Machine Learning
Game Theory and Decision Theory in Agent-Based Systems
Game Theory and Decision Theory in Agent-Based Systems
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
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ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Learning to Share Meaning in a Multi-Agent System
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Autonomous Agents and Multi-Agent Systems
ANEMONE: an effective minimal ontology negotiation environment
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Ontology-guided learning to improve communication between groups of agents
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
The Computer Journal
Developing Multi-Agent Systems with JADE (Wiley Series in Agent Technology)
Developing Multi-Agent Systems with JADE (Wiley Series in Agent Technology)
Ontology-guided collaborative concept learning in multiagent systems
Ontology-guided collaborative concept learning in multiagent systems
Ontology negotiation: goals, requirements and implementation
International Journal of Agent-Oriented Software Engineering
Inferring user's preferences using ontologies
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Concept Learning for Achieving Personalized Ontologies: An Active Learning Approach
Agents and Data Mining Interaction
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
Multi-agent planning as a dynamic search for social consensus
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1
The contract net: a formalism for the control of distributed problem solving
IJCAI'77 Proceedings of the 5th international joint conference on Artificial intelligence - Volume 1
Incremental Non-unanimous Concept Reformation through Queried Object Classification
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
Simple coalitional games with beliefs
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
An Introduction to MultiAgent Systems
An Introduction to MultiAgent Systems
Concept Learning Games: The Game of Query and Response
WI-IAT '10 Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
A novel approach to multiagent reinforcement learning: utilizing OLAP mining in the learning process
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A fuzzy ontology and its application to news summarization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Positive Impact of State Similarity on Reinforcement Learning Performance
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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In this paper, we intend to have a game theoretic study on the concept learning problem in a multi-agent system. Concept learning is a very essential and well-studied domain of machine learning when it is studied under the characteristics of a multi-agent system. The most important reasons are the partiality of the environment perception for any agent and also the communication holdbacks, resulting into a deep need for a collaborative protocol in favor of multi-agent transactions. Here we wish to investigate multi-agent concept learning with the help of its components, thoroughly with a game theoretic taste, esp. on the pre-learning processes. Based on two standard notations, we address the non-unanimity of concepts, classification of objects, voting and communicating protocol, and also the learning itself. In such a game of concept learning, we consider a group of agents, communicating and consulting to upgrade their ontologies based on their conceptualizations of the environment. For this purpose, we investigate the problem in two separate and standard distinctions of game theory study, cooperation and competition. Several solution concepts and innovative ideas from the multi-agent realm are used to produce an approach that contains the reasoning process of the agents in this system. Some experimentations come at the end to show the functionality of our approach. These experimentations come distinctly for both cooperative and competitive views.