基于整数规划的几何因果定性推理建模和求解

因果定性推理是一种通过分析，描述物理系统行为和关系的约束，找出系统内部各个成分之间的因果结构的推理方法，本文提出了一种基于整数 几何因果定性分析模型和算法，该方法在产品设计中有广泛的应用，利用这个模型和算法可以较好地解决参数化设计中的几何推理问题，还可以用作概念设计的工具，用于完成复杂系统设计任务的划分以及定序、设计变量之间相互依赖关系分析等工作，算法具有约束处理能力强、应用范围广、求解效率和稳定     

一种因果推理形式

针对定性推理在有冲突情况下进行传播时所存在的问题，本文提出了一种因果关系形式，引入了“时间”和“效果”概念，给出了因果推理过程：先建立因果关系图，再传播原因变量的行为，并应用定性代数方法来处理冲突问题．这种因果推理形式推广了Ｉｗａｓａｋｉ因果推理方法.     

多值因果图的推理算法研究

针对多值因果图存在的两个困难：（1）不严格满足概率论；（2）将其用于实际问题时，推理结果可能出现错误，提出了一种基于因果影响可能性分配的推理算法，该算法将多值因果图的推量分成3个阶段，首先对多值因果图进行补充定义，使多值因果图能够兼容单值因果图；接着将多值因果图转化为单值因果图进行概率计算，最后对多值因果图进行可能性计算，将单值因果图计算得到的概率按多值因果图计算得到的可能性进行分配，以核电站二回路系统中蒸汽发生器故障诊断因果图为例，展示了该算法推理计算的全过程，实例表明，该算法能够有效地克服多值因果图存在的困难，其推理过程严谨，计算结果符合实际情况，而前面提出的推理算法基础上，针对其不能处理模型情况的局限性，提出了一种模糊推理算法，该算法对多值因果图进行了模糊扩展定义，在读数变量和事件变量之间建立了用于表达模糊知识的模糊对应关系，在事件变量上定义了一个等价的虚拟模糊状态，使读数变量取值对应一个模糊状态，把读数和模糊推理转化为对应模糊状态的非模糊推理，通过本文的工作，目前因果图已发展成了一个能够处理离散变量和连续变量的混合因果图模型。     

基于几何推理的布局设计新方法

面向约束的布局设计问题一直是布局研究的热点．本文提出了一个基于几何推理的布局设计生成算法，其最大优点就是充分利用参数化设计的优点，建立了影响布局设计的变量之间的关系．该算法具有较强的柔性，易于扩充，并支持约束一致性检测和影响布局设计参变量的局部修改．算法已在作者研制的车身内布置设计系统中得到了较好的应用．     

多值因果图的一种推理算法

该文针对多值因果图存在的两个困难:不严格满足概率论;将其用于实际问题时,推理结果可能出现错误。提出了一种基于因果影响可能性分配的推理算法。该算法对多值因果图进行了补充定义,使多值因果图能够兼容单值因果图;定义了事件变量状态可能性,及其精确计算方法和近似计算方法;给出了将多值因果图转化为单值因果图的方法和步骤,并推导了相应的计算公式;给出了收到证据后,感兴趣变量状态的后验概率计算方法。以核电站二回路系统中蒸汽发生器故障诊断因果图为例,展示了该算法推理计算的全过程。实例表明,该算法能够有效地克服多值因果图存在的困难,其推理过程严谨,计算结果符合实际情况。     

结合方向关系和拓扑关系的约束满足推理

结合定性空间推理中的区域连接演算（RCC）和基于区域的主方向关系模型,应用拓扑和方向关系上的复合表,将方向关系和拓扑关系的推理看作约束满足问题（CSP）,给出了结合RCC8和主方向关系的约束满足问题推理算法,该算法可结合拓扑关系和方向关系进行推理。     

一种分层递阶的定性拓扑推理方法

文章针对现有的定性表示方法和拓扑推理算法存在的问题,提出了一种新的方法.首先,提出了基于概念邻域结构的定性表示方法.然后,给出了不同粒度层次上拓扑关系复合表的计算方法.最后,设计得出分层递阶的拓扑推理算法.文章给出的方法具有较高的认知合理性,所提出的推理算法可根据问题来选择合适的表示和推理层次,在已有推理算法给不出解的情况下,可以给出问题的合理解,对一般定性推理研究有参考价值.     

三维几何约束求解的自由度归约算法

三维几何约束求解在装配设计、几何造型和动力学分析等领域有着广泛的应用．在分析基本几何元素间的约束关系对刚体自由度状态影响的基础上,提出刚体自由度的归约算法,以求得满足约束后刚体的自由度状态空间;以刚体自由度状态空间分析为基础,实现对合理约束的推理求解和约束一致性维护,该算法解决了三维几何约束求解中自由度计算问题,同时避免了一些推理求解算法中出现的“组合爆炸”问题．     

机器推理的进展与展望

机器学习算法的发展仍受到泛化能力较弱、鲁棒性较差、缺乏可解释性等问题的限制.文中介绍机器推理,说明推理对于机器学习人的知识和逻辑、理解和解释世界的重要作用.首先分析人类大脑推理机制,从认知地图、神经元和奖赏回路,扩展到受脑启发的直觉推理、神经网络和强化学习.进而总结机器推理的方式及其相互关联的现状、进展及挑战,具体包括直觉推理、常识推理、因果推理和关系推理等.最后展望机器推理的应用前景与未来的研究方向.     

QUIOS的事件、进程和推理算法

本文讨论开放型物理系统的定性推理方法．文中阐释QUIOS机制下事件和进程的关系及两种视图的表达方式，用实例说明定性推理方法，并给出QUIOS的推理算法．     

Reasoning with Numeric and Symbolic Time Information

Representing and reasoning about time is fundamental in many applications of Artificial Intelligence as well as of other disciplines in computer science, such as scheduling, planning, computational linguistics, database design and molecular biology. The development of a domain-independent temporal reasoning system is then practically important. An important issue when designing such systems is the efficient handling of qualitative and metric time information. We have developed a temporal model, TemPro, based on the Allen interval algebra, to express and manage such information in terms of qualitative and quantitative temporal constraints. TemPro translates an application involving temporal information into a Constraint Satisfaction Problem (CSP). Constraint satisfaction techniques are then used to manage the different time information by solving the CSP. In order for the system to deal with real time applications or those applications where it is impossible or impractical to solve these problems completely, we have studied different methods capable of trading search time for solution quality when solving the temporal CSP. These methods are exact and approximation algorithms based respectively on constraint satisfaction techniques and local search. Experimental tests were performed on randomly generated temporal constraint problems as well as on scheduling problems in order to compare and evaluate the performance of the different methods we propose. The results demonstrate the efficiency of the MCRW approximation method to deal with under constrained and middle constrained problems while Tabu Search and SDRW are the methods of choice for over constrained problems.     

基于分层因果关系的定性推理方法

定性推理能为信息不完全的复杂系统产生行为预测,但推理分支可能出现组合爆炸,限制了它的应用.传统的算法不仅产生无法控制的推理分支,并且占用了大量的内存,不仅使用户不易理解推理的结果,而且甚至导致推理的失败.在LSIM方法的基础上提出了一种基于分层因果关系的定性推理方法LCQR(layered causal qualitative reasoning),旨在解决这一问题,使提取出的变量间的因果关系层次化,并应用于定性推理,取得了较满意的结果.     

Privacy Loss in Distributed Constraint Reasoning: A Quantitative Framework for Analysis and its Applications

It is critical that agents deployed in real-world settings, such as businesses, offices, universities and research laboratories, protect their individual users’ privacy when interacting with other entities. Indeed, privacy is recognized as a key motivating factor in the design of several multiagent algorithms, such as in distributed constraint reasoning (including both algorithms for distributed constraint optimization (DCOP) and distributed constraint satisfaction (DisCSPs)), and researchers have begun to propose metrics for analysis of privacy loss in such multiagent algorithms. Unfortunately, a general quantitative framework to compare these existing metrics for privacy loss or to identify dimensions along which to construct new metrics is currently lacking. This paper presents three key contributions to address this shortcoming. First, the paper presents VPS (Valuations of Possible States), a general quantitative framework to express, analyze and compare existing metrics of privacy loss. Based on a state-space model, VPS is shown to capture various existing measures of privacy created for specific domains of DisCSPs. The utility of VPS is further illustrated through analysis of privacy loss in DCOP algorithms, when such algorithms are used by personal assistant agents to schedule meetings among users. In addition, VPS helps identify dimensions along which to classify and construct new privacy metrics and it also supports their quantitative comparison. Second, the article presents key inference rules that may be used in analysis of privacy loss in DCOP algorithms under different assumptions. Third, detailed experiments based on the VPS-driven analysis lead to the following key results: (i) decentralization by itself does not provide superior protection of privacy in DisCSP/DCOP algorithms when compared with centralization; instead, privacy protection also requires the presence of uncertainty about agents’ knowledge of the constraint graph. (ii) one needs to carefully examine the metrics chosen to measure privacy loss; the qualitative properties of privacy loss and hence the conclusions that can be drawn about an algorithm can vary widely based on the metric chosen. This paper should thus serve as a call to arms for further privacy research, particularly within the DisCSP/DCOP arena.     

A constraint-based approach to spatiotemporal reasoning

We introduce a form of spatiotemporal reasoning that uses homogeneous representations of time and the three dimensions of space. The basis of our approach is Allen's temporal logic on the one hand and general constraint satisfaction algorithms on the other, where we present a new view of constraint reasoning to cope with the affordances of spatiotemporal reasoning as introduced here. As a realization for constraint reasoning, we suggest a massively parallel implementation in form of Boltzmann machines.     

CAUPS系统中的时空推理机制与算法

动态城市规划方案仿真系统中的一个重要问题就是抽象和提取仿真对象之间涉及到的空间和时间逻辑关系,并以此作为仿真过程的基本准则和规范动态推理演算。为了建立CAUPS系统中的时空推理机制,针对城市规划方案的动态仿真过程中需要应用到的基本准则和规范,在传统时空关系描述和推理规范的基础上定义出适用于面向城市规划动态仿真的时空关系。考虑到城市规划过程中存在的非刚体对象,提出面向城市辅助规划系统方案应用结果仿真的时间空间推理规范,该规范能够非常好的支持如植被、水域等非刚体对象,并给出一个适合多Agent系统采用的时间空间推理规范执行解决方案,包括时间推理规范算法和空间推理规范算法。面向城市规划的时间推理规范算法和空间推理规范算法已经被成功应用于CAUPS系统底层的多Agent交互关系调整中。     

时空推理中自动生成复合表的通用算法

时空推理是面向时间/空间问题的研究领域,在人工智能(如语义Web、机器人导航、自然语言处理、物理过程的定性模拟和常识推理等)和其他领域有着广泛的应用前景.复合推理在时空推理中具有重要作用,是约束满足问题等其他定性推理的基础.复合推理是由R(a,b)和R(b,c)决定R(a,c)的一种演绎推理.一般将关系复合结果放在复合表中备查.但目前复合表的建立需要逐个模型进行手工推导,少数模型给出了独立的复合表生成算法,没有适合多种时空关系模型、能自动生成复合表的通用算法.为此,提出了一种能自动生成复合表的通用算法.首先,给出了基于空间划分的通用时空表示模型.在此基础上,提出了基于场景检测的通用复合表生成算法.通过理论分析和对RCC、宽边界、区间代数等20余种典型时空模型的测试,证明了本算法对于所有以精确区域(或区间)为基础的确定、不确定时空模型均能正确快速地生成复合表.     

Interactive Case-Based Planning for Forest Fire Management

This paper describes an AI system for planning the first attack on a forest fire. This planning system is based on two major techniques, case-based reasoning, and constraint reasoning, and is part of a decision support system called CHARADE. CHARADE is aimed at supporting the user in the whole process of forest fire management. The novelty of the proposed approach is mainly due to the use of a local similarity metric for case-based reasoning and the integration with a constraint solver in charge of temporal reasoning.     

基于层次因果诊断模型的搜索策略研究

文章针对定性推理QSIM算法存在组合爆炸的问题,根据水产专家的多年总结的实践经验与知识,对鱼病的数据、信息进行了分析,将领域专家的知识作为约束条件,提出了基于层次因果的诊断模型的搜索策略,减少了推理分支,提高了效率。     

Exact and approximate reasoning about temporal relations1

Allen gives an algebra for representing qualitative temporal information about the relationships between pairs of intervals. In this paper, we address a fundamental reasoning task that arises in applications of the algebra: Given (possibly indefinite) knowledge about the relationships between intervals, find all feasible relationships between two intervals. We call this the minimal labels problem. Finding the minimal labels can be viewed as computing the deductive consequences of our knowledge. Determining exact solutions to this problem has been shown to be (almost assuredly) intractable. Allen gives an approximation algorithm based on constraint propagation. We present new approximation algorithms; determine analytically under what conditions the algorithms are exact; and examine, through some computational experiments, the quality of the approximate solutions produced by the algorithms. We also give a simple test for predicting when the approximation algorithms will and will not produce good quality approximations. Finally, we survey three example applications of the interval algebra chosen from the literature to show where the results of this paper could be useful.