Using fuzzy logic to integrate neural networks and knowledge-based systems

Outlined here is a novel hybrid architecture that uses fuzzy logic to integrate neural networks and knowledge-based systems. The author's approach offers important synergistic benefits to neural nets, approximate reasoning, and symbolic processing. Fuzzy inference rules extend symbolic systems with approximate reasoning capabilities, which are used for integrating and interpreting the outputs of neural networks. The symbolic system captures meta-level information about neural networks and defines its interaction with neural networks through a set of control tasks. Fuzzy action rules provide a robust mechanism for recognizing the situations in which neural networks require certain control actions. The neural nets, on the other hand, offer flexible classification and adaptive learning capabilities, which are crucial for dynamic and noisy environments. By combining neural nets and symbolic systems at their system levels through the use of fuzzy logic, the author's approach alleviates current difficulties in reconciling differences between low-level data processing mechanisms of neural nets and artificial intelligence systems

A hierarchical distributed control model for coordinating intelligent systems

A hierarchical distributed control (HDC) model for coordinating cooperative problem-solving among intelligent systems is described. The model was implemented using SOCIAL, an innovative object-oriented tool for integrating heterogeneous, distributed software systems. SOCIAL embeds applications in 'wrapper' objects called Agents, which supply predefined capabilities for distributed communication, control, data specification, and translation. The HDC model is realized in SOCIAL as a 'Manager'Agent that coordinates interactions among application Agents. The HDC Manager: indexes the capabilities of application Agents; routes request messages to suitable server Agents; and stores results in a commonly accessible 'Bulletin-Board'. This centralized control model is illustrated in a fault diagnosis application for launch operations support of the Space Shuttle fleet at NASA, Kennedy Space Center

An ontology framework for developing platform-independent knowledge-based engineering systems in the aerospace industry

This paper presents the development of a novel knowledge-based engineering (KBE) framework for implementing platform-independent knowledge-enabled product design systems within the aerospace industry. The aim of the KBE framework is to strengthen the structure, reuse and portability of knowledge consumed within KBE systems in view of supporting the cost-effective and long-term preservation of knowledge within such systems. The proposed KBE framework uses an ontology-based approach for semantic knowledge management and adopts a model-driven architecture style from the software engineering discipline. Its phases are mainly (1) Capture knowledge required for KBE system; (2) Ontology model construct of KBE system; (3) Platform-independent model (PIM) technology selection and implementation and (4) Integration of PIM KBE knowledge with computer-aided design system. A rigorous methodology is employed which is comprised of five qualitative phases namely, requirement analysis for the KBE framework, identifying software and ontological engineering elements, integration of both elements, proof of concept prototype demonstrator and finally experts validation. A case study investigating four primitive three-dimensional geometry shapes is used to quantify the applicability of the KBE framework in the aerospace industry. Additionally, experts within the aerospace and software engineering sector validated the strengths/benefits and limitations of the KBE framework. The major benefits of the developed approach are in the reduction of man-hours required for developing KBE systems within the aerospace industry and the maintainability and abstraction of the knowledge required for developing KBE systems. This approach strengthens knowledge reuse and eliminates platform-specific approaches to developing KBE systems ensuring the preservation of KBE knowledge for the long term

Working Notes from the 1992 AAAI Workshop on Automating Software Design. Theme: Domain Specific Software Design

The goal of this workshop is to identify different architectural approaches to building domain-specific software design systems and to explore issues unique to domain-specific (vs. general-purpose) software design. Some general issues that cut across the particular software design domain include: (1) knowledge representation, acquisition, and maintenance; (2) specialized software design techniques; and (3) user interaction and user interface

An active, ontology-driven network service for Internet collaboration

Web portals have emerged as an important means of collaboration on the WWW, and the integration of ontologies promises to make them more accurate in how they serve users’ collaboration and information location requirements. However, web portals are essentially a centralised architecture resulting in difficulties supporting seamless roaming between portals and collaboration between groups supported on different portals. This paper proposes an alternative approach to collaboration over the web using ontologies that is de-centralised and exploits content-based networking. We argue that this approach promises a user-centric, timely, secure and location-independent mechanism, which is potentially more scaleable and universal than existing centralised portals

Cooperation Patterns and Adaptation Patterns for Service-Based Inter-Organizational Workflows

International audienceModernization is an effective approach to making existing mainframe and distributed systems more responsive to business needs. SOA (service-oriented architecture) is an adequate paradigm that allows companies to tap into the business value in their current systems and position IT for rapid future changes to the business model. In our research works, we focus on the use of SOA to implement Inter- Organizational WorkFlows (IOWF). The goal is to take benefits from the advantages offered by the SOA paradigm like interoperability, reusability and flexibility in order to deal with workflow models easily adaptable, evolvable and reusable. This paper focuses on two specific architectures of IOWF which are the "chained execution" and the "subcontracting"; the first issue of this work is to define Service-Based Cooperation Patterns (SBCP) suitable to the two architectures considered. A SBCP is based on SOA; it is defined through three main dimensions: the distribution of services among the partner's sites, the control of instance execution and the structure of interaction between the workflows involved in the cooperation. The second issue of the paper consists of adaptation and evolution of IOWF process models obeying to the defined SBCP. Then, we state the main operations of adaptation that can be applied on these models; we focus on adaptation at process and interactional levels. Conformably to the three dimensions of SBCP, we define three classes of adaptation patterns: "service adaptation", "control flow adaptation" and "interaction adaptation" patterns. Also, we particularly distinguish some operations of adaptation called evolution of process models based on two perspectives: the expansion of the global functionality of the process and the expansion of the cooperation; we show that some evolutions are realized by reuse of existing IOWF models. For implementation, we consider IOWF process models specified with BPEL

Benchmarking Memory Management Capabilities within ROOT-Sim

In parallel discrete event simulation techniques, the simulation model is partitioned into objects, concurrently executing events on different CPUs and/or multiple CPUCores. In such a context, run-time supports for logical time synchronization across the different simulation objects play a central role in determining the effectiveness of the specific parallel simulation environment. In this paper we present an experimental evaluation of the memory management capabilities offered by the ROme OpTimistic Simulator (ROOT-Sim). This is an open source parallel simulation environment transparently supporting optimistic synchronization via recoverability (based on incremental log/restore techniques) of any type of memory operation affecting the state of simulation objects, i.e., memory allocation, deallocation and update operations. The experimental study is based on a synthetic benchmark which mimics different read/write patterns inside the dynamic memory map associated with the state of simulation objects. This allows sensibility analysis of time and space effects due to the memory management subsystem while varying the type and the locality of the accesses associated with event processin

A Survey of Adaptive Resonance Theory Neural Network Models for Engineering Applications

This survey samples from the ever-growing family of adaptive resonance theory (ART) neural network models used to perform the three primary machine learning modalities, namely, unsupervised, supervised and reinforcement learning. It comprises a representative list from classic to modern ART models, thereby painting a general picture of the architectures developed by researchers over the past 30 years. The learning dynamics of these ART models are briefly described, and their distinctive characteristics such as code representation, long-term memory and corresponding geometric interpretation are discussed. Useful engineering properties of ART (speed, configurability, explainability, parallelization and hardware implementation) are examined along with current challenges. Finally, a compilation of online software libraries is provided. It is expected that this overview will be helpful to new and seasoned ART researchers