A Conceptual Approach for Knowledge Structure Update and Learning in Multi-Agent Systems
نویسنده
چکیده
The paper proposes a concept for a knowledge representation approach that consists of the knowledge structure in the form of ontology, knowledge base in the form of rules and environment model in the form of objects in the environment. The approach is developed for domains where the main problem is to choose the most appropriate capability for a particular action. Both the agents’ knowledge and also knowledge structures can be edited by the user after launching the system. An Ontology Learning Tool is implemented for this purpose. Additionally the knowledge base can be complemented by the agents themselves as a result of the learning process that is based on the user’s feedback. The proposed approach is explained in this paper on an example of vacuum cleaning multi-robot system. The implementation is done in the form that is suitable for development of JADE based multi-agent systems. The first validation of the concept is intended in a virtual multi-agent environment where JADE agents simulate cleaning robots. Key-Words: Knowledge Structure, Multi-Agent System, Ontology Learning, Machine Learning
منابع مشابه
An Online Q-learning Based Multi-Agent LFC for a Multi-Area Multi-Source Power System Including Distributed Energy Resources
This paper presents an online two-stage Q-learning based multi-agent (MA) controller for load frequency control (LFC) in an interconnected multi-area multi-source power system integrated with distributed energy resources (DERs). The proposed control strategy consists of two stages. The first stage is employed a PID controller which its parameters are designed using sine cosine optimization (SCO...
متن کاملAn Unsupervised Learning Method for an Attacker Agent in Robot Soccer Competitions Based on the Kohonen Neural Network
RoboCup competition as a great test-bed, has turned to a worldwide popular domains in recent years. The main object of such competitions is to deal with complex behavior of systems whichconsist of multiple autonomous agents. The rich experience of human soccer player can be used as a valuable reference for a robot soccer player. However, because of the differences between real and simulated soc...
متن کاملInterfirm Alliance Interactions and knowledge Learning: A Conceptual Research Model
Alliance raises many knowledge transfer and interfirm learning issues that have implications for how the alliance partners manage their cooperative learning activities in the alliance system. Many of these implications are grounded in the assumption that partners in the alliances have routines for transferring knowledge, learning, gaining management efficiencies. Thus organisations can support ...
متن کاملUtilizing Generalized Learning Automata for Finding Optimal Policies in MMDPs
Multi agent Markov decision processes (MMDPs), as the generalization of Markov decision processes to the multi agent case, have long been used for modeling multi agent system and are used as a suitable framework for Multi agent Reinforcement Learning. In this paper, a generalized learning automata based algorithm for finding optimal policies in MMDP is proposed. In the proposed algorithm, MMDP ...
متن کاملVariable Structure Behavioural Controller for Multi-agent Systems
In previous papers authors have considered agents as inertia-less self driven particles and designed a flocking algorithm. Application of this algorithm to agents with considerable inertial characteristics needs a behavioural controller. The controller uses the local information and helps every agent to imitate the desired behaviour as a member of the flocking frame which covers the main is...
متن کامل