Learning automata act in a stochastic environment and are able to update their action probabilities considering the inputs from their environment, so optimizing their functionality as a result. In this paper, the goal is to investigate and evaluate the application of learning automata to cooperation in multiagent systems, using soccer simulation server as a test bed. We have also evaluated our ...

Active learning of register automata infers extended finite state machines (EFSMs) with registers for storing values from a possibly infinite domain, and transition guards that compare data parameters to registers. In this paper, we present RALib, an extension to the LearnLib framework for automata learning. RALib provides an extensible implementation of active learning of register automata, to...

Cellular automata-based evolutionary development is presented for the design of single-function and polymorphic (two-function) combinational circuits. The impact of evolution of the cellular automaton initial state on the success rate of the evolved solutions is investigated. The experiments show that it is more suitable to fix a proper initial state in order to increase the successfulness and ...

Learning Automata (LA) were recently shown to be valuable tools for designing Multi-Agent Reinforcement Learning algorithms. One of the principal contributions of LA theory is that a set of decentralized, independent learning automata is able to control a finite Markov Chain with unknown transition probabilities and rewards. We extend this result to the framework of Multi-Agent MDP’s, a straigh...

In electronic commerce markets, agents often should acquire multiple resources to fulfil a high-level task. In order to attain such resources they need to compete with each other. In multi-agent environments, in which competition is involved, negotiation would be an interaction between agents in order to reach an agreement on resource allocation and to be coordinated with each other. In recent ...

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...

This paper presents one-stack automata as acceptors of context-free languages; these are equivalent to Pushdown Automata which are well known in automata theory. As equivalence relations such as equivalence of Turing Machines and two-stack Pushdown Automata help in learning general properties of formal modeling, the equivalence relation of Pushdown Automata and one-stack automata also helps in ...