Default logic as a query language

Research in nonmonotonic reasoning has focused largely on the idea of representing knowledge about the world via rules that are generally true but can be defeated. Even if relational databases are nowadays the main tool for storing very large sets of data, the approach of using nonmonotonic AI formalisms as relational database query languages has been investigated to a much smaller extent. In this work, we propose a novel application of Reiter's default logic by introducing a default query language (DQL) for finite relational databases, which is based on default rules. The main result of this paper is that DQL is as expressive as SO_∃∀ the existential-universal fragment of second-order logic. This result is not only of theoretical importance: We exhibit queries-which are useful in practice-that can be expressed with DQL and cannot with other query languages based on nonmonotonic logics such as DATALOG with negation under the stable model semantics. In particular, we show that DQL is well-suited for diagnostic reasoning     

‘Real’ information is not flat – and why it matters

We consider Luciano Floridi's proposal for a structural realism based on an Informational Structural Realism which, as he describes in his book (p. 339): ‘As a form of realism … is committed to the existence of a mind-independent reality addressed by, and constraining knowledge’. In doing this, we inform and reform aspects of the argument within a mathematical and, specifically, computability theoretic context.     

Foundations of Vagueness: a Category-theoretic Approach

The idea that vagueness has its origin in indiscernibility is not new. In this paper, we have tried to establish this thesis on a category-theoretic basis. In the process some other related notions, e.g. property, have been clarified.     

Control: An emergent property of biological neurons

Taxis, the tendency of an organism to approach or retreat from an external stimulus, is a common property of mobile living organisms. Taxis may be observed, e.g. in single-celled organisms, paramecia and Escherichia coli. Control in these organisms is implemented by chemical means. Shortly after multi-celled organisms appeared, their cells began to specialize. Early mobile multi-celled organisms also demonstrate taxis. For example, the pillbug seeks light. In medusa (jellyfish) of Phylum cnidaria the control task is accomplished by neuronal methods. The author is aware of no reports of jellyfish with associative memories. Thus, there exist multi-celled organisms that can control the motion of their bodies, yet possess no memory capacities. We speculate that the first task of neuronal circuitry was that of control rather than that of memory. In this paper we discuss the properties of neurons that make them natural controllers. It is the thesis of this paper that the ability to control is an emergent property of neurons.     

The opposite of control: a Deweyan perspective on intrinsic motivation in “After 3” technology programs

A central feature of the design of many “After 3” technology programs is the assumption that student learning and motivation requires that they have choice and control of their activity. Similarly, the dominant cognitive-rational perspective of motivation portrays effective learners as having control of themselves and their environment. In this article, we build on Dewey's (1934. Art as experience. New York: Perigree.) aesthetics and epistemology — as most fully developed in “Art as experience” — to suggest that to be deeply engaged in learning, to be truly moved, requires not only control, but also the “opposite of control”. In “Art as experience” Dewey proposed that aesthetic experience — compelling, transformative experience — requires doing (acting on the world), reflection (standing back from the world), and undergoing (being acted upon by the world). Furthermore, grasping the meaning of these experiences emerges through a qualitative sense in addition to intentional analysis and reflection. Thus, intrinsic motivation, or what we shall call transformative experience, finds a balance between control and its opposite. We elaborate our conception of the “opposite of control” and discuss how this idea helps us appreciate heretofore unilluminated qualities of intrinsic motivation in “After 3” technology programs.     

Taking CSCW seriously

The topic of Computer Supported Cooperative Work (CSCW) has attracted much attention in the last few years. While the field is obviously still in the process of development, there is a marked ambiguity about the exact focus of the field. This lack of focus may hinder its further development and lead to its dissipation. In this paper we set out an approach to CSCW as a field of research which we believe provides a coherent conceptual framework for this area, suggesting that it should be concerned with thesupport requirements of cooperative work arrangements. This provides a more principled, comprehensive, and, in our opinion, more useful conception of the field than that provided by the conception of CSCW as being focused on computer support for groups. We then investigate the consequences of taking this alternative conception seriously, in terms of research directions for the field. As an indication of the fruits of this approach, we discuss the concept of ‘articulation work’ and its relevance to CSCW. This raises a host of interesting problems that are marginalized in the work on small group support but critical to the success of CSCW systems ‘in the large’, i. e., that are designed to meet current work requirements in the everyday world.     

一种基于人工逻辑蛛网的路由算法

文中从生物仿生学的角度,对自然界中看似简单的生物行为进行研究,并从中提取有益的思想运用到通讯网络路由技术中。对传统的路由算法加以扩充、改进,将关键链路思想加入其中,并应用到MPLS网络的LSP路由当中。人工蜘蛛网络是源于蜘蛛结网捕食的行为研究提出的一种网络拓扑结构。基于蛛网,给出了一种路由算法(CRASW)。此算法在确定了搜索的区域后,能计算出区域内任意点到目的节点的路径。实验表明本算法更容易获得最优路径。     

Multiobject behavior recognition by event driven selectiveattention method

This paper presents a multiobject behaviour recognition approach based on assumption generation and verification, i.e., feasible assumptions about the present behaviors consistent with the input image and behavior models are dynamically generated and verified by finding their supporting evidence in input images. This can be realized by an architecture called the selective attention model, which consists of a state-dependent event detector and an event sequence analyzer. The former detects image variation (event) in a limited image region (focusing region), which is not affected by occlusions and outliers. The latter analyzes sequences of detected events and activates all feasible states representing assumptions about multiobject behaviors. We further extend the system by introducing colored-token propagation to discriminate different objects in state space, and integration of multiviewpoint image sequences to disambiguate the single-view recognition results. Extensive experiments of human behavior recognition in real world environments demonstrate the soundness and robustness of our architecture     

New Directions in Type-Theoretic Grammars

This paper argues for the idea that in describing language we should follow Haskell Curry in distinguishing between the structure of an expression and its appearance or manifestation. It is explained how making this distinction obviates the need for directed types in type-theoretic grammars and a simple grammatical formalism is sketched in which representations at all levels are lambda terms. The lambda term representing the abstract structure of an expression is homomorphically translated to a lambda term representing its manifestation, but also to a lambda term representing its semantics.     

Representation and processing of information related to real world events

In this paper, we discuss a formal system for representing and analyzing real world events. The event representation discussed in this paper accounts for three important event attributes, namely, time, space, and label. We introduce the notion of sequence templates that appears natural for capturing event related semantics; and in semantically analyzing user queries. To harness this potential, we present a formal structure to represent the queries related to real world events as well as an approach to semantically analyze a user query, and collate event related information to be dispatched to the user. Finally, we discuss the design and implementation of the Query-Event Analysis System (QEAS), which is an integrated system to (a) identify a best-matching sequence template(s) given a user query; (b) derive the meta-events based on the selected sequence templates; and (c) and use the meta-event information to answer the user query.     

A planning language for embedded systems

Recent research in planning is more and more focusing on planning systems working in ‘real world’ domains. These systems need to act in sense and represent the real world. Furthermore, no action, even if apparently simple, is guaranteed to succeed and, therefore, no planning can be ‘sound’ (with respect to the real world) without taking into account possible failures. This is mainly due to the intrinsic complexity of reality. A planning language is therefore required to represent explicitly failures, sensing tasks, planning tasks, and task combinations. In this paper, we propose a planning language (called ?) which addresses the above features. ? allows representing the basic planning activities, the control structures and the basic operations to deal with failures. As a consequence, a uniform representation is used to describe both acting/sensing in the external world and basic planning activities. In this paper, we give the syntax and the semantics of ?. Furthermore we also give some examples from an application (the project MAIA) which uses ? as planning language.     

基于本体论相互属性的Web资源元数据模型

要使Web资源支持资源的自动发现,首先需要对Web资源提供必要的描述。Web资源的元数据模型——资源描述框架只能表示资源之间的二元关系,多元关系必须转化为二元关系后才能表示。然而多元关系与其转化为多个二元关系后所具有的语义是不相同的。针对资源描述框架存在的上述问题,该文引入本体论相互属性的概念,避免了不恰当地使用多个二元关系表示多元关系。同时给出了Web资源元数据本体模型的XML标记库,使得描述Web资源的本体模型可以在Web网络上传输,并被搜索引擎解析和理解。     

Realistic or Abstract Imagery: The Future of Computer Graphics?

The big idea in computer graphics, what makes CG different from other ways of making images, is that CG represents images symbolically. The artist or designer creates a symbolic representation of the image, and the computer converts that representation to physical media. Because computational processes are so flexible, we have the freedom to invent any abstract representation that suits our needs. Somewhat surprisingly, most of computer graphics research has focused on the science and technology needed to make photorealistic images representing the physical world. In this talk, I will argue that we should shift our focus to developing techniques for manipulating abstract image representations. Historically, abstract imagery is more recent and more innovative than realistic imagery. Functionally, abstract image representations are often more informative and more expressive than realistic ones. More fundamentally, abstract image models better depict our mental models of the world, and are hence more useful to most people that use computer graphics in their work. In addition to motivating this line of research, I will outline some potentially promising research directions.     

Reinforcement learning of iterative behaviour with multiple sensors

Reinforcement learning allows an agent to be both reactive and adaptive, but it requires a simple yet consistent representation of the task environment. In robotics this representation is the product of perception. Perception is a powerful simplifying mechanism because it ignores much of the complexity of the world by mapping multiple world states to each of a few representational states. The constraint of consistency conflicts with simplicity, however. A consistent representation distinguishes world states that have distinct utilities, but perception systems with sufficient acuity to do this tend to also make many unnecessary distinctions.In this paper we discuss reinforcement learning and the problem of appropriate perception. We then investigate a method for dealing with the problem, called theLion algorithm [1], and show that it can be used to reduce complexity by decomposing perception. The Lion algorithm does not allow iterative rules to be learned, and we describe modifications that overcome this limitation. We present experimental results that demonstrate their effectiveness in further reducing complexity. Finally, we mention some related research, and conclude with suggestions for further work.     

基于状态的类测试研究

类是面向对象程序语言的基本组成单位,所以类测试是整个面向对象软件测试的关键所在;论文主要针对传统状态测试方法在发现错误机制上存在的不足,研究了一种利用OSD模型的基于状态的类测试方法,提出了利用OSD模型与系统分析设计的状态图模型进行比较的思想;通过比较两个状态图模型中的状态是否一致以及各个状态间的方法转移是否一致,来发现传统状态测试难以发现的错误;实践证明,该方法能够检测出传统的状态测试方法难以检测到的错误。     

网络扫描权限证书机制研究

针对网络安全管理领域，本文提出网络扫描权限证书的概念、组成，并提出类X.509的扫描证书管理机制，从技术角度探索了解决网络扫描合法性的问题，从而为网络安全管理开拓了新思路。     

Ontology enhancing process for a situated and curiosity-driven robot

Nowadays, robots need to be able to interact with humans and objects in a flexible way and should be able to share the same knowledge (physical and social) of the human counterpart. Therefore, there is a need for a framework for expressing and sharing knowledge in a meaningful way by building the world model. In this paper, we propose a new framework for human–robot interaction using ontologies as powerful way of representing information which promote the sharing of meaningful knowledge between different objects. Furthermore, ontologies are powerful notions able to conceptualise the world in which the object such as Robot is situated. In this research, ontology is considered as improved solution to the grounding problem and enables interoperability between human and robot. The proposed system has been evaluated on a large number of test cases; results were very promising and support the implementation of the solution.     

移动环境下基于Hu—Tucker算法的选择调谐模式

在移动环境下，服务器采用何种数据结构向客户端传送信息直接关系到移动系统的效率。本文基于Hu-Tucker算法而提出的选择调谐方法将有效降低AT和TT。其基本思想就是根据记录的使用频率构造最优查找树。理论推导和模拟实验证明这种方法优于现有方法。     

Graph Classification by Means of Lipschitz Embedding

In pattern recognition and related fields, graph-based representations offer a versatile alternative to the widely used feature vectors. Therefore, an emerging trend of representing objects by graphs can be observed. This trend is intensified by the development of novel approaches in graph-based machine learning, such as graph kernels or graph-embedding techniques. These procedures overcome a major drawback of graphs, which consists of a serious lack of algorithms for classification. This paper is inspired by the idea of representing graphs through dissimilarities and extends our previous work to the more general setting of Lipschitz embeddings. In an experimental evaluation, we empirically confirm that classifiers that rely on the original graph distances can be outperformed by a classification system using the Lipschitz embedded graphs.