Authors:
Yulia Korukhova
and
Sergey Kuryshev
Affiliation:
M.V. Lomonosov Moscow State University, Russian Federation
Keyword(s):
Multi-agent Systems, Neural Networks, Dominated Strategies.
Related
Ontology
Subjects/Areas/Topics:
Agents
;
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Distributed and Mobile Software Systems
;
Enterprise Information Systems
;
Evolutionary Computing
;
Formal Methods
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Knowledge Discovery and Information Retrieval
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Machine Learning
;
Methodologies and Methods
;
Multi-Agent Systems
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Planning and Scheduling
;
Sensor Networks
;
Signal Processing
;
Simulation and Modeling
;
Soft Computing
;
Software Engineering
;
Symbolic Systems
;
Task Planning and Execution
;
Theory and Methods
Abstract:
The paper deals with multi-agent system that represents trading agents acting in the environment with
imperfect information. Fictitious play algorithm, first proposed by Brown in 1951, is a popular theoretical
model of training agents. However, it is not applicable to larger systems with imperfect information due to
its computational complexity. In this paper we propose a modification of the algorithm. We use neural
networks for fast approximate calculation of the best responses. An important feature of the algorithm is the
absence of agent’s a priori knowledge about the system. Agents’ learning goes through trial and error with
winning actions being reinforced and entered into the training set and losing actions being cut from the
strategy. The proposed algorithm has been used in a small game with imperfect information. And the ability
of the algorithm to remove iteratively dominated strategies of agents' behavior has been demonstrated.