Interdisciplinary Journal of Information, Knowledge, and Management Multi-agent System for Knowledge-based Access to Distributed Databases

Introduction Knowledge-Based Systems (KBS) are productive tools of Artificial Intelligence (AI) working in a narrow domain to impart quality, effectiveness, and knowledge-oriented approach in decisionmaking process. Being a product of fifth generation computer technology, KBS possess characteristics like (Efraim, 1993): * providing a high intelligence level; * assisting people to discover and develop unknown fields; * offering vast knowledge base; * aiding management activities; * solving social problems in better way; * acquiring new perceptions by simulating unknown situations; * offering significant software productivity improvement; and * reducing cost and time to develop computerized systems. One of the main components of KBS is the knowledge base, in which domain knowledge, knowledge about knowledge, factual data, procedural rules, business heuristics, and so on are available. The inference engine is another component, which infers new knowledge and utilizes existing knowledge for decision-making and problem solving. Explanation/reasoning and self-learning are two more components to improve acceptability and scope of the system. These components also provide justification for the decision taken. Additionally, a user interface is available to interact with users in more friendly way. Figure 1 shows position of the KBS in the well-known data pyramid along with its general structure. [FIGURE 1 OMITTED] Typical relational database management systems deal with data stored in predefined format in one or more databases/tables. These systems do not deal with knowledge and/or decision processing and do not include features like: * capability to add powers to the solution and concentrate on effectiveness; * transfer of expertise, use of expertise in decision making, self learning, and explanation; * mainly symbolic manipulation; * learning by case/mistakes; * ability to deal with partial and uncertain information; and * work for narrow domain in a proactive manner. In the information and communication technology era today, a large number of processes is automated and generates number of large databases. Some applications span their boundaries in multiple dimensions and deal with multiple databases in a distributed fashion. Such large databases in business contain staggering amounts of raw data. These data must be looked at to find new relationships, emerging lines of the business, and ways for improving it. Trying to make sense out of these data requires a knowledge-oriented perspective, which is not easily achieved through either statistical process or even multidimensional visualization alone (Cox, 2005). The potential validity or usefulness of data elements or patterns of data elements may be different for various users. The relevance of such items is highly contextual, personal, and changing continuously. According to Donovan (2003), making retrieved data or a description of data patterns generally understandable is also highly problematic. Moreover, the structure and size of the data set or database and the nature of the data itself make the procedure more complex and tedious. This leads to the need for the proposed system in which databases can be accessed in knowledge-oriented fashion. To achieve this, productive agents like KBS can be utilized to search and manage database content to impart quality and effectiveness. Section two of this paper proposes a framework and methodology of knowledge-based access to multiple databases using modified Knowledge Query and Manipulation Language (KQML) as communication means between agents. Section three discusses an illustrative situation along with the structure of databases, a sample agent communication using KQML block, and a typical query by an agent to another agent with an example in dairy industry that works on the proposed architecture. …