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...

We study the problem of eeciently learning to play a game optimally against an unknown adversary chosen from a computationally bounded class. We both contribute to the line of research on playing games against nite automata, and expand the scope of this research by considering new classes of adversaries. We introduce the natural notions of games against recent history adversaries (whose current...

An approach for indirect spatial data extraction by learning restricted finite state automata from web documents created using Bulgarian language are outlined in the paper. It uses heuristics to generalize initial finite-state automata that recognizes only the positive examples and nothing else into automata that recognizes as larger language as possible without extracting any non-positive exam...