Constructing deliberative agents with case-based reasoning technology

Abstract

This article shows how autonomous agents may be constructed with the help of case-based reasoning (CBR) systems. The advantages and disadvantages of deliberative agents are discussed, and it is shown how to solve some of their inconveniences, especially those related to their implementation and adaptation. The Internet is one of the most popular vehicles for disseminating and sharing information through computer networks and it is influencing the business world. An agent-based solution is presented to show how the proposed technology may facilitate and improve an e-business strategy.

Unsupervised Visualization of SQL Attacks by Means of the SCMAS Architecture

“…As a result, this new agent contributes to improving the classification performance of SCMAS. The SCMAS architecture proposes a novel strategy to block SQL injection attacks through a distributed approach based on the capacities of the SQLCBR agents, which are a particular type of CBR-BDI agents [8]. The architecture has been divided into four levels so that the specific tasks are assigned according to the degree of complexity.…”

MOVIH-IDS: A mobile-visualization hybrid intrusion detection system

“…CBR-BDI agents [19], [57] use CBR systems [3] as a reasoning mechanism, which allows them to learn from initial knowledge, to interact autonomously with the environment, users and other agents within the system, and to have a large capacity for adaptation to the needs of their surroundings.…”

“…CBR-BDI agents [12], [19], [57] provide reasoning based on previous experiences as CBR systems use memory to solve new problems. The main idea when working with CBR systems is the concept of case, that can be seen as a past experience described by the 3-tuple <Problem, Solution, Results>.…”

“…In the case of MOVIH-IDS, an unsupervised neural model is embedded in such agents (see Section 5.3 for further details) to perform network Intrusion Detection (ID). CBR-BDI agents [19] use CBR as a reasoning mechanism, which allows them to learn from initial knowledge, to interact autonomously with the environment as well as with users and other agents within the system, and to have a large capacity for adaptation to the needs of their surroundings.…”

Multiagent Systems for Network Intrusion Detection: A Review

“…It includes deliberative agents characterized by the use of an unsupervised connectionist model to identify intrusions in computer networks. These deliberative agents are defined as CBR-BDI agents [38], [39] using the Case-based Reasoning paradigm [40] as a reasoning mechanism, which allows them to learn from initial knowledge, to interact autonomously with the environment, users and other agents within the system, and to have a large capacity for adaptation to the needs of their surroundings.…”