Data Set Used
Knowledge Visualizer (KV) uses a General Logic Diagram (GLD) to display examples and/or various forms of knowledge learned from them in a planar model of a multi-dimensional discrete space. Knowledge can be in different forms, for example, decision rules, decision trees, logical expressions, clusters, classifiers, and neural nets with discrete input… (More)
This paper contains a proposal of application of rule induction for generating agent strategy. This method of learning is tested on predator-prey domain, in which predator agents learn how to capture preys. However, we assume that proposed learning mechanism will be beneficial in all domains, in which agents can determine direct results of their actions.… (More)
—Reinforcement learning suffers from inefficiency when the number of potential solutions to be searched is large. This paper describes a method of improving reinforcement learning by applying rule induction in multi-agent systems. Knowledge captured by learned rules is used to reduce search space in reinforcement learning, allowing it to shorten learning… (More)
This paper briefly describes the LUS-MT method for automatically learning user signatures (models of computer users) from datastreams capturing users' interactions with computers. The signatures are in the form of collections of multistate templates (MTs), each characterizing a pattern in the user's behavior. By applying the models to new user activities,… (More)
In this paper we propose an agent-based system for Service-Oriented Architecture self-adaptation. Services are supervised by autonomous agents which are responsible for deciding which service should be chosen for interoperation. Agents learn the choice strategy autonomously using supervised learning. In experiments we show that supervised learning (Na¨ıve… (More)
Decision trees and rules are completing methods of knowledge representation. Both have advantages in some applications. Algorithms that convert trees to rules are common. In the paper an algorithm that converts rules to decision tree and its implementation in inductive database VINLEN is presented.