A knowledge encapsulation approach to ontology modularization

The development of monolithic ontologies for complex domains may face various challenges in reasoning and implementation. The notion of modularity can be employed for developing more efficient ontologies, especially in distributed environments. In this paper, we introduce a framework for developing ontologies in a modular manner. We describe the interface-based modular ontology formalism, (IBF), which theoretically supports the framework. The main feature of the framework is its support for knowledge encapsulation, i.e., it allows ontologies to define their main content using well-defined interfaces, such that their knowledge bases can only be accessed by other ontologies through these interfaces. An important implication of the proposed framework is that ontology modules can be developed completely independent of each other’s signature and languages. Such modules are free to only utilize the required knowledge segments of the others. We also investigate the issues of inconsistency in the proposed modular ontology framework. We provide solutions for isolating inconsistent ontology modules from the other parts of a modular ontology and also resolve inconsistencies which may be arisen by integrating consistent knowledge bases.     

Modular strategies for recursive game graphs

Many problems in formal verification and program analysis can be formalized as computing winning strategies for two-player games on graphs. In this paper, we focus on solving games in recursive game graphs which can model the control flow in sequential programs with recursive procedure calls. While such games can be viewed as the pushdown games studied in the literature, the natural notion of winning in our framework requires the strategies to be modular with only local memory; that is, resolution of choices within a module does not depend on the context in which the module is invoked, but only on the history within the current invocation of the module. While reachability in (global) pushdown games is known to be EXPTIME-complete, we show reachability in modular games to be NP-complete. We present a fixed-point computation algorithm for solving modular games such that in the worst case the number of iterations is exponential in the total number of returned values from the modules. If the strategy within a module does not depend on the global history, but can remember the history of the past invocations of this module, that is, if memory is local but persistent, we show that reachability becomes undecidable.     

The PIAM approach to modular integrated assessment modelling

The next generation of integrated assessment modelling is envisaged as being organised as a modular process, in which modules encapsulating knowledge from different scientific disciplines are independently developed at distributed institutions and coupled afterwards in accordance with the question raised by the decision maker. Such a modular approach needs to respect several stages of the model development process, approaching modularisation and integration on a conceptual, numerical, and technical level. The paper discusses the challenges at each level and presents partial solutions developed by the PIAM (Potsdam Integrated Assessment Modules) project at the Potsdam Institute for Climate Impact Research (PIK). The challenges at each level differ greatly in character and in the work done addressing them. At the conceptual level, the notion of conceptual consistency of modular integrated models is discussed. At the numerical level, it is shown how an adequate modularisation of a problem from climate–economy leads to a modular configuration into which independently developed climate and economic modules can be plugged. At the technical level, a software tool is presented which provides a simple consistent interface for data transfer between modules running on distributed and heterogeneous computer platforms.     

Cognitive versatility

Jerry Fodor divides the mind into peripheral, domain-specific modules and a domaingeneral faculty of central cognition. John Tooby and Lisa Cosmides argue instead that the mind is modular all the way through; cognition consists of a multitude of domain-specific processes. But human thought has a flexible, innovative character that contrasts with the inflexible, stereotyped performances of modular systems. My goal is to discover how minds that are constructed on modular principles might come to exhibit cognitive versatility. Cognitive versatility is exhibited in the ability to learn from experience. How can this ability emerge from the resources made available by earlier stages of cognitive specialization without sacrificing the many benefits of modularization? A transition into versatile cognition occurred in the history of our species. A similar development which occurs within individual ontogeny provides clues about the phylogenetic changes. Annette Karmiloff-Smith describes an ontogenetic process in which the mind's representational resources are enriched. The key idea is that versatile thinkers have access to an inferentially integrated library of knowledge. A distinction between nonconceptual and conceptual representations helps to explain how smart minds can draw much finer-grained discriminations within their experience than can simple minds. This is an important though insufficient condition for cognitive versatility.     

Simulation of Quantification Abilities Using a Modular Neural Network Approach

A novel modular neural network architecture and its application to the field of numerical cognition simulation are presented. Previous modular connectionist systems are typically constrained at one of two levels: at the representational level, in that the connectivity of the modules is hard-wired by the modeller; or at a local architectural level, in that the modeller explicitly allocates each module to a specific subtask. Our approach aims to minimise the constraints, thus reducing the bias possibly introduced by the modeller. The efficacy of this approach is demonstrated through the successful simulation of the development of two quantification abilities, subitising and counting, amongst children. It is concluded that such a minimally constrained modular system may contribute to both the capturing of learnt behaviour, and the allocation of modules to subtasks according to the nature of the task.     

IOTA: A Modular Programming System

A highly interactive programming system is presented which supports hierarchical and modular program development with abstraction mechanisms. By taking advantage of abstraction mechanisms, the system provides a "truly modular" environment, in which modules are constructed, debugged, verified, and compiled in a module-by-module fashion. Such an environment naturally requires system management of the information concerning ongoing program development, in the form of module databases. As a result, further problems arise as to how to modify the information in efficient and consistent ways. This paper discusses design objectives for modular programming systems by focusing on such issues as information management, interactive construction and modification of modules, separate processing, specification and verification, and supports for cooperative program development.     

A distributed constraint-based scheduler

This paper presents the design and implementation of a distributed advisory system which helps different human experts in the management and control of traffic within railway stations and along railway branches. Our approach allows the management of a whole railway line in a modular, expandable and scalable way. The scheduling of trains along a railway line is performed by several modules each one controlling a certain number of resources. These modules solve the scheduling of trains by interacting and communicating with each other. Each module has to deal with temporal constraints, priority between trains and constraints due to the structure of the station and railway branches. Our approach is based on the constraint logic programming (CLP) paradigm for solving the constraints involved in the problem. Therefore, this paper shows the versatility and adequacy of the CLP approach for the problems of this type.     

Modular architecture of the microfactories for automatic micro-assembly

The construction of a new generation of MEMS which includes micro-assembly steps in the current microfabrication process is a big challenge. It is necessary to develop new production means named micromanufacturing systems in order to perform these new assembly steps. The classical approach called “top-down” which consists in a functional analysis and a definition of the tasks sequences is insufficient for micromanufacturing systems. Indeed, the technical and physical constraints of the microworld (e.g. the adhesion phenomenon) must be taken into account in order to design reliable micromanufacturing systems. A new method of designing micromanufacturing systems is presented in this paper. Our approach combines the general “top-down” approach with a “bottom-up” approach which takes into account technical constraints. The method enables to build a modular architecture for micromanufacturing systems. In order to obtain this modular architecture, we have devised an original identification technique of modules and an association technique of modules. This work has been used to design the controller of an experimental robotic micro-assembly station.     

基于本体模块接口的入侵防御系统研究

建立一种新型分布式入侵防御模型并进行应用示例,该模型的知识库采用基于接口的模块化本体,防御方法采用上下文相关的策略。模块化本体能有效克服单一本体推理效率低、扩展性差等缺点,而在模块化本体中采用接口的方法有利于模块的独立进化和灵活配置。本体模块通过发送事件消息能够高效、及时地进行知识更新。     

Contextualization as an independent abstraction mechanism for conceptual modeling

The notion of context appears in computer science, as well as in several other disciplines, in various forms. In this paper, we present a general framework for representing the notion of context in information modeling. First, we define a context as a set of objects, within which each object has a set of names and possibly a reference: the reference of the object is another context which “hides” detailed information about the object. Then, we introduce the possibility of structuring the contents of a context through the traditional abstraction mechanisms, i.e., classification, generalization, and attribution. We show that, depending on the application, our notion of context can be used as an independent abstraction mechanism, either in an alternative or a complementary capacity with respect to the traditional abstraction mechanisms. We also study the interactions between contextualization and the traditional abstraction mechanisms, as well as the constraints that govern such interactions. Finally, we present a theory for contextualized information bases. The theory includes a set of validity constraints, a model theory, as well as a set of sound and complete inference rules. We show that our core theory can be easily extended to support embedding of particular information models in our contextualization framework.     

Neural and Neuro-Fuzzy Integration in a Knowledge-Based System for Air Quality Prediction

In this paper we propose a unified approach for integrating implicit and explicit knowledge in neurosymbolic systems as a combination of neural and neuro-fuzzy modules. In the developed hybrid system, training data set is used for building neuro-fuzzy modules, and represents implicit domain knowledge. The explicit domain knowledge on the other hand is represented by fuzzy rules, which are directly mapped into equivalent neural structures. The aim of this approach is to improve the abilities of modular neural structures, which are based on incomplete learning data sets, since the knowledge acquired from human experts is taken into account for adapting the general neural architecture. Three methods to combine the explicit and implicit knowledge modules are proposed. The techniques used to extract fuzzy rules from neural implicit knowledge modules are described. These techniques improve the structure and the behavior of the entire system. The proposed methodology has been applied in the field of air quality prediction with very encouraging results. These experiments show that the method is worth further investigation.     

Evaluating efficiency of self-reconfiguration in a class of modular robots

In this article we examine the problem of dynamic self-reconfiguration of a class of modular robotic systems referred to as metamorphic systems. A metamorphic robotic system is a collection of mechatronic modules, each of which has the ability to connect, disconnect, and climb over adjacent modules. A change in the macroscopic morphology results from the locomotion of each module over its neighbors. Metamorphic systems can therefore be viewed as a large swarm of physically connected robotic modules that collectively act as a single entity. What distinguishes metamorphic systems from other reconfigurable robots is that they possess all of the following properties: (1) a large number of homogeneous modules; (2) a geometry such that modules fit within a regular lattice; (3) self-reconfigurability without outside help; (4) physical constraints which ensure contact between modules. In this article, the kinematic constraints governing metamorphic robot self-reconfiguration are addressed, and lower and upper bounds are established for the minimal number of moves needed to change such systems from any initial to any final specified configuration. These bounds are functions of initial and final configuration geometry and can be computed very quickly, while it appears that solving for the precise number of minimal moves cannot be done in polynomial time. It is then shown how the bounds developed here are useful in evaluating the performance of heuristic motion planning/reconfiguration algorithms for metamorphic systems. © 1996 John Wiley & Sons, Inc.     

Some Issues in Analysis and Specialization of Modular Ciao-Prolog Programs

Separating programs into modules is a well-known technique which has proven very useful in program development and maintenance. Starting by introducing a number of possible scenarios, in this paper we study different issues which appear when developing analysis and specialization techniques for modular logic programming. We discuss a number of design alternatives and their consequences for the different scenarios considered and describe where applicable the decisions made in the Ciao system analyzer and specializer. In our discussion we use the module system of Ciao Prolog. This is both for concreteness and because Ciao Prolog is a second-generation Prolog system which has been designed with global analysis and specialization in mind, and which has a strict module system. The aim of this work is not to provide a theoretical basis on modular analysis and specialization, but rather to discuss some interesting practical issues.The authors would like to thank Francisco Bueno for many interesting discussions on analysis of modular programs, and the anonymous referees for their constructive comments. This work was funded in part by Spanish CICYT projects TIC99-1151 EDIPIA and TIC97-1640-CE.     

多约束条件下定制产品模块划分方法

为解决定制设计过程中模块划分问题,提出多约束条件下模块划分方法,该方法以功能域、结构域、用户需求域为多约束条件,提出产品定制模块多约束划分准则,构建产品零部件功能关联性矩阵、结构性关联矩阵以及用户需求关联性矩阵,并采用层次分析法确定三种约束之间的权重系数,计算产品零部件两两之间模块化综合关联度。依据模块化综合关联度和阈值生成相应的模块划分方案。并以开关柜为实例,验证了该方法的可行性。     

Web-based modular interface geometries with constraints in assembly models

The use of a modular technique in modeling for assembly can improve design efficiency and reduce the cost of product development. This paper presents an approach to an assembly model that was built using modular interface geometries. This paper proposes a novel, hybrid modular design strategy instead of the traditional, top-down design process. The curve-joint method is used as a simplified process for converting a 3D solid model to a skeleton model with interface geometries in modeling for assembly. This research builds assembly interface geometries with their constraints in the assembly model instead of using information about individual assembly parts for the product. These interface geometries are easy to share, and they deliver the design requirements properly. They also ensure that minimal efforts will be required in the design change process. By implementing this method, the constraints of the features in modular assembly parts can be transferred to interface geometries. Designers can easily add, replace, and delete design parts in the modular product. Module interaction for application programmed interface (MIAPI) is developed using HTML and JavaScript. The module structure of products can be verified via the web-based Internet in VRML format. These simplified assembly models that have fewer constraints allow design project managers to simulate the functioning of the product in the modularized design before the prototype is built. By using the assembly models, customers can easily choose various modules to assemble the exact products they are seeking via the Internet process. A desk lamp model is used as the example for implementation to validate the feasibility of this research.     

Wearing the playware

In this conceptual paper, we describe and define the range of possible applications, and the technical contours, of a robotic system to be worn on the body for playful interactions. Earlier work on a modular robotic wearable (MRW) described how, by using modular robotics to create a wearable, it is possible to obtain a flexible wearable processing system where freely interchangeable input/output modules can be positioned on a body suit in accordance with the task at hand. Here, we guide attention toward early prototypes to show the potentialities of such an approach, and focus on depicting possible applications in the electronic games domain. Indeed, the MRW is an example of modular playware which can create playful interactions for many application domains, including electronic games.     

Evolving modular networks with genetic algorithms: application to nonlinear time series

Abstract: A key problem of modular neural networks is finding the optimal aggregation of the different subtasks (or modules) of the problem at hand. Functional networks provide a partial solution to this problem, since the inter‐module topology is obtained from domain knowledge (functional relationships and symmetries). However, the learning process may be too restrictive in some situations, since the resulting modules (functional units) are assumed to be linear combinations of selected families of functions. In this paper, we present a non‐parametric learning approach for functional networks using feedforward neural networks for approximating the functional modules of the resulting architecture; we also introduce a genetic algorithm for finding the optimal intra‐module topology (the appropriate balance of neurons for the different modules according to the complexity of their respective tasks). Some benchmark examples from nonlinear time‐series prediction are used to illustrate the performance of the algorithm for finding optimal modular network architectures for specific problems.     

带有多个可转化约束的频繁项集挖掘算法

1.引言频繁项集的挖掘是数据挖掘课题中的一个很重要的方面,然而频繁项集的挖掘过程通常会产生数目庞大的频繁项集,并且其中的绝大多数并不是客户所期望得到的,因而使挖掘过程的效果和效率都大打折扣。     

一种基于网络节点的安全代理模型研究

随着网络入侵技术的发展,单独的防火墙、入侵检测系统等安全技术很难对付层出不穷的安全攻击。文中讨论的是基于网络节点的安全代理模型,它使用高度模块化技术,把各种安全技术作为一个个单独的功能模块来对待。根据安全策略服务器要求,安全代理动态加载和配置安全模块,故网络更加安全,也使得网络管理更加智能化、自动化。同时,该模型也支持第三方软件,故具有可扩展性和通用性。     

Comparing the notions of optimality in CP-nets,strategic games and soft constraints

The notion of optimality naturally arises in many areas of applied mathematics and computer science concerned with decision making. Here we consider this notion in the context of three formalisms used for different purposes in reasoning about multi-agent systems: strategic games, CP-nets, and soft constraints. To relate the notions of optimality in these formalisms we introduce a natural qualitative modification of the notion of a strategic game. We show then that the optimal outcomes of a CP-net are exactly the Nash equilibria of such games. This allows us to use the techniques of game theory to search for optimal outcomes of CP-nets and vice-versa, to use techniques developed for CP-nets to search for Nash equilibria of the considered games. Then, we relate the notion of optimality used in the area of soft constraints to that used in a generalization of strategic games, called graphical games. In particular we prove that for a natural class of soft constraints that includes weighted constraints every optimal solution is both a Nash equilibrium and Pareto efficient joint strategy. For a natural mapping in the other direction we show that Pareto efficient joint strategies coincide with the optimal solutions of soft constraints.