基于路标隐式分解的前向搜索规划方法

提出一种利用路标信息隐式分解前向搜索过程的规划算法。以路标计数启发式估值的降低作为分界点,将规划任务分解成多个规模更小的子任务,当访问到估值更低的状态时,表明搜索过程完成一个子任务的求解,反复执行这一过程直到路标计数启发式估值降低为零。与其它将路标具体指定为中间目标的分解方法相比,基于路标计数启发式的隐式分解方法能指导前向搜索过程快速向目标方向推进,实现搜索空间的大规模压缩,在求解效率和规划解质量上都有较大提高。     

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.     

基于定向爬山的遗传算法

爬山法是一种局部搜索能力相当好的算法,主要是因为它是通过个体的优劣信息来引导搜索的。而传统的遗传算法作为一种全局搜索算法,在搜索过程中却没有考虑个体间的信息,而仅依靠个体适应度来引导搜索,使得算法的收敛性受到限制。将定向爬山机制应用于遗传算法,提出了一种基于定向爬山的遗传算法（OHCGA）。该算法结合了爬山法与遗传算法的优点,通过比较个体的优劣,使用定向爬山操作引导算法向更优秀的解区域进行搜索。实验结果表明,与传统遗传算法（TGA）相比,OHCGA较大地提高了算法的收敛速度和搜索最优解的能力。     

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.     

基于多启发式信息融合的攻击路径发现算法研究

攻击路径发现对于提高信息系统安全具有重要意义,传统攻击路径发现技术存在考虑因素有限以及可扩展性不高的问题,导致其在网络攻击复杂化和网络规模扩大化的趋势下应用价值有限。针对该问题,本文提出一种基于多启发式信息融合的攻击路径发现算法,该算法结合攻击路径发现背景知识,将漏洞威胁程度,漏洞成功率以及主机资产作为启发式函数计算依据引导攻击路径搜索,达到减少搜索范围、提高路径可用性的目的;并且基于SMHA^*（Share Multi-Heuristic A^*,SMHA^*）框架实现多种启发式信息融合,共同引导攻击路径搜索。通过与现有规划算法进行对比实验,验证了本算法能够更加灵活而全面地考虑攻击路径发现中的现实因素,且规划效率也能够满足实际需求,能够有效提高规划结果的可行性以及应用价值。     

束搜索算法的精度优化研究

启发式爬山搜索算法能高效地实现搜索剪枝,求解实际问题时,能在庞大的假设空间中,找到最优或近似最优解,束搜索算法保持了爬山搜索算法的高效剪枝特性,同时能有效减小爬山搜索收敛到局部最优解的风险,人工智能领域广泛采用束搜索策略。对于宽度为k的束搜索,由于只维持有限的k条搜索路径,提高束搜索算法的搜索精度,重在这k条路径的选择上,但如何选取这k条路径,作者未见描述相关方法的资料,目前多数算法是在每一搜索步选取具有最大启发式性能量度值的k个候选作为进一步搜索的入口。论文简要讨论了束搜索算法,提出了几种合理的候选选取方法以及避免单亲填满的思想,并在UCI测试数据库上进行了对比实验,给出了实验结果。     

面向对象的自主车越野路径规划的设计和实现

本文介绍了一种使用启发式估值函数进行二次搜索的路径规划方法，设计和实现了自主车的越野路径规划。首先是在领域式规划空间上进行领域式搜索，由于该空间是一种面向地形本身分布特点和性质的地质分割，解路径的粒度较粗，因此又进行了在栅格规划空间上的第二次搜索－栅格搜索，以对解路径进行细化，最佳结点只在三个方向上扩展，并取代价最小者。     

需求可拆分校车路径问题的元启发式算法

校车路径规划中,允许站点乘车需求拆分通常能有效地降低校车服务成本。将该问题定义为需求可拆分校车路径问题(SDSBRP)进行求解。由于校车服务中要顾及学生最大乘车时间,且优化目标要兼顾所需校车数量和校车行驶距离,经典SDVRP算法难以直接应用于SDSBRP。因此分析了该问题的解特征,首次构建双目标SDSBRP数学模型,并首次设计针对该问题的元启发式求解算法。该算法首先构造初始可行解,然后在模拟退火算法框架下,引入站点需求拆分的邻域搜索算子进行迭代搜索,逐步改善解的质量。邻域搜索中,设计了多目标问题的邻域接受准则来引导邻域解的搜索方向,并引入破坏重建机制来增加解的多样性。使用已有的测试案例集和改造的测试案例进行算法测试,实验结果表明所提算法收敛性好,能够显著降低校车服务成本。     

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.     

图最小线性排序问题的Memetic爬山算法

针对图最小线性排序问题优化目标的特性及其可行域总是连通的特点,提出了一个新型的Memetic爬山算法。在Memetic算法框架及其主要算子内部流程中同时结合爬山法,并在主要算子内部采用迂回爬山策略。设计可变型顶点-边-邻接交叉算子,改进使用基于贪心随机自适应搜索过程的初始解生成算法,采用动态更新等保持种群多样性策略。公认测试集的实验结果表明,与最近的两阶段模拟退火算法(two-stage simulated annealing,TSSA)和分散搜索与路径重链接算法(scatter search and path relinking,SSPR)相比,该算法具有更好的整体性能。在相近平均运行时间内,该算法近优解质量分别平均提高1.6%和2.01%,21个测试例子中13个获得当时最好的近优解,比TSSA算法多出4个,比SSPR算法多出2个。     

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.     

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.     

基于静态前提的谓词知识树分解策略

智能规划问题实质是一种搜索问题,通常需采用某种策略来缩小搜索空间,提高规划效率。在“以谓词为主体”的规划求解方法中,规划树的生成效率将直接影响规划求解效率。为此,提出了基于静态前提的谓词知识树分解策略,并给出了相应的分解算法。对任意一个规划领域,利用该分解算法可将知识树分解成若干个较小规模的知识子树。在规划求解的过程中,利用知识子树可有效地减少搜索空间,从而快速生成规划树,提高规划效率。同时,利用知识子树还可提取出隐含在动作描述中的领域知识。实验结果表明该分解算法是有效的。     

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。     

An evolutionary algorithm with guided mutation for the maximum clique problem

Estimation of distribution algorithms sample new solutions (offspring) from a probability model which characterizes the distribution of promising solutions in the search space at each generation. The location information of solutions found so far (i.e., the actual positions of these solutions in the search space) is not directly used for generating offspring in most existing estimation of distribution algorithms. This paper introduces a new operator, called guided mutation. Guided mutation generates offspring through combination of global statistical information and the location information of solutions found so far. An evolutionary algorithm with guided mutation (EA/G) for the maximum clique problem is proposed in this paper. Besides guided mutation, EA/G adopts a strategy for searching different search areas in different search phases. Marchiori's heuristic is applied to each new solution to produce a maximal clique in EA/G. Experimental results show that EA/G outperforms the heuristic genetic algorithm of Marchiori (the best evolutionary algorithm reported so far) and a MIMIC algorithm on DIMACS benchmark graphs.     

On the importance of breaking ties in the relaxed plan heuristic

The relaxed plan heuristic is a domain-independent heuristic for automated planning that computes an estimate of the cost for achieving the goals from a given state. This heuristic is based on the idea of solving a relaxed version of the planning task. Due to the great size of the state space, most heuristic search algorithms in planning suffer from scalability problems. These algorithms have to evaluate a great amount of states, and the time devoted to heuristic evaluations is one of the causes of the scalability problems. We argue that one way to lighten this problem is breaking ties in the heuristic value using additional information computed during the relaxed plan construction. We add a complementary value to the heuristic, allowing algorithms to discriminate between states with relaxed plans of the same length but with a different difficulty. The experimental evaluation in some planning benchmarks shows that the modification to the original heuristic can reduce the number of evaluated nodes for the most common algorithms used in heuristic planning.