Background:  Human coagulation factor IX (hFIX) is a glycoprotein with two N-glycosylation sites at the activation peptide. Since the activation peptide is removed in mature hFIX, the exact role of N-glycosylation is unclear. To investigate the role of N-glycosylation in the secretion and activity of hFIX, we inhibited N-glycosylation by tunicamycin in the stable Human Embryonic Kidney (HEK)- c...

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 ...

We consider optimization problems where the objective function is defined over some continuous and some discrete variables, and only noise corrupted values of the objective function are observable. Such optimization problems occur naturally in PAC learning with noisy samples. We propose a stochastic learning algorithm based on the model of a hybrid team of learning automata involved in a stocha...

We present a Python package for learning (non-)probabilistic deterministic finite state automata and provide heuristics in the red-blue framework. As our package is built along the API of the popular scikit-learn package, it is easy to use and new learning methods are easy to add. It provides PDFA learning as an additional tool for sequence prediction or classification to data scientists, witho...

A stochastic automaton can perform a finite number of actions in a random environment. When a specific action is performed, the environment responds by producing an environment output that is stochastically related to the action. The aim is to design an automaton, using an evolutionary reinforcement scheme (the basis of the learning process), that can determine the best action guided by past ac...

Automata learning has been successfully applied in the verification of hardware and software. The size of the automaton model learned is a bottleneck for scalability and hence optimizations that enable learning of compact representations are important. In this paper we develop a class of optimizations and an accompanying correctness proof for learning algorithms, building upon a general framewo...

This paper introduces a novel payoff-based learning scheme for distributed optimization in repeatedly-played strategic-form games. Standard reinforcement-based learning schemes exhibit several limitations with respect to their asymptotic stability. For example, in two-player coordination games, payoff-dominant (or efficient) Nash equilibria may not be stochastically stable. In this work, we pre...

In this paper we report some of the research endeavors we are embarking on as part of the Doctoral research of the first author. We have already completed an investigation of some of the existing algorithms in the areas of Network Routing and Traffic Engineering, and we propose superior algorithms that would adapt to the changes in the environment in which they operate. In this attempt, we inte...

Cellular Learning Automata (CLA) has been used in many fields of image processing such as noise elimination, smoothing, retrieval, fractionated and extraction of the content Characteristics of the images. The edge detection in images and methods if edge detection, have a great role in machine vision and cognizance systems. This method uses operands for analyzing images and digital image process...