使得前向搜索具备良好的前瞻性.当增强型爬山法失败时,采取一种从局部极小值重启完备搜索的策略以保持系统完备性.通过对国际规划大赛基准问题的测试表明,基于该剪枝策略及前向搜索算法实现的前向规划系统有效地缩小了搜索空间,搜索的节点数目比FF的有利动作策略明显要少,搜索效率有显著的提升.     

动态不确定环境下多目标路径规划方法

提出一种在动态不确定环境下求解多目标问题时快速调整移动路径的方法.首先提出采用逆向多目标启发式搜索进行全局规划,求解问题的最优路径集合;然后提出动态多目标路径规划方法,先根据当前观测进行全局规划,在移动过程中探测到不一致的环境信息时,通过对先前搜索中部分信息的重用,在全局规划的基础上进行增量重规划,调整当前状态与目标状...     

基于改进RRT算法的无人机三维避障规划

RRT算法由于其在复杂环境中有强大的随机搜索能力,在无人机避障规划中被广泛运用.为了提高无人机避障规划的效率,提出了一种基于预规划路径优化RRT算法的无人机三维避障规划算法.算法首先在障碍物膨胀规则和相交规则下生成预规划路径,然后将预规划路径看做成连续的质点组成,按一定的扩展树步长的比例从连续质点取点来确定搜索树的随机状态点,最后RRT算法在这些随机状态点的引导下进行搜索,生成避障规划路径.仿真结果表明,改进的RRT算法生成的预规划路径降低了障碍物搜索的时间和增强了搜索树扩展的方向性;预先确定的随机状态点使搜索树在扩展中具有方向性,可减少新生节点的个数和路径长度,进而提高了无人机避障路径规划的效率,使得最终生成避障路径的时间更优.     

Fast forward planning by guided enforced hill climbing

In recent years, a number of new heuristic search methods have been developed in the field of automated planning. Enforced hill climbing (EHC) is one such method which has been frequently used in a number of AI planning systems. Despite certain weaknesses, such as getting trapped in dead-ends in some domains, this method is more competitive than several other methods in many planning domains. In order to enhance the efficiency of ordinary enforced hill climbing, a new form of enforced hill climbing, called guided enforced hill climbing, is introduced in this paper. An adaptive branch ordering function is the main feature that guided enforced hill climbing has added to EHC. Guided enforced hill climbing expands successor states in the order recommended by the ordering function. Our experimental results in several planning domains show a significant improvement in the efficiency of the enforced hill climbing method, especially when applied to larger problems.     

Parallel planning via the distribution of operators

This paper describes Operator Distribution Method for parallel Planning (ODMP), a parallelization method for efficient heuristic planning. The method innovates in that it parallelizes the application of the available operators to the current state and the evaluation of the successor states using the heuristic function. In order to achieve better load balancing and a lift in the scalability of the algorithm, the operator set is initially enlarged, by grounding the first argument of each operator. Additional load balancing is achieved through the reordering of the operator set, based on the expected amount of imposed work. ODMP is effective for heuristic planners, but it can be applied to planners that embody other search strategies as well. It has been applied to GRT, a domain-independent heuristic planner, and CL, a heuristic planner for simple logistics problems, and has been thoroughly tested on a set of logistics problems adopted from the AIPS-98 planning competition, giving quite promising results.     

Conformant planning via heuristic forward search: A new approach

Conformant planning is the task of generating plans given uncertainty about the initial state and action effects, and without any sensing capabilities during plan execution. The plan should be successful regardless of which particular initial world we start from. It is well known that conformant planning can be transformed into a search problem in belief space, the space whose elements are sets of possible worlds. We introduce a new representation of that search space, replacing the need to store sets of possible worlds with a need to reason about the effects of action sequences. The reasoning is done by implication tests on propositional formulas in conjunctive normal form (CNF) that capture the action sequence semantics. Based on this approach, we extend the classical heuristic forward-search planning system FF to the conformant setting. The key to this extension is an appropriate extension of the relaxation that underlies FF's heuristic function, and of FF's machinery for solving relaxed planning problems: the extended machinery includes a stronger form of the CNF implication tests that we use to reason about the effects of action sequences. Our experimental evaluation shows the resulting planning system to be superior to the state-of-the-art conformant planners MBP, KACMBP, and GPT in a variety of benchmark domains.     

State agnostic planning graphs: deterministic,non-deterministic,and probabilistic planning

Planning graphs have been shown to be a rich source of heuristic information for many kinds of planners. In many cases, planners must compute a planning graph for each element of a set of states, and the naive technique enumerates the graphs individually. This is equivalent to solving a multiple-source shortest path problem by iterating a single-source algorithm over each source.We introduce a data-structure, the state agnostic planning graph, that directly solves the multiple-source problem for the relaxation introduced by planning graphs. The technique can also be characterized as exploiting the overlap present in sets of planning graphs. For the purpose of exposition, we first present the technique in deterministic (classical) planning to capture a set of planning graphs used in forward chaining search. A more prominent application of this technique is in conformant and conditional planning (i.e., search in belief state space), where each search node utilizes a set of planning graphs; an optimization to exploit state overlap between belief states collapses the set of sets of planning graphs to a single set. We describe another extension in conformant probabilistic planning that reuses planning graph samples of probabilistic action outcomes across search nodes to otherwise curb the inherent prediction cost associated with handling probabilistic actions. Finally, we show how to extract a state agnostic relaxed plan that implicitly solves the relaxed planning problem in each of the planning graphs represented by the state agnostic planning graph and reduces each heuristic evaluation to counting the relevant actions in the state agnostic relaxed plan. Our experimental evaluation (using many existing International Planning Competition problems from classical and non-deterministic conformant tracks) quantifies each of these performance boosts, and demonstrates that heuristic belief state space progression planning using our technique is competitive with the state of the art.     

模式数据库在智能规划中的应用

该规划器通过对智能规划领域里传统的构造模式数据库的方法进行改进,从而改进模式数据库启发式的效率：通过分析和移除一些在实际问题空间里不可能存在对应的完整状态的模式有效地减少了模式数据库的构造时间,并提高了模式数据库启发值的紧致性,使得模式数据库启发式能更好的指导搜索算法以求得问题的最优解。该规划器在linux系统下设计,通过使用规划器解决积木世界领域的规划问题来研究改进前后模式数据库启发式在搜索过程中所起的作用。     

Sequential Monte Carlo in reachability heuristics for probabilistic planning

Some of the current best conformant probabilistic planners focus on finding a fixed length plan with maximal probability. While these approaches can find optimal solutions, they often do not scale for large problems or plan lengths. As has been shown in classical planning, heuristic search outperforms bounded length search (especially when an appropriate plan length is not given a priori). The problem with applying heuristic search in probabilistic planning is that effective heuristics are as yet lacking.In this work, we apply heuristic search to conformant probabilistic planning by adapting planning graph heuristics developed for non-deterministic planning. We evaluate a straight-forward application of these planning graph techniques, which amounts to exactly computing a distribution over many relaxed planning graphs (one planning graph for each joint outcome of uncertain actions at each time step). Computing this distribution is costly, so we apply Sequential Monte Carlo (SMC) to approximate it. One important issue that we explore in this work is how to automatically determine the number of samples required for effective heuristic computation. We empirically demonstrate on several domains how our efficient, but sometimes suboptimal, approach enables our planner to solve much larger problems than an existing optimal bounded length probabilistic planner and still find reasonable quality solutions.     

A case-based approach to heuristic planning

Most of the great success of heuristic search as an approach to AI Planning is due to the right design of domain-independent heuristics. Although many heuristic planners perform reasonably well, the computational cost of computing the heuristic function in every search node is very high, causing the planner to scale poorly when increasing the size of the planning tasks. For tackling this problem, planners can incorporate additional domain-dependent heuristics in order to improve their performance. Learning-based planners try to automatically acquire these domain-dependent heuristics using previous solved problems. In this work, we present a case-based reasoning approach that learns abstracted state transitions that serve as domain control knowledge for improving the planning process. The recommendations from the retrieved cases are used as guidance for pruning or ordering nodes in different heuristic search algorithms applied to planning tasks. We show that the CBR guidance is appropriate for a considerable number of planning benchmarks.     

Landmark-based heuristic online contingent planning

In contingent planning problems, agents have partial information about their state and use sensing actions to learn the value of some variables. When sensing and actuation are separated, plans for such problems can often be viewed as a tree of sensing actions, separated by conformant plans consisting of non-sensing actions that enable the execution of the next sensing action. We propose a heuristic, online method for contingent planning which focuses on identifying the next useful sensing action. We select the next sensing action based on a landmark heuristic, adapted from classical planning. We discuss landmarks for plan trees, providing several alternative definitions and discussing their merits. The key part of our planner is the novel landmarks-based heuristic, together with a projection method that uses classical planning to solve the intermediate conformant planning problems. The resulting heuristic contingent planner solves many more problems than state-of-the-art, translation-based online contingent planners, and in most cases, much faster, up to 3 times faster on simple problems, and 200 times faster on non-simple domains.     

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。