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 ...

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 ...

In this paper, a new algorithm which is the result of the combination of cellular learning automata and frog leap algorithm (SFLA) is proposed for optimization in continuous, static environments.At the proposed algorithm, each memeplex of frogs is placed in a cell of cellular learning automata. Learning automata in each cell acts as the brain of memeplex, and will determine the strategy of moti...

in this paper an application of cellular learning automata (cla) to vlsi placement is presented. the cla, which is introduced for the first time in this paper, is different from standard cellular learning automata in two respects. it has input and the cell neighborhood varies during the operation of cla. the proposed cla based algorithm for vlsi placement is tested on number of placement proble...

The recent work on automata whose variables and parameters are real numbers (e.g., Blum, Shub, and Smale, 1989; Koiran, 1993; Bournez and Cosnard, 1996; Siegelmann, 1996; Moore, 1996) has focused largely on questions about computational complexity and tractability. It is also revealing to examine the metric relations that such systems induce on automata via the natural metrics on their paramete...

This paper introduces the notion of multidimensional fuzzy finite tree automata (MFFTA) and investigates its closure properties from the area of automata and language theory. MFFTA are a superclass of fuzzy tree automata whose behavior is generalized to adapt to multidimensional fuzzy sets. An MFFTA recognizes a multidimensional fuzzy tree language which is a regular tree language so that for e...

Automata models of learning systems introduced in the 1960s were popularized as learning automata (LA) in a survey paper by Narendra and Thathachar (1974). Since then, there have been many fundamental advances in the theory as well as applications of these learning models. In the past few years, the structure of LA, has been modified in several directions to suit different applications. Concept...