AN INTELLIGENT FRAMEWORK FOR DYNAMIC TEST PLAN OF CLIENT/SERVER APPLICATIONS

To assist a solution to the problem of the test environment spanning multiple platforms, this paper proposes a decision support framework with the blackboard model to integrate all complementary features into a single automated test environment for multi-platform client/server applications. Before testing client/server applications, the input into this framework are testing tools with different approaches and client sites which are going to run the test. The planning agent will make a decision dynamically and produce a testing plan to allocate testing tasks to these testing tools to client sites. Two complementary features for testing client/server applications are illustrated in this paper to demonstrate how the framework works. The concept of mobile agents is applied to launch the test driver to different client sites, execute the tests and bring back the test results from client sites as well as the trace file from the server site for inspecting the interaction behavior among clients. Based on the multicast framework, the same test data can be broadcasted to multiple clients sites to run the tests simultaneously and the test results can be returned from client sites for examining the problem of repeated executions.

Research on Communication Reconnaissance Information Processing and Fusion

In complex electromagnetic signal environment, the reconnaissance equipments in tactical communication system can uninterruptedly reconnoiter a variety of enemy’s communication signals as well as access a number of characteristic parameters of time, frequency and space domain by searching analysis, feature extraction, direction finding and comprehensive identification. After a series of signal processing, data mining and information fusion, we can get the characteristic parameters of the electromagnetic spectrum of the enemy’s reconnaissance equipments, which provide the basis for analysis and estimation of electromagnetic situation in battlefield. In this paper a multi-hierarchical blackboard model is proposed for multi-sources communication reconnaissance information mining and fusion and the effectiveness of the method is validated in simulation environment.

Research on Cooperation Mechanism of Multi-Agent Grinding Control System Based on Blackboard Model

In order to solve the complex industrial control problems in grinding control,we construct multi-agent grinding control system based on the blackboard model in this paper. Through the study on the structure and control mechanism of the blackboard model, we design a framework for blackboard model in multi-agent grinding control system, divide and introduce the function model of the blackboard in detail, establish blackboard control agent to control and coordinate how the individual agent takes use of the blackboard resources centralizedly.Aiming at actual situations of the grinding control,we put forward cooperation mechanism multi-agent based on a combination of tasks sharing and results sharing, establish control decision agent to solve the conflict caused by multi-agent cooperation. The cooperation mechanism multi-agent grinding control system improves the execution ability of the individual agent, avoids the conflict caused by different goals and the competition of resources, ensures the coordinated executive of multiple tasks.

Research on Grinding Control System Design Method Based on Multi-Agent System Model

This paper proposes a Multi-Agent grinding control system model based on the blackboard model and in accordance with the Multi-Agent Systems modeling method . We hierarchical decompose of the grinding control task, establish a grinding control subsystem model, we establish the specific function of each subsystem model and establish a complete multi-Agent grinding control system structure. We establish a Multi-Agent System based on the blackboard model and multi-Agent Communication and Coordination Mechanism. This paper describes the architecture of the control multi-Agent grinding system and its function results in details and designs a blackboard coordination model, the results shared collaborative mechanisms and Multi-Agent collaboration conflict resolution strategies. This system model reduces the difficulty of the operation of the grinding control, multi-Agent collaboration enhance the system's ability to execute, decision-making consultation mechanisms improves the control precision and accuracy of the system.