The query answering system realizes the selection of the data, preparation, pattern discovering, and pattern development processes in an agent-based structure within the multi agent system, and it is designed to ensure communication between agents and an effective operation of agents within the multi agent system. The system is suggested in a way to process and evaluate fuzzy incomplete information by the use of fuzzy SQL query method. The modelled system gains the intelligent feature, thanks to the fuzzy approach and makes predictions about the future with the learning processing approach. The operation mechanism of the system is a process in which the agents within the multi agent system filter and evaluate both the knowledge in databases and the knowledge received externally by the agents, considering certain criteria. The system uses two types of knowledge. The first one is the data existing in agent databases within the system and the latter is the data agents received from the outer world and not included in the evaluation criteria. Upon receiving data from the outer world, the agent primarily evaluates it in knowledgebase, and then evaluates it to be used in rule base and finally employs a certain evaluation process to rule bases in order to store the knowledge in task base. Meanwhile, the agent also completes the learning process. This paper presents an intelligent query answering mechanism, a process in which the agents within the multi-agent system filter and evaluate both the knowledge in databases and the knowledge received externally by the agents. The following sections include some necessary literature review and the query answering approach Then follow the future trends and the conclusion.
The query answering system in agents utilizes fuzzy SQL queries from the agents, then creates and optimizes a query plan that involves the multiple data source of the whole multi agent system. Accordingly, it controls the execution of the task to generate the data set. The query operation constitutes the basic function of query answering. By query operation, the most important function of the system is fulfilled. This study also discusses peer to peer network structure and SQL structure, as well as query operation.
Query operation was applied in various fields. For example, selecting the related knowledge in a web environment was evaluated in terms of relational concept in databases. Relational database system particularly assists the system in making evaluations for making decisions about the future and in making the right decisions with fuzzy logic approach (Raschia & Mauaddib, 2002; Tatarinov et al. 2003; Galindo et al. 2001; Bosc et al. Chaudhry et.al. 1999; Saygın et al. 1999; Turgay et al.2006).
Key Terms in this Chapter
Multi-Agent System: It is a flexible incorporated network of software agents that interact to solve the problems that are beyond the individual capacities or knowledge of each problem solver.
Agent: A system that fulfils the independent functions, perceives the outer world and establishes the linking among the agents through its software
which is acting of situated: independent, reactive, proactive, flexible, recovers from failure and interacts with other agents
Intelligent Agent: It consists of a sophisticated intelligent computer program
Fuzzy SQL Query: Fuzzy SQL allows the system to make flexible queries about crisp or fuzzy attributes in fuzzy relational data or knowledge.
Fuzzy SQL(Structural Query Language): It is an extension of the SQL language that allows us to write flexible conditions in our queries. The FSQL allows us to use linguistic labels defined on any attribute.
Query: Caries out the scanning of the data with required specifications.
System: A set of components considered to act as a single goal-oriented entity.
Flexible Query: Incorporates some elements of the natural language so as to make a possible simple and powerful expression of subjective information needs.
Query Answering: Answers a user query with the help of a single or multi-database in the multi agent system.