The structure of a Bayesian network represents a set of conditional independence relations that hold in the domain. Learning the structure of the Bayesian network model that represents a domain can reveal in sights into its underlying causal structure. Automatically lea More
The structure of a Bayesian network represents a set of conditional independence relations that hold in the domain. Learning the structure of the Bayesian network model that represents a domain can reveal in sights into its underlying causal structure. Automatically learning the graph structure of a Bayesian network is a challenge pursued within artificial intelligence studies. In this paper, a new algorithm based on learning automata is proposed for learning the structure of the Bayesian networks. In this algorithm, automata is used as a tool for searching in structure’s space (DAG’s space) of the Bayesian networks. The mathematical behavior of the proposed algorithm is studied.
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In this paper, a new criterion is introduced for solving optimization problems on stochastic graphs- as a model of computer networks-by stochastic learning Automata. This proposed method, because of considering estimated variance of response of environment, can better a More
In this paper, a new criterion is introduced for solving optimization problems on stochastic graphs- as a model of computer networks-by stochastic learning Automata. This proposed method, because of considering estimated variance of response of environment, can better adaptation to changes of environment. As a result, the proposed method can produce better response to learning Automata actions.
The proposed method, by entering a noise, can avoid learning Automata being stuck at a local optimum point. Our simulation shows that this proposed method can be improve the convergence rate of Automata-based algorithm.
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