Multiaction learning automata which update their action probabilities on the basis of the responses they get from an environment are considered in this paper. The automata update the probabilities according to whether the environment responds with a reward or a penalty. Learning automata are said to possess ergodicity of the mean if the mean action probability is the state probability (or uncon...

Social networks are streaming, diverse and include a wide range of edges so that continuously evolves over time and formed by the activities among users (such as tweets, emails, etc.), where each activity among its users, adds an edge to the network graph. Despite their popularities, the dynamicity and large size of most social networks make it difficult or impossible to study the entire networ...

This paper presents adaptive finite state automata as an alternative formalism to model individuals in a genetic algorithm environment. Adaptive finite automata, which are basically finite state automata that can change their internal structures during operation, have proven to be an attractive way to represent simple learning strategies. We argue that the merging of adaptive finite state autom...

We present a new algorithm IDS for incremental learning of deterministic finite automata (DFA). This algorithm is based on the concept of distinguishing sequences introduced in [Angluin 1981]. We give a rigorous proof that two versions of this learning algorithm correctly learn in the limit. Finally we present an empirical performance analysis that compares these two algorithms, focussing on le...

In this report, a novel approach to intelligence and learning is introduced; this approach is based upon what we called perception logic. What we call ‘perception automata’ is introduced in which learning is accomplished at different perception resolution. Learning in this automata is not heuristic, rather it guarantees the convergence of the approximated function to whatever precision required...

The present paper establishes the learnability of simple deterministic finitememory automata via membership and equivalence queries. Simple deterministic finite-memory automata are a subclass of deterministic finite-memory automata introduced by Kaminski and Francez [9] as a model generalizing finite-state automata to infinite alphabets. For arriving at a meaningful learning model we first prov...

PSO, like many stochastic search methods, is very sensitive to efficient parameter setting such that modifying a single parameter may cause a considerable change in the result. In this paper, we study the ability of learning automata for adaptive PSO parameter selection.We introduced two classes of learning automata based algorithms for adaptive selection of value for inertiaweight and accelera...