A blackboard architecture for control

The control problem—which of its potential actions should an AI system perform at each point in the problem-solving process?—is fundamental to all cognitive processes. This paper proposes eight behavioral goals for intelligent control and a ‘blackboard control architecture’ to achieve them. The architecture distinguishes domain and control problems, knowledge, and solutions. It enables AI systems to operate upon their own knowledge and behavior and to adapt to unanticipated problem-solving situations. The paper shows how opm, a blackboard control system for multiple-task planning, exploits these capabilities. It also shows how the architecture would replicate the control behavior of hearsay-ii and hasp. The paper contrasts the blackboard control architecture with three alternatives and shows how it continues an evolutionary progression of control architectures. The paper concludes with a summary of the blackboard control architecture's strengths and weaknesses.     

A blackboard control architecture for model selection and sequencing

Model management systems are computerised systems that facilitate the management of large numbers of decision models used in organizations. Model selection and sequencing in a model management system is the problem of processing a given model base in order to arrive at a sequence of models that can be executed to produce a set of required outputs (goal). Prior solution approaches do not attempt to solve this problem such that the goal is achieved while best meeting the objectives of the user. Instead, research to date has typically provided the first sequence of models which satisfy the goal, without attempting to optimise the objectives of the user. This restricts the applicability of many existing approaches to problems with unique solutions or to situations where users exhibit no preference among the candidate model sequences (i.e. solutions). In many real-world problems, however, multiple solutions may exist and users may prefer a certain solution over the others, based on a variety of criteria such as solution cost, accuracy and so on. In this paper, we present an architecture based on the concept of blackboard control that solves the model selection and sequencing problem while attempting to optimise the objectives of the user. We also discuss the applicability of the proposed approach for solving other problems encountered in the area of model management.     

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.     

An expert system on design of liquid-retaining structures with blackboard architecture

Abstract: The design of liquid‐retaining structures involves many decisions to be made by the designer based on rules of thumb, heuristics, judgement, codes of practice and previous experience. Structural design problems are often ill structured and there is a need to develop programming environments that can incorporate engineering judgement along with algorithmic tools. Recent developments in artificial intelligence have made it possible to develop an expert system that can provide expert advice to the user in the selection of design criteria and design parameters. This paper introduces the development of an expert system in the design of liquid‐retaining structures using blackboard architecture. An expert system shell, Visual Rule Studio, is employed to facilitate the development of this prototype system. It is a coupled system combining symbolic processing with traditional numerical processing. The expert system developed is based on British Standards Code of Practice BS8007. Explanations are made to assist inexperienced designers or civil engineering students to learn how to design liquid‐retaining structures effectively and sustainably in their design practices. The use of this expert system in disseminating heuristic knowledge and experience to practitioners and engineering students is discussed.     

A blackboard structure for handling engineering design data

With the wide range of computer-based design aids now available there is a need for these to be able to communicate so that design data can be passed between them. One way of doing this is by means of a blakboard, that is, a central data area to which a number of systems has access. A simple blackboard structure is described together with how the idea of constraint modeling can be used to provide some intelligence for decision making to be undertaken.     

Reactive process control using a blackboard architecture

A software architecture to engineer complex process control applications must combine into the same paradigm efficient reactive and real-time functionalities and mechanisms to capture dynamic time-pressured intelligent behaviors, and must provide convenient high level tools to free the programmer from having to think at an unappropriate level of detail. We implement such characteristics into a blackboard framework that builds the basic abstract elements of reactive behavior and the blackboard computational model on top of low level real-time operating system functions. Under this approach, the engineer gets a powerful and flexible high level medium to map a complex system design that requires artificial intelligence techniques, like intelligent monitoring, and reactive planning and execution, with fully support for real-time programming. The paper also reviews other alternatives which have been explored in the past recent years for implementing complex reactive planning and execution systems.     

An application of blackboard architecture for the coordination among the security systems

The paper describes the design and implementation of a security architecture for a coordination information system constructed by blackboard architecture (BBA). The need arises for systems to coordinate with one another, to manage diverse attacks across networks and time. The coordination issue is the essential problem since it is beyond the scope of any one intrusion detection system (IDS) to deal with the intrusions. This paper shows a modeling and simulation of network security in which the multiple IDSes and a firewall coordinate by sharing attacker’s information for the effective detection of the intrusion. Another characteristic in the proposed simulation is the composition of a real intrusion by generating non-abstracted intrusion packets and, accordingly, the construction of non-abstracted version of IDS and firewall model components that are closely related to the intrusion packets.     

Knowledge decomposition for a product design blackboard expert system

Product design is a complex process, requiring many design factors and knowledge areas to be considered simultaneously. An expert system with a blackboard architecture is appropriate for representing this variety of factors and areas. However, oftentimes it is unclear how to transform the knowledge into the component knowledge bases of a blackboard expert system. The paper describes a non-directed graph decomposition procedure for transforming the knowledge into knowledge bases as part of an approach for developing a product design blackboard expert system. A wood head golf club design example is provided.     

A CASE tool for software architecture design

This paper describes the Software Architect's Assistant, an automated visual tool for the design and construction of Regis distributed programs. Unlike conventional CASE tools and their supported methodologies, the Architect's Assistant supports a compositional approach to program development in which the software architecture plays a central role throughout the software life-cycle—from the early design stage through to system management and evolution.In its implementation, we have addressed some of the limitations of existing CASE tools, particularly in the degree of automated support offered to the human developer. Conscious effort has been made to maximise usability and efficiency, primarily by enhancing the level of automation and flexibility together with careful design of the user interface. Our objective is to provide a tool which automates all mundane clerical tasks, enforces program correctness and consistency and, at the same time, accommodates the individual working styles of its users.Although currently specific to the development of Regis programs, the Architect's Assistant embodies concepts and ideas which are applicable to CASE tools in general.     

A temporal blackboard for a multi-agent environment

The multi-agent system paradigm emerges as an interesting approach in the Knowledge Based System (KBS) field, when distributed problem-solving techniques are required for solving problems that can be represented as a collection of groups of cooperating intelligent individuals. A key concept in the multi-agent systems is the interaction between agents. On the other hand time plays a crucial role in a wide range of KBS applications. Temporal reasoning and representations consists of formalizing the notion of time and providing means to represent and reason about the temporal aspects of knowledge. This paper presents a framework for agent communication based on the blackboard paradigm which is able to manage temporal information, and it provides its multiple access and coherence management protocols.     

A temporal blackboard structure for process control

Complex control systems require the use of new A.I. techniques to cope all the variable relations. One of the open topics is the use of temporal information and the ability of reason about it. Blackboard systems are one of the most promising proposal for real time expert system. In this paper a temporal blackboard to be used in a real time expert system is described. In order to represent the information a temporal model and its methods will be presented. Finally, some temporal features has been explained using a small scenario of a cement kiln process.     

Application of the BB1 blackboard control architecture to arrangement-assembly tasks

BB1 provides a uniform blackboard architecture for systems that reason about their own actions as well as about particular problems and solutions. A BB1 system can control its problem-solving strategy, explain its problem-solving actions in terms of its strategy, and learn new strategy knowledge from experience. BB1 provides a development environment suitable for a broad range of applications. In this paper, we focus on applications that employ a constructive assembly method to solve arrangement problems: arrange a set of objects in some context to satisfy a set of constraints. We present ACCORD, a BB1 knowledge base that contains the domain-independent knowledge required to assemble arrangements. We describe two applications of BB1-ACCORD: the PROTEAN system for protein-structure analysis and the SIGHTPLAN system for designing construction-site layouts.     

A data-centric design for n-tier architecture

The increasing heterogeneity, complexity, and distributed nature of deployment architectures only serve to compound the problems faced by software solutions. With the advent of the Internet and web technologies, system designers have had to reevaluate the applicability of n-tier architectures, and assess which technologies are appropriate at each tier. In this paper we evaluate the design issues of n-tier architecture. We debate whether it is good to move code or data to communicate messages between applications. Based on the latest technology of J2EE and .NET framework, we recommend a data centric design of n-tier architecture. Based on our experience we also suggest a modified interactive software process model.     

Using constraint propagation in blackboard systems: a flexiblesoftware architecture for reactive and distributed systems

Constraint propagation is explained, and the integration of problem-solving components is discussed. Validity and completeness results are considered, and the extension of constraint propagation to reactive and distributed systems using a blackboard architecture is addressed     

Goal-driven blackboard control architecture based on extending partially complete general goal trees

A control architecture for goal-driven blackboard systems is introduced. The basic elements of the architecture are goals, policies, strategies, methods and knowledge sources. The basic control loop employs a bidding mechanism to determine the knowledge source to be executed at the current cycle. The architecture employs separate control and domain blackboards, and separate knowledge sources for the control problem and for representing the domain knowledge. The major characteristics of the architecture are that it has a simple and uniform structure, and that its basic control loop is based on a formal basis, namely, extending a partially complete general goal tree. The architecture is implemented in Smalltalk and tested on a multiple-task planning problem.     

A blackboard application for process monitoring and supervision

This paper describes the architecture of a supervisory control and data-acquisition system. It works like an operator support tool by diagnosing malfunctions of a production process and inspecting the quality of the product. The system provides the operator in a plant with relevant process data and, at the same time, has to meet tough real-time requirements. The integration of intelligent alarm processing into a large supervisory control system will be described and techniques covering on-line processing of real-time data and intelligent alarm processing will be discussed. The system is based on a blackboard architecture which integrates a sequence of problem-solving methods and exhibits an incremental and opportunistic problem-solving style.     

多种哈希算法的可重构硬件架构设计

针对现有的哈希算法硬件架构仅实现少量几种算法的问题,设计了一种可实现SM3,MD5,SHA-1以及SHA-2系列共7种哈希算法的可重构IP,以满足同一系统对安全性可选择的需求。通过分析各哈希算法及其运算逻辑的相似性,该设计最大化地重用加法器和寄存器,极大地减少了总的实现面积。此外,该设计灵活可配,可以对内存直接存取。以Altera的Stratix II为FPGA目标器件,其最高频率可达100 MHz,总面积较现有设计减少26.7%以上,且各算法单位面积吞吐率均优于现有设计。     

A layered software architecture for quantum computing design tools

Compilers and computer-aided design tools are essential for fine-grained control of nanoscale quantum-mechanical systems. A proposed four-phase design flow assists with computations by transforming a quantum algorithm from a high-level language program into precisely scheduled physical actions.