A Multi-Intelligent Agent for Knowledge Discovery in Database (MIAKDD): Cooperative Approach with Domain Expert for Rules Extraction

A multi-disciplinary review of knowledge acquisition methods: From human to autonomous eliciting agents

“…In [31,30] the authors note that agents -generally seen through the lens of autonomous agents and multi-agent systems, and knowledge discoverygenerally seen as a data mining exercise, initially emerged and established as separate standalone research fields but in the last two decades methods from both fields merged into a new field of research, the "agent mining". The agent mining concept was largely supported by numerous studies in the literature [15,32,145,110,40], which proposed agents for agent mining applications under various names such as knowledge driven agents [15], knowledge collector agents [145], or miner agents [6,40]. In [30] Cao et al identify three approaches on agents and knowledge discovery that are essential for the emergence and establishment of the machine agents in knowledge acquisition: the data mining-driven agents, the agent-driven data mining, and the agent mining itself, where the former two were precursors of the latter.…”

“…The integration-based methods describe complex intelligent (autonomous) agents which fulfil the mining role individually, by implementing the learning mechanism implied by the knowledge discovery exercise through integration of one or more techniques from various fields, such as machine learning or evolutionary computation in order to implement one or more of the knowledge discovery tasks, such as rule extraction, classification or clustering [42,145,110]. From the integration point of view, Chemchem and Drias identify three major types of agents [40]: agents based on expert systems, which use inference engines for constructing knowledge, agents based on machine learning, which extract knowledge using machine learning techniques, and agents based on data mining which rely on knowledge discovery methods for extracting the knowledge more efficiently.…”

“…The Inference Engine creates the feedback loop that reinforce the rule base in order to emulate the reasoning process. Following the same type of integration framework as the one described in [40], Kadhim et al [110] proposed MI-AKDD, a multi-intelligent agent for knowledge discovery in databases in cooperation with human experts. The MIAKDD agent integrates a combined rule generation-classification component which uses classification based on association.…”

Multi-agent-based modeling for extracting relevant association rules using a multi-criteria analysis approach

“…Data mining and MAS have been used for building a complex system [34], they are combined to produce automatic data mining system. In this work, we used MAS to modeling several autonomous intelligent agents: Mining Rules Agent (MR-Agent), Quality Measurement Agent (QM-Agent), Decision Support Agent (DS-Agent), Principal Agent (MCA-Principal Agent), Control agent (Control Agent) and the user interface agent.…”

“…Multi-agent systems today represent a new technology for a design and control of complex systems. It is composed of independent software and hardware entities called agents; this system usually has several important features such as parallelism, robustness and scalability [34].…”