Blackboard Mechanism Research Articles

Research on Cooperation Mechanism of Multiple Expert Systems for Oil-Gas Reservoir Protection Based on Agent

To solve collaboration problem in intelligent agents, a new cooperation mechanism is used in multi-expert system.Firstly,by introducing five-tuple with constraint condition,a new agent task decomposition model is presented.Secondly, blackboard mechanism based on point-to point is analyzed to solve communication among agents. The application of the expert system shows that our proposed task decompostion model is valid and blackboard mechanism based on point-to point can be used as communication among agents.Finally, minimum-cost-based cooperation mechanism based on agents can effectively solve the collaboration of experts in the multiple expert systems and improve the accuracy of inference.

A Multi-Staged Blackboard Query Optimization Framework for World-Spanning Distributed Database Resources

With the advent of distributed computing, particularly since the emergence of Grids, Clouds and other Service Oriented Computing paradigms, the querying of huge datasets of distributed databases or data repositories on a global scale has become a challenging research question. Currently, beside various other topics, two major concerns in this research area have to be addressed: data access & integration and query execution planning. Our research effort addresses the second issue, namely the query optimization of distributed database queries. Hereby we consider a variety of different heterogeneous and homogeneous infrastructures, parallel algorithms, and huge datasets, which span across several virtual organizations (VOs) with usually no centralized authority. This paper introduces a novel heuristic framework for the optimization of query execution plans (QEP) on a world-wide scale. Our work is based on a multi-staged blackboard mechanism to determine which available data, resources and operations have to be considered to perform a query optimally. Moreover, an evaluation scenario proves our findings that even small changes in the selection of e.g. sort operations for a query execution tree (QET) lead to significant performance improvements.

Blackboard Mechanism Based Ant Colony Theory for Dynamic Deployment of Mobile Sensor Networks

A novel bionic swarm intelligence algorithm, called ant colony algorithm based on a blackboard mechanism, is proposed to solve the autonomy and dynamic deployment of mobiles sensor networks effectively. A blackboard mechanism is introduced into the system for making pheromone and completing the algorithm. Every node, which can be looked as an ant, makes one information zone in its memory for communicating with other nodes and leaves pheromone, which is created by ant itself in nature. Then ant colony theory is used to find the optimization scheme for path planning and deployment of mobile Wireless Sensor Network (WSN). We test the algorithm in a dynamic and unconfigurable environment. The results indicate that the algorithm can reduce the power consumption by 13% averagely, enhance the efficiency of path planning and deployment of mobile WSN by 15% averagely.

ASIS&T Joint Chapter Virtual Program: A Learning Experience for the Geographically Dispersed

ASIS&T Joint Chapter Virtual Program: A Learning Experience for the Geographically Dispersed

Supervising conflicts in production management

Abstract In the computer integrated manufacturing field, the problem of integrating production management decisions is often viewed as nothing but a problem of data sharing between activities. To be truly ‘computer integrated’ a production management system must be able to coordinate these decisions and to take in charge the conflicts arising between the decisionmaking systems of a production management system. In this paper, we propose an architecture in which the decision coordination and decision synchronization task (also called supervision task) is viewed as a software layer (called a local-controller) over each decision-making system. We derive the knowledge needed by each local-controller, first to detect conflicts between the decision-making systems or with the state of the production system, second the knowledge needed to solve these conflicts. Each local-controller is placed into two relation networks through which its cooperative behaviour can be predefined. Moreover, we defined a data structure allowing description of the concessions the decision-making systems are ready to make in order to help conflict solving. Each local-controller is a knowledge-based reactive system, using a blackboard mechanism and able to dialogue with other local-controllers. General behaviour models for any local-controller are defined, particularly cooperative behaviour of local-controllers with each other. A local-controller shell has been developed from which all local-controllers are instantiated.

SFILTEX: toward an expert system for time-series filter design

This paper describes an attempt to develop a statistical expert system (FILTEX) as an intelligent aid for time-series filter design. To this end the knowledge of the filter design strategy is represented in Prolog and coupled with numerical routines of a general purpose signal processing package. This knowledge-based system is conceived as a set of independent knowledge sources integrated into a system by a blackboard mechanism which embodies overall control of the filter design process. Modularity and flexibility of knowledge representation in such a framework preserve usability of the evolving system during its development from the original numerical package to an expert system for filter design. This approach seems to be more flexible than the use of shells and less time consuming than building from scratch. A novel method for incorporating classical statistical information into an uncertainty management mechanism is presented. Experimental results confirm the feasibility of the approach and set direct...

Dice: A framework for intelligent real-time control

This article presents the development of a real-time intelligent control environment: DICE. Main emphasis is given to the requirements of knowledge-based systems to be realized in a real-time system. This results in a multi-task environment with several expert system kernels running in parallel. A blackboard mechanism is used for intertask communication. A kernel of the system uses demons to control other expert system kernels. DICE offers both boolean and multiple valued logic reasoning in separate expert system modules. As a worked out example an adaptive fuzzy logic based controller is described. Finally attention is paid to a general intelligent control scheme. Shown is that DICE is very well suited for the tasks involved in such a strategy. The role of neural networks in this configuration is also treated. The article ends with some conclusions about the use of DICE as a framework for intelligent control.