Machine Learning and Inductive Logic Programming for Multi-agent Systems (original) (raw)

Machine Learning for Agents and Multi-Agent Systems

2003

ABSTRACT In order to be truly autonomous, agents need the ability to learn from and adapt to the environment and other agents. This chapter introduces key concepts of machine learning and how they apply to agent and multiagent systems. Rather than present a comprehensive survey, we discuss a number of issues that we believe are important in the design of learning agents and multi-agent systems.

Suitable Machine Learning Methods for Agent-Based Systems

2009

Currently, with the Internet’s massive use, electronics information growth exponentially; therefore human’s capacities to exploit them is decreased. Indeed a search for information on the World Wide Web, or a human handling of electronic documents, can become a too repetitive and fastidious task and can consumes enormous time; then it is obvious that with these advances in technologies of information, intelligent systems are increasingly necessary to automatically and intelligently exploit this large volume of information. Agent technology and Artificial intelligence, in particular machine learning, constitute powerful platforms for the development of such systems. Existing work on Learning Agent-based systems are relative to specific domains, in his paper we propose suitable machine Learning methods for Agent-based systems independently of any domain application; then we argue ours proposals by presenting the learning module of an Intelligent Agent for E-Mails automatic handling. T...

Learning and Multiagent Reasoning for Autonomous Agents IJCAI-07 Computers and Thought Paper

2007

Abstract One goal of Artificial Intelligence is to enable the creation of robust, fully autonomous agents that can coexist with us in the real world. Such agents will need to be able to learn, both in order to correct and circumvent their inevitable imperfections, and to keep up with a dynamically changing world. They will also need to be able to interact with one another, whether they share common goals, they pursue independent goals, or their goals are in direct conflict.

A Framework for Machine Learning based Multi Agent System

2017

Applications which can facilitate decision making are gaining importance. They can be found in domains like process control, air traffic control, inventory management, airline reservation etc. Developing applications for these domains have several flaws. First, they do not achieve the desired goal. Second, even if they do, their implementation and maintenance is very costly. Agent oriented system offers a qualitative change in this position. An agent learns from its environment, manipulates itself and coordinates with other agents in the system. Researchers have designed many agents but they were unable to solve complex dynamic applications of this kind. It is still an open problem for the researchers to let the agent learn and adapt as per requirements. In this paper, we addressed this issue. We have focused on design and development of multi agent system (MAS) which can reason and learn for environment. We have chosen Airline reservation as our case study.

Machine Learning For Autonomous Robotic Agents

We present some results of our research in the field of Machine Learning applied to robotics problems. In particular we have investigated on:(i) the application of Learning Classifier Systems to the synthesis of robot controllers;(ii) learning of fuzzy controllers;(iii) learning of purposeful representations of the environment;(iv) and the application of versions of Q-learning to robot training. Experimental results suggest that Machine Learning techniques might soon have an important impact on realistic robotic ...