Blackboard System Research Articles

Resource allocation for cellular data services using multiagent schemes

Because the radio spectrum is limited, managing the limited amount of resources is an important issue, especially for high-speed data applications. This paper presents a distributed multiagent scheme (DMAS) that was developed for supporting resource allocation in a customer-accepted and cost-effective fashion. The scheme consists of a collection of problem-solving agents with three modules built into the scheme: the knowledge source, the blackboard system, and the control engine. Through operations and cooperation among the active agents, an allocation policy is selected and a customer-accepted schedule that meets the specified quality of service (QoS) is generated. A comparison of the performance between the DMAS scheme and other references previously proposed was derived using a 55 microcell model. It was found that the proposed scheme gave the highest quality of service satisfaction (around 90% at light loading and 60% at heavy loading).

A specific blackboard expert system to simulate and automate the design of high recirculation airlift reactors

This paper describes a new specific blackboard expert system to simulate and automate the design of high recirculation airlift reactors. The research work described aims to bring about savings in construction and running costs by automating the design process and removing unnecessary conservatism from the design process. Blackboard expert systems are reviewed and the paper describes the work to create a new blackboard framework for designing high recirculation airlift reactors. Two prototype programs were created in order to test the feasibility of a blackboard architecture expert system for this application and these are described. The testing of the two prototype programs led to the creation of a new expert system that interacted with industrial design programs. Expert design knowledge concerning the design of high recirculation airlift reactors was acquired and this process and the knowledge that was captured are described. Methods used to categorise and represent this knowledge are presented and the operation of the new blackboard system is discussed.

Intelligent control of a cryogenic cooling plant based on blackboard system architecture

Intelligent system techniques have been rapidly assimilating into process control engineering, with many applications reported in the last decade. Intelligent control is bringing a new perspective as well as new challenges to process control. In this paper, a software architecture for a Blackboard for Integrated Intelligent Control Systems (BIICS) is described. The system is designed to simultaneously support multiple heterogeneous intelligent methodologies, such as neural networks, expert systems, fuzzy logic, neural networks and genetic algorithms. It will be shown how such methodologies can be readily assimilated into the software architecture. The BIICS system represents a multi-purpose platform for design and simulation of intelligent control paradigms for different kinds of processes. Currently the system utilizes intelligent control techniques (neuro-fuzzy and genetic optimization) for controlling a cryogenic plant used for superconductor testing at temperatures below 100 K.

Concurrent marketing analysis: a multi‐agent model for product, price, place and promotion

Marketing management presents challenging ground for the integrated use of intelligent agents. The design of a strategic marketing plan presents a natural division of four distinct yet independent decision problems. Concurrent execution of these four functions can yield a significant decrease in time‐to‐market for new products, and turnaround time for modified products. Electronic information is a two‐edged sword. On the one hand, it results in information overload, taxing the decision‐ making process. On the other hand, the electronic nature of these vast information flows makes them accessible to intelligent agents who can improve the decision‐making process. This paper presents the problem of information overload and decision‐making complexity in marketing management. In it, we present a blackboard‐based agent architecture that allows individual agents to proceed toward their given goals yet react opportunistically to developments reported by other marketing agents. We present the blackboard model of problem solving, and discuss why it is well suited to support multiple marketing agents. CMA, a multi‐agent model for concurrent marketing analysis, is discussed and the agent architecture required for creating a cooperating society of marketing agents is presented.

Task-based conceptual graphs as a basis for automating software development

It is widely recognized that requirements engineering is a knowledge-intensive process and cannot be dealt with using only a few general principles. Since knowledge plays a crucial role in software development, software engineers have to describe and organize various aspects of knowledge before the program can be written. A recent work by Robillard reveals that software development can be further improved by recognizing the relevant knowledge structures. In this paper, we propose the use of a task-based conceptual graphs (TBCB) framework as a basis for automating software development. By structuring and operationalizing conceptual graphs, TBCG specifications can be transformed into executable programs automatically. To construct a conceptual model, task-based specification methodology is used as the mechanism to structure the knowledge captured in conceptual models, whereas conceptual graphs are adopted as the formalism to express task-based specifications and to provide a reasoning capability for the purpose of automation. Once task-based graphs have been constructed and verified, a blackboard system will automatically transform TBCG specifications into a software system composed of database schemas, knowledge base, and user interfaces. A meeting scheduling system is used as an illustrative example to demonstrate our approach. © 2000 John Wiley & Sons, Inc.

Robotic melon harvesting

Intelligent sensing, planning, and control of a prototype robotic melon harvester is described. The robot consists of a Cartesian manipulator mounted on a mobile platform pulled by a tractor. Black and white image processing is used to detect and locate the melons. Incorporation of knowledge-based rules adapted to the specific melon variety reduces false detections. Task, motion and trajectory planning algorithms and their integration are described. The intelligent control system consists of a distributed blackboard system with autonomous modules for sensing, planning and control. Procedures for evaluating performance of the robot performing in an unstructured and changing environment are described. The robot was tested in the field on two different melon cultivars during two different seasons. Over 85% of the fruit were successfully detected and picked.

Signal understanding and tool condition monitoring

This paper presents a new method, called signal understanding, for the monitoring of engineering processes, in particularly for the monitoring of tool conditions in machining processes. The new method is based on the blackboard system, an artificial intelligence method developed in the 1980s. It emulates a group of experts examining sensor signals from various aspects, and making monitoring decisions step-by-step. The blackboard system consists of two blackboards: an event blackboard, used to track the interpretations of the signal by the experts and a control blackboard, used to direct the interpretation process leading to monitoring decisions. As demonstrated by an example of tool condition monitoring in end milling, the method has a number of advantages over the existing methods, including improved reliability and reduced decision time.

Intelligent Diagnosing and Learning Agents for Intelligent Tutoring Systems

This research suggests two cooperating intelligent agents with new approaches for Intelligent Tutoring Systems (ITSs): one (a diagnosis agent) for correctly diagnosing the student's answers and another one (a learning agent) for intelligently learning from the student's responses. The diagnosis agent incorporates the classification tree concepts to identify the student's misconceptions and to do score assignment that includes the score assignment of partial correctness. The learning agent is a blackboard multistrategy machine learning model. The main purpose of the learning agent is to learn the features of the student's inconsistent behaviors for the system to take effective strategies to prevent the happening of the student's inconsistent behaviors. Importantly, accurate diagnosis on the student's answers is the prerequisite of a successful student model. Then an effective and efficient ITS is required to deal with the student's inconsistent behaviors.

Navigation integration of a mobile robot in dynamic environments

This article presents a design and experimental study of navigation integration of an intelligent mobile robot in dynamic environments. The proposed integration architecture is based on the virtual-force concept, by which each navigation resource is assumed to exert a virtual force on the robot. The resultant force determines how the robot will move. Reactive behavior and proactive planning can both be handled in a simple and uniform manner using the proposed integration method. A real-time motion predictor is employed to enable the mobile robot to deal in advance with moving obstacles. A grid map is maintained using on-line sensory data for global path planning, and a bidirectional algorithm is proposed for planning the shortest path for the robot by using updated grid-map information. Therefore, the mobile robot has the capacity to both learn and adapt to variations. To implement the whole navigation system efficiently, a blackboard model is used to coordinate the computation on board the vehicle. Simulation and experimental results are presented to verify the proposed design and demonstrate smooth navigation behavior of the intelligent mobile robot in dynamic environments. ©1999 John Wiley & Sons, Inc.

SCOPE: a blackboard model-based decision support system for crude-oil trading

A complex decision problem dealing with crude-oil trading is hierarchically decomposed into multiple subproblems. A blackboard architecture for a Decision Support System (DSS) called SCOPE is developed in which the subproblems generated during the initial decision analysis can be mapped to relevant knowledge sources incorporating the most appropriate techniques for each. A prototype implementation of SCOPE-0, an expert system application using Knowledge Engineering Environment (KEE) for supporting the most critical subproblem—crude-oil evaluation—is presented. Some of the lessons learned from this research project, methodological issues, and promising future directions for DSS design are outlined. Copyright © 1999 John Wiley & Sons, Ltd.

Borg: a knowledge-based system for automatic generation of image processing programs

This article deals with the design of a system that automates the generation of image processing applications. Users describe tasks to perform on images and the system constructs a specific plan, which, after being executed, should yield the desired results. Our approach to this problem belongs to a more general category of systems for the supervision of a library of operators. The generation of an application is considered as the dynamic building of chains of image processing through the selection, parameter tuning and scheduling of existing operators. To develop such a system, we suggest to use a knowledge-rich resolution model and to integrate seven design rules. The Borg system has been developed following these prescriptions. It hinges on hierarchical, opportunistic and incremental planning by means of knowledge sources of the blackboard model, which enable to take into account the planning, evaluation and knowledge acquisition issues.

Intelligent on-line system identification using a blackboard system

Adaptive control systems have been developed and used for a number of decades. However, there are still some problems with their operation, which limit wider industrial adoption. This paper addresses one aspect of adaptive control, namely the on-line system identification of the time-varying process being controlled. The approach adopted is to use a blackboard system to identify the time-varying process model. The blackboard system contains knowledge sources with, algorithmic, fuzzy-logic and evolutionary reasoning. The paper describes the design of a computer simulation of this approach, concentrating on the fuzzy reasoning used to validate the multiple models, and the evolutionary techniques used to reject poor models and introduce better models. The computer simulation is then evaluated, firstly using simulated plant data and then using real plant data from an experimental hot air dryer. It is concluded that the technique is practical, and worthy of further testing on a pilot plant.

A blackboard system approach to electromagnetic sensor data interpretation

Abstract: A prototype system for automated defect classification and characterisation of automotive or other components involving two separate inspection sensors, vision and electromagnetic, was developed. This paper concentrates on the development work and issues related to the electromagnetic sensor. In particular, the issues relating to knowledge acquisition and knowledge representation are discussed. For instance, one of the problems which arose during the development work was that it appeared that the reasoning carried out unconsciously by a human was more complex than had been realised and not easily encapsulated as high level knowledge. A blackboard architecture was used to integrate the different areas of expertise required for each sensor to interpret the results of the inspections. The main issue here was in the effective use of the blackboard architecture for intelligent data fusion at all levels to improve interpretation.

Fuzzy logic in a blackboard system for controlling plasma deposition processes

A blackboard system, ARBS, has been used to control a plasma processing unit, which is used for depositing coatings on the surface of electronic or mechanical components. Previous applications of ARBS have been based on crisp logic, but fuzzy logic was added in this study for plasma deposition control. Fuzzy rules have been introduced into ARBS without changing either the rule syntax or the existing inference engines, thereby demonstrating the flexibility of the software. Consequently crisp and fuzzy rules can coexist within a single knowledge source (i.e. module). An efficient technique for defuzzification has been employed in which the membership functions are replaced by Dirac delta functions. The technique is equivalent to standard methods of defuzzification, without loss of precision or accuracy, but with a reduced number of calculations. Multi-variable control of DC-bias (an electrical parameter) by automatic adjustment of pressure and RF (radio frequency) electrical power is demonstrated.

Application of a blackboard framework to a cooperative fixture design system

An approach based on artificial intelligence techniques is proposed to develop an automated fixturing system that can interpret the semantic information of features of a 3-D object, created in a computer-aided design system, and suggest the possible clamping conditions on the object. This paper reports the preliminary work that investigates the suitability of using a blackboard framework as a problem-solving model to realize such a system. It describes the creation of functional knowledge sources (KSs)(for fixture design) in detail and demonstrates their applications in a cooperative problem solving environment. A preliminary system architecture implemented in the GBB blackboard system has been presented in the paper.

A conceptual framework for interactive and blackboard based CAPP

This paper gives an extensive overview of the data models and the knowledge sources that form the back-bone of an interactive Computer Aided Process Planning (CAPP) kernel. The CAPP kernel is based on a blackboard system architecture. The paper describes the information that resides on the blackboard and how this information is instantiated: the blackboard approach provides the human process planner with assisting 'expert modules', each capable of performing a specific automated process planning task. In each process planning step that is performed by such expert module, the human operator has full control over the generation of the process plan and can always change the solution proposed by the expert modules.

A blackboard model of accenting

This paper outlines a model of accenting that is based on a combination of syntactic/metrical and semantic/pragmatic considerations. The model, called theblackboardmodel of accenting in an allusion to the blackboard architectures used in artificial intelligence, explains how the interaction of various factors (namely ««triggers»» as well as ««obstacles»» for accenting) determines the locations of sentence accents in speech. The interactions between factors related to novelty and contrast are studied in some detail. The paper focuses on the problem of accent location in the prosodic component of a concept-to-speech system.

A behaviour-based architecture with attention control

In this paper we present a behaviour-based mobile robot system for task execution. The behaviour model of this system consists of a number of motion behaviours, including reflexes and voluntary motion behaviours, and knowledge acquisition modules providing supporting information. Execution of a task is regarded as a problem-solving process. A blackboard model is introduced to overcome some shortcomings of the behaviour-based architectures, especially concerning modularity and task execution capability. The concept of attention is introduced in the behaviour control, which is more advantageous than the behaviour control mechanisms presented in the literature. Its introduction results in situation-dependent behaviour coordination. For efficient task execution, environment knowledge is maintained in a memory. Task-achieving behaviour is designed to make use of the memory when available.

An object-oriented blackboard based approach for automated finite element modeling and analysis of multichip modules

This paper addresses the application of the blackboard system architecture and object-oriented data abstraction techniques to the domain of finite element modeling and analysis. Specifically, a hierarchical object-oriented database was used to represent the physical system at different levels of abstraction including the user-defined physical system level, a computer-generated, simplified physical model level, and the finite element model level. Object link relationships within a given abstraction level and across different abstraction levels resulted in seamless bidirectional information exchange. The blackboard system architecture employed provided a framework for distributed cooperative problem solving, for the application of simplifying domain-specific modeling assumptions, and for integrating the various software modules that are involved in the entire finite element modeling and analysis process. These methodologies were implemented in a prototype computational tool calledIMCMA theIntelligentMultichipModuleAnalyzer. An example illustrates howIMCMA automates finite element thermal analysis of small integrated circuit features in multichip modules through a two-step finite element submodeling process.

A behaviour-based blackboard architecture for reactive and efficient task execution of an autonomous robot

Mobile robot control architectures have matured in two major directions: hierarchical and behaviour-based. Behaviour-based architectures enable rapid response to environment changes and they feature robustness in real worlds. Earlier behaviour-based systems have suffered from difficulties in system modularity, state representation and integration of world models. In this paper we present a behaviour-based mobile robot system for task execution. The behaviour model of this system consists of a number of motion behaviours, including reflexes and voluntary motion behaviours, and knowledge acquisition modules providing supporting information. Execution of a task is regarded as a problem-solving process. A blackboard model is introduced to overcome some shortcomings of the behaviour-based architectures, especially concerning modularity and task execution capability. The concept of attention is introduced in the behaviour control, which is more advantageous than the behaviour control mechanisms presented in the literature. Its introduction results in situation-dependent behaviour coordination. For efficient task execution, environment knowledge is maintained in the memory. Task-achieving behaviour is designed to make use of the memory when available.