基于因果图启发式的并行概率规划求解

并行概率规划(PPP)是近年来智能规划领域中的研究热点。在该类问题中,动作具有并发性和不确定性,非常贴近现实问题。然而,现有的两种针对PPP的主要求解方法都有明显的缺点。一种基于模拟抽样,以规划器PROST为代表,但求解速度慢;另一种基于迭代深化,以规划器Glutton为代表,但求解质量差。因此,我们尝试使用高效的启发式搜索方法来求解这类问题。目前,因果图启发式(CGH)是启发式规划方法中的佼佼者。考虑到PPP问题采用RDDL语言来描述,其中的条件概率函数(CPF)非常适合用于构建因果图(CG),所以我们引入因果图来对基于RDDL描述的PPP问题进行启发式求解。本文的主要启发式算法称为CGH_RDDL,整体求解方法是使用rddlsim模拟状态演化以及用CGH_RDDL引导搜索。具体做法是：先从领域描述构建出因果图及领域转换图(DTG);然后根据CG和DTG,计算单个状态变量任意一对取值间的转换代价;接着在rddlsim的模拟演化过程中,由CGH_RDDL推送具有最佳估值的后继状态,其中状态的启发值定义为状态轨迹的转换代价和立即回报值的总和;最后累加在限定轮数内rddlsim状态演化的回报值,即为最终的求解质量。在PPP基准领域上的实验结果表明,在不允许手工干预和参数调整的前提下,本文方法的求解效果要好于PROST和Glutton。更进一步地,与其它的基本启发式相比,CGH_RDDL的求解质量高于随机搜索,求解速度快于爬山法。这表明在经典规划领域中高效的启发式搜索策略可扩展去求解这一类非经典规划问题。而非经典规划问题更具有现实意义和应用前景,更值得探讨先进的规划方法去求解它们。     

Markov决策过程的蚁群规划算法

在智能规划问题上,寻找规划解都是NP甚至NP完全问题,如果动作的执行效果带有不确定性,如在Markov决策过程的规划问题中,规划的求解将会更加困难,现有的Markov决策过程的规划算法往往用一个整体状态节点来描述某个动作的实际执行效果,试图回避状态内部的复杂性,而现实中的大量动作往往都会产生多个命题效果,对应多个命题节点。为了能够处理和解决这个问题,提出了映像动作,映像路节和映像规划图等概念,并在其基础上提出了Markov决策过程的蚁群规划算法,从而解决了这一问题。并且证明了算法得到的解,即使在不确定的执行环境下,也具有不低于一定概率的可靠性。     

智能规划器StepByStep的研究和开发

智能规划器是智能规划研究成果的重要表现形式,规划器的求解效率和规划质量是智能规划理论研究的直接反映.首先介绍智能规划器的一般结构和StepByStep规划器的总体结构,然后详细阐述StepByStep规划器各组成部分所采用的方法和策略,定义谓词知识树来提取领域知识.在谓词知识树的基础上定义谓词规划树,并用各种策略来提高规划树的生成效率.在谓词规划树的基础上设计StepByStep的规划策略,最后用8个规划器对3个具有代表性的基准规划领域及其规划问题进行实际的求解实验,分析了StepByStep规划器在求解效率和规划质量上的具体表现.实验数据表明,StepByStep规划器的规划策略对3个不同规划领域都具有很好的指导作用,验证了领域知识在规划求解过程中的实际价值.     

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...     

命题编码中公理的组合与设计

近年来,基于可满足性的规划方法研究逐渐成为智能规划研究领域中的热点。提出3种基于Graphplan的编码方式中公理的改进:动作互斥的部分放松、动作互斥的完全放松方法、添加框架公理。基于SATPLAN2006规划系统分别实现上述3种改进的编码方式,并对国际规划竞赛中选用的标准后勤域与积木世界域的问题样例予以测试,分析不同编码方式的编码规模与求解效率,验证了基于Graphplan编码方式的改进在绝大多数情况下是有效的。最后,实现基于状态的编码方式,并对上述两个域进行测试,比较约简动作与约简状态这两种极端方式的求解效率和编码规模。实验结果表明,在后勤域的某些问题上基于状态的编码方式比基于动作的编码方式有效得多。上述的改进策略表明,可根据问题域的特性等来考虑该问题最适宜哪些公理组合的编码方式,而不固定使用某种特定的编码方式。     

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.     

基于启发式搜索的灵活规划的算法研究与系统实现

随着智能规划研究的深入,经典规划已不能满足实际应用的需要.本文分析了经典规划无法满足实际应用要求及产生灵活规划的原因.在对启发式搜索和灵活规划深入研究的基础上,提出了利用启发式搜索的方法来处理灵活规划问题的思想,并给出了基于启发式搜索的灵活规划算法和求解模型.采用智能规划中的基准问题对该算法进行测试,实验表明该方法在处理很多领域问题上都可以得到非常好的效果.     

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。     

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.     

自动获取派生谓词规划领域的通用规划

通用规划(解)是针对某个领域的像算法一样的规划解,通过对其的解释可以直接得出具体问题的规划解,而不需要调用任何规划系统.但是目前通用规划的提取只能在一些简单或者特殊的领域中进行,没有推广到复杂或者一般的规划领域.该文提出在包含派生谓词的规划领域自动获取通用规划的方法.与已有获取方法不同的是:首先,基于派生谓词规则,文中方法明确指出派生谓词目标与动作效果之间的依赖关系,用以完善通用规划中动作应用的目的;其次,在提取过程中借助角色来帮助识别规划解中的循环结构.实验结果表明,文中方法不仅容易在派生谓词规划领域中获取通用规划,而且还能够以较好的性能求解一类以派生谓词为主要目标的规划"难"题.该文是在派生谓词规划领域中提取通用规划的首创性工作.     

A STUDY FOR THE APPLICATION OF AUTOMATED PLANNING TO MOBILE ASSISTIVE ROBOTS

In recent years, Automated Planning (AP) has experienced important advances. In this study we apply such advances to the field of Mobile Assistive Robots (MAR). In particular, we propose the use of AP to implement the deliberative step between observation and action execution in MAR. First, we analyze the requirements that allow a MAR to plan navigation and manipulation actions in near real time. The intention is to build the foundation for a planning module within the Simultaneous User Learning and TAsk executioN (SULTAN) architecture, allowing a MAR to perform Daily Life Activities (DLA) in humanlike environments. Second, we apply AP techniques in fully observable, deterministic and static simulated environments with a single MAR. In addition, we analyze and compare the best available satisficing automated planners. The selected planners participate in several experiments to obtain plans for a Planning Domain Definition Language (PDDL) based on the Tidybot domain. Finally, in order to know how competitive the selected planners are, we compare the experimental results in detail.     

Planning and execution through variable resolution planning

Generating sequences of actions–plans–for robots using Automated Planning in stochastic and dynamic environments has been shown to be a difficult task with high computational complexity. These plans are composed of actions whose execution might fail due to different reasons. In many cases, if the execution of an action fails, it prevents the execution of some (or all) of the remainder actions in the plan. Therefore, in most real-world scenarios computing a complete and sound (valid) plan at each (re-)planning step is not worth the computational resources and time required to generate the plan. This is specially true given the high probability of plan execution failure. Besides, in many real-world environments, plans must be generated fast, both at the start of the execution and after every execution failure. In this paper, we present Variable Resolution Planning which uses Automated Planning to quickly compute a reasonable (not necessarily sound) plan. Our approach computes an abstract representation–removing some information from the planning task–which is used once a search depth of $k$ steps has been reached. Thus, our approach generates a plan where the first $k$ actions are applicable if the domain is stationary and deterministic, while the rest of the plan might not be necessarily applicable. The advantages of this approach are that it: is faster than regular full-fledged planning (both in the probabilistic or deterministic settings); does not spend much time on the far future actions that probably will not be executed, since in most cases it will need to replan before executing the end of the plan; and takes into account some information of the far future, as an improvement over pure reactive systems. We present experimental results on different robotics domains that simulate tasks on stochastic environments.     

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.     

An Approach for Merging Plans Hierarchically in Decomposable Domains

Recent works in domain‐independent plan merging have shown that the optimal plan‐merging problem is NP-hard, and heuristic algorithms have been proposed to generate near‐optimal solutions. These works have shown heuristic algorithms which assume that the mergeability of two actions can be determined by considering only the actions themselves. In this paper, we show that it is often that case that the surrounding actions in the plan must also be considered when determining the mergeability of two or more actions; therefore, the context in which the actions are used affects their mergeability. To address this problem, we have developed a plan-merging methodology that merges partial-order plans based on the the notion of plan fragments, which encapsulate actions with the context in which they are being utilized. This methodology applies to a class of planning domains, called decomposable domains, which consist of actions that follow all or some portion of a type sequence. Merging is performed hierarchically by action type. We also investigate the previously unexplored notion of alternative actions in domain-independent plan merging, which can improve the quality of the resulting merged plan. A novel approach is presented for removing cyclic dependencies that may occur during the plan-merging process.
A key part of the methodology is the computation of a minimum cost cover of plan fragments. We provide theoretical analyses of two optimal algorithms and a greedy-based algorithm for computing the minimum cost cover. We support the theoretical analysis of these algorithms with experimental data to show that the greedy approach is near-optimal and efficient.     

An architecture for a self-improving instructional planner for intelligent tutoring systems

Machine instructional planners use changing and uncertain data to incrementally configure plans and control the execution and dynamic refinement of these plans. Current instructional planners cannot adequately plan, replan, and monitor the delivery of instruction. This is due in part to the fact that current instructional planners are incapable of planning in a global context, developing competing plans in parallel, monitoring their planning behavior, and dynamically adapting their control behavior. In response to these and other deficiencies of instructional planners a generic system architecture based on the blackboard model was implemented. This self-improving instructional planner (SUP) dynamically creates instructional plans, requests execution of these plans, replans, and improves its planning behavior based on a student's responses to tutoring. Global planning was facilitated by explicitly representing decisions about past, current, and future plans on a global data structure called the plan blackboard. Planning in multiple worlds is facilitated by labeling plan decisions by the context in which they were generated. Plan monitoring was implemented as a set of monitoring knowledge sources. The flexible control capability for instructional planner was adapted from the blackboard architecture BB1. The explicit control structure of SUP enabled complex and flexible planning behavior while maintaining a simple planning architecture.     

IPSS: A Hybrid Approach to Planning and Scheduling Integration

Recently, the areas of planning and scheduling in artificial intelligence (AI) have witnessed a big push toward their integration in order to solve complex problems. These problems require both reasoning on which actions are to be performed as well as their precedence constraints (planning) and the reasoning with respect to temporal constraints (e.g., duration, precedence, and deadline); those actions should satisfy the resources they use (scheduling). This paper describes IPSS (integrated planning and scheduling system), a domain independent solver that integrates an AI planner that synthesizes courses of actions with constraint-based techniques that reason based upon time and resources. IPSS is able to manage not only simple precedence constraints, but also more complex temporal requirements (as the Allen primitives) and multicapacity resource usage/consumption. The solver is evaluated against a set of problems characterized by the use of multiple agents (or multiple resources) that have to perform tasks with some temporal restrictions in the order of the tasks or some constraints in the availability of the resources. Experiments show how the integrated reasoning approach improves plan parallelism and gains better makespans than some state-of-the-art planners where multiple agents are represented as additional fluents in the problem operators. It also shows that IPSS is suitable for solving real domains (i.e., workflow problems) because it is able to impose temporal windows on the goals or set a maximum makespan, features that most of the planners do not yet incorporate