Add to ORKGA cooperative game played in a sequential manner by a pair of learning automata is investigated in this paper. The automata operate in an unknown random environment which gives a common pay-off to the automata. Necessary and sufficient conditions on the functions in the reinforcement scheme are given for absolute monotonicity which enables the expected pay-off to be monotonically increasing in any arbitrary environment. As each participating automaton operates with no information regarding the other partner, the results of the paper are relevant to decentralized control
A cooperative-game-playing learning automata model is presented for a complex nonlinear associative ...
Dynamic zero-sum games are a model of multiagent decision-making that has been well-studied in the m...
We consider stochastic automata models of learning systems in this article. Such learning automata s...
A cooperative game played in a sequential manner by a pair of learning automata is investigated in t...
This paper considers a multi-person discrete game with random payoffs. The distribution of the rando...
We consider the ability of economic agents to learn in a decentralized envi-ronment in which agents ...
Multi-Agent systems naturally arise in a variety of domains such as robotics, distributed control an...
A multi-person discrete game where the payoff after each play is stochastic is considered. The distr...
A multi-person discrete game where the payoff after each play is stochastic is considered. The distr...
Learning Automata (LA) were recently shown to be valuable tools for designing Multi-Agent Reinforcem...
We represent the multiple pursuers and evaders game as a Markov game and each player as a decentrali...
The multiple pursuers and evaders game may be represented as a Markov game. Using this modeling, one...
This paper introduces a novel payoff-based learning scheme for distributed optimization in repeatedl...
A cooperative-game-playing learning automata model is presented for a complex nonlinear associative ...
In this paper we summarize some important theoretical results from the domain of Learning Automata. ...
A cooperative-game-playing learning automata model is presented for a complex nonlinear associative ...
Dynamic zero-sum games are a model of multiagent decision-making that has been well-studied in the m...
We consider stochastic automata models of learning systems in this article. Such learning automata s...
A cooperative game played in a sequential manner by a pair of learning automata is investigated in t...
This paper considers a multi-person discrete game with random payoffs. The distribution of the rando...
We consider the ability of economic agents to learn in a decentralized envi-ronment in which agents ...
Multi-Agent systems naturally arise in a variety of domains such as robotics, distributed control an...
A multi-person discrete game where the payoff after each play is stochastic is considered. The distr...
A multi-person discrete game where the payoff after each play is stochastic is considered. The distr...
Learning Automata (LA) were recently shown to be valuable tools for designing Multi-Agent Reinforcem...
We represent the multiple pursuers and evaders game as a Markov game and each player as a decentrali...
The multiple pursuers and evaders game may be represented as a Markov game. Using this modeling, one...
This paper introduces a novel payoff-based learning scheme for distributed optimization in repeatedl...
A cooperative-game-playing learning automata model is presented for a complex nonlinear associative ...
In this paper we summarize some important theoretical results from the domain of Learning Automata. ...
A cooperative-game-playing learning automata model is presented for a complex nonlinear associative ...
Dynamic zero-sum games are a model of multiagent decision-making that has been well-studied in the m...
We consider stochastic automata models of learning systems in this article. Such learning automata s...