Planning in a hierarchy of abstraction spaces

A problem domain can be represented as a hierarchy of abstraction spaces in which successively finer levels of detail are introduced. The problem solver ABSTRIPS, a modification of STRIPS, can define an abstraction space hierarchy from the STRIPS representation of a problem domain, and it can utilize the hierarchy in solving problems. Examples of the system's performance are presented that demonstrate the significant increases in problem-solving power that this approach provides. Then some further implications of the hierarchical planning approach are explored.     

求解QBF 问题的启发式调查传播算法

提出了一种启发式调查传播算法,并基于该算法设计了一种QBF(quantified Boolean formulae)求解器——HSPQBF(heuristic survey propagation algorithm for solving QBF)系统.它将Survey Propagation信息传递方法应用到QBF求解问题中.利用Survey Propagation作为启发式引导DPLL(Davis,Putnam,Logemann and Loveland)算法,选择合适的变量进行分支,从而可以减小搜索空间,并减少算法回退的次数.在分支处理过程中,HSPQBF系统结合了单元传播、冲突学习和满足蕴涵学习等一些优秀的QBF求解技术,从而能够提高QBF问题的求解效率.实验结果表明,HSPQBF无论在随机问题上还是在QBF标准测试问题上都有很好的表现,验证了调查传播技术在QBF问题求解中的实际价值.     

Application of NASA general-purpose solver to large-scale computations in aeroacoustics

Of several iterative and direct equation solvers evaluated previously for computations in aeroacoustics, the most promising was the NASA-developed general-purpose solver (winner of NASA's 1999 software of the year award). This paper presents detailed, single-processor statistics of the performance of this solver, which has been tailored and optimized for large-scale aeroacoustic computations. The statistics compiled using an SGI ORIGIN 2000 computer with 12 Gb available memory (RAM) and eight available processors, are the central processing unit time, RAM requirements, and solution error. The equation solver is capable of solving 10 thousand complex unknowns in as little as 0.01 s using 0.02 Gb RAM, and 8.4 million complex unknowns in slightly less than 3 h using all 12 Gb. This latter solution is the largest aeroacoustics problem solved to date with this technique. The study was unable to detect any noticeable error in the solution, since noise levels predicted from these solution vectors are in excellent agreement with the noise levels computed from the exact solution. The equation solver provides a means for obtaining numerical solutions to aeroacoustics problems in three dimensions.     

Levels of abstraction in designs of human–computer interaction: The case of e-mail

People process information at different levels of abstraction (e.g., talking about a topic in general terms and then going into the details). They move from one level to another but focus on a particular level at any specific moment. We see this behavior in the most common of tasks, such as solving problems, communicating and designing. This paper explores the implications of levels of abstraction on designing interactive systems. It demonstrates the idea by showing the feasibility and desirability of building a simple e-mail system based on the idea of levels of abstraction and testing its usability.We believe the implications of levels of abstraction on design are profound as regards the design of interactive systems that support dynamic behavior. Having shown the feasibility of some basic design implications, we call for empirical studies to test their usability and explore more advanced design implications.     

INTEGRATING CLASSIFICATION-BASED COMPILED LEVEL REASONING WITH FUNCTION-BASED DEEP LEVEL REASONING

Problem solving based on compiled associations between elements of the decision space and data is an efficient mode of reasoning for a large percentage of situations faced by an expert. But in some (usually small) percentage of cases, compiled associations are not enough by themselves to lead to correct results. Reasoning from “deeper” levels of understanding offers the advantage of producing correct results even in atypical cases, but at the cost of expanding more computational resources. Thus the trade-off between compiled level systems and deep level systems is between computational efficiency (at the compiled level) and problem-solving generality (at the deep level). We describe a hybrid system containing elements of both deep level reasoning and compiled level reasoning. More particularly, we propose a problem-solving architecture for category-based diagnostic problem solving which at the compiled level centers on classification problem solving and at the deep level uses a type of function-based reasoning. We concentrate in this report on the interaction between the compiled and deep level units and on the mechanisms of function-based reasoning that we employ. We show how our function-based consequence-finding problem solver can be focused by problem solving at the compiled level and how, through such interaction, we obtain the computational efficiency characteristic of compiled level problem solving while retaining the robustness characteristic of deep level problem solving.     

Information problem solving by experts and novices: analysis of a complex cognitive skill

In (higher) education students are often faced with information problems: tasks or assignments that require them to identify information needs, locate corresponding information sources, extract and organize relevant information from each source, and synthesize information from a variety of sources. It is often assumed that students master this complex cognitive skill of information problem solving all by themselves. In our point of view, however, explicit and intensive instruction is necessary. A skill decomposition is needed in order to design instruction that fosters the development of information problem solving. This research analyzes the information problem solving process of novices and experts in order to reach a detailed skill decomposition. Results reveal that experts spend more time on the main skill ‘define problem’ and more often activate their prior knowledge, elaborate on the content, and regulate their process. Furthermore, experts and novices show little differences in the way they search the Internet. These findings formed the basis for formulating instructional guidelines.     

A projected back-tracking line-search for constrained interactive inverse kinematics

Inverse kinematics is the problem of manipulating the pose of an articulated figure in order to achieve a desired goal disregarding inertia and forces. One can approach the problem as a non-linear optimization problem or as non-linear equation solving. The former approach is superior in its generality and ability to generate realistic poses, whereas the latter approach is recognized for its low iteration cost. Therefore, many prefer equation solving over optimization for interactive applications. In this paper we present a projected-gradient method for solving an inverse kinematics problem interactively, which exhibit good performance and precision. The method is compared to existing work in terms of visual quality and accuracy. Our method shows good convergence properties and deals with joint constraints in a simple and elegant manner. Our main contribution lies in an explicit incorporation of joint limits in an interactive solver. This makes it possible to compute the pose in each frame without the discontinuities exhibited by existing key frame animation techniques.     

Integrated knowledge systems

This paper presents a preliminary system structure supporting integration of expert systems and knowledge-based problem solving to other kinds of computing. The structure is based on layers on different abstraction levels communicating with each other through well-defined interface protocols. The result of applying such structure is a knowledge system capable of utilizing existing computer programs and information stores during its problem solving process.The structure is used in an application supporting hydrodynamic design of ships. A brief presentation of a demonstration system integrated to an existing ship design and engineering system is given.     

A hybrid parallel solver for systems of multivariate polynomials using CPUs and GPUs

This paper deals with a problem of finding valid solutions to systems of polynomial constraints. Although there have been several quite successful algorithms based on domain subdivision to resolve this problem, some major issues are still demanding further research. Prime obstacles in developing an efficient subdivision-based polynomial constraint solver are the exhaustive, although hierarchical, search of the zero-set in the parameter domain, which is computationally demanding, and their scalability in terms of the number of variables. In this paper, we present a hybrid parallel algorithm for solving systems of multivariate constraints by exploiting both the CPU and the GPU multicore architectures. We dedicate the CPU for the traversal of the subdivision tree and the GPU for the multivariate polynomial subdivision. By decomposing the constraint solving technique into two different components, hierarchy traversal and polynomial subdivision, each of which is more suitable to CPUs and GPUs, respectively, our solver can fully exploit the availability of hybrid, multicore architectures of CPUs and GPUs. Furthermore, our GPU-based subdivision method takes advantage of the inherent parallelism in the multivariate polynomial subdivision. We demonstrate the efficacy and scalability of the proposed parallel solver through several examples in geometric applications, including Hausdorff distance queries, contact point computations, surface–surface intersections, ray trap constructions, and bisector surface computations. In our experiments, the proposed parallel method achieves up to two orders of magnitude improvement in performance compared to the state-of-the-art subdivision-based CPU solver.     

A granular computing framework for self-organizing maps

When using granular computing for problem solving, one can focus on a specific level of understanding without looking at unwanted details of subsequent (more precise) levels. We present a granular computing framework for growing hierarchical self-organizing maps. This approach is ideal since the maps are arranged in a hierarchical manner and each is a complete abstraction of a pattern within data. The framework allows us to precisely define the connections between map levels. Formulating a neuron as a granule, the actions of granule construction and decomposition correspond to the growth and absorption of neurons in the previous model. In addition, we investigate the effects of updating granules with new information on both coarser and finer granules that have a derived relationship. Called bidirectional update propagation, the method ensures pattern consistency among data abstractions. An algorithm for the construction, decomposition, and updating of the granule-based self-organizing map is introduced. With examples, we demonstrate the effectiveness of this framework for abstracting patterns on many levels.     

面向随机模型检验的模型抽象技术

随机模型检验是经典模型检验理论的延伸和推广,由于其结合了经典模型检验算法和线性方程组求解或线性规划算法等,并且运算处理的是关于状态的概率向量而非经典模型检验中的位向量,所以状态爆炸问题在随机模型检验中更为严重.抽象作为缓解状态空间爆炸问题的重要技术之一,已经开始被应用到随机模型检验领域并取得了一定的进展.以面向随机模型检验的模型抽象技术为研究对象,首先给出了模型抽象技术的问题描述,然后按抽象模型构造技术分类归纳了其研究方向及目前的研究进展,最后对比了目前的模型抽象技术及其关系,总结出其还未能给出模型抽象问题的满意答案,并指出了有效解决模型抽象问题未来的研究方向.     

Hierarchical case-based reasoning integrating case-based anddecompositional problem-solving techniques for plant-control softwaredesign

Case based reasoning (CBR) is an artificial intelligence technique that emphasises the role of past experience during future problem solving. New problems are solved by retrieving and adapting the solutions to similar problems, solutions that have been stored and indexed for future reuse as cases in a case-base. The power of CBR is severely curtailed if problem solving is limited to the retrieval and adaptation of a single case, so most CBR systems dealing with complex problem solving tasks have to use multiple cases. The paper describes and evaluates the technique of hierarchical case based reasoning, which allows complex problems to be solved by reusing multiple cases at various levels of abstraction. The technique is described in the context of Deja Vu, a CBR system aimed at automating plant-control software design     

基于粗糙的属性约简在企业客户优惠决策中的应用

粗糙集理论是近代新兴的数据处理方法,在对粗糙集理论的一些算法研究后,提出了用其解决企业中大量数据中获取的较优决策,为企业信息系统的决策支持提供新的解决方法。     

操作系统安全结构框架中应用类通信安全模型的研究

经典的BLP模型是解决保密性问题的理论基础,Biba模型是一种简明易实现的完整性模型．在应用系统中数据的共享和安全是一对矛盾．在将应用系统抽象为应用类的基础上,引入完整性规则集代表信息的可信度,结合BLP模型和Biba模型构造了一种应用类通信的安全模型,并给出了模型的形式化描述和正确性证明．应用类通信安全模型不仅解决了保密性问题,而且解决了完整性问题．以支持B／S文电传输应用系统的安全为例,给出了在操作系统中实现应用类通信安全模型的方法,分析了模型实现的有效性．     

基于可满足性模理论求解器的程序路径验证方法

针对程序中因存在路径条数过多或复杂循环路径而导致路径验证时的路径搜索空间过大,直接影响验证的效率和准确率的问题,提出一种基于可满足性模理论（SMT）求解器的程序路径验证方法。首先利用决策树的方法对复杂循环路径提取不变式,构造无循环控制流图（NLCFG）;然后通过基本路径法对控制流图（CFG）进行遍历,提取基本路径信息;最后利用SMT求解器作为约束求解器,将路径验证问题转化为约束求解问题来进行处理。与同样基于SMT求解器的路径验证工具CBMC和FSoft-SMT相比,该方法在对测试集程序的验证时间上比CBMC降低了25%以上,比FSoft-SMT降低了15%以上;在验证精度上,该方法有明显的提升。实验结果表明,方法可以有效解决路径搜索空间过大的问题,同时提高路径验证的效率和准确率。     

Extending a blackboard architecture for approximate processing

Approximate processing is an approach to real-time AI problem-solving systems in domains where there are a range of acceptable answers in terms of certainty, accuracy, and completeness. Such a system needs to evaluate the current situation, make time predictions about the likelihood of achieving current objectives, monitor the processing and pursuit of those objectives, and if necessary, choose new objectives and associated processing strategies that are achievable in the available time. In this approach, the system is performingsatisficing problem-solving, in that it is attempting to generate the best possible solutions within available time and computational resource constraints.Previously published work (Lesser, Pavlin and Durfee 1988) has dealt with this approach to real-time; however, an important aspect was not fully developed: the problem solver must be very flexible in its ability to represent and efficiently implement a variety of processing strategies. Extensions to the blackboard model of problem solving that facilitate approximate processing are demonstrated for the task of knowledge-based signal interpretation. This is accomplished by extending the blackboard model of problem solving to include data, knowledge, and control approximations. With minimal overhead, the problem solver dynamically responds to the current situation by altering its operators and state space abstraction to produce a range of acceptable answers. Initial experiments with this approach show promising results in both providing a range of processing algorithms and in controlling this dynamic system with low overhead.This work was partly supported by the Office of Naval Research under a University Research Initiative grant, number N00014-86-K-0764, NSF-CER contract DCR-8500332, and ONR contract N00014-89-J-1877.     

Substructuring techniques in material nonlinear analysis

This work deals with the application of substructuring techniques to the analysis of quasi-static elastoplastic problems. After a short review of the underlying principles, implementation in a computer program which utilizes a frontal equation solver is discussed. Two numerical examples are considered and an annotated version of the adapted subroutine for solving the linearized system of equations is listed as an appendix.     

An Introduction to SequenceL: A Language to Experiment with Constructs for Processing Nonscalars

SequenceL is a language intended for experimentation with declarative constructs for nonscalar processing. In SequenceL, a problem solver provides little in the way of iterative/recursive details in a problem solution. Instead, the problem solver describes the solution directly by specifying, via a metastructure, the data structures which will hold results useful for solving the problem. SequenceL is a small language (i.e. there is a small number of language constructs), and it is not domain dependent. This paper is meant to introduce the reader to SequenceL.     

Design of Information Content and Layout for Process Control Based on Goal–Means Domain Analysis

With regard to the design of information content in information display, it is often claimed that the abstraction hierarchy (AH) of the work domain should be considered as a basis for identifying and structuring the information content. The primary advantage of AH-based analysis and design is that functionally abstracted information can systematically be identified and provided to the operator, which has rarely been presented in traditional displays. This study evaluated the effectiveness of providing functional information, which was abstracted and represented based on goal–means analysis along the AH, to the operator in two task situations (fault diagnosis and operation). The results showed that the operator’s performance was improved with the high-level information, and the latter’s utility became greater when the goal–means relations between information at different abstraction levels were exhibited. From the results, three design principles for information display can be drawn. First, information should be identified and displayed at multiple abstraction levels. Second, the goal–means relations among the abstraction levels should be explicitly presented, especially for analytical cognitive tasks. Third, information layout should support information integration along decomposition structure within an abstraction level as well as along abstraction levels.