Extended Breadth-First Search Algorithm

TL;DR

This paper introduces an extended breadth-first search algorithm that efficiently handles large state spaces by incorporating a more accessible heuristic concept, improving search performance in AI problem-solving.

Contribution

The paper presents a novel search algorithm that manages large state spaces and simplifies heuristic integration compared to classical methods.

Findings

Handles huge state spaces effectively

Eases the embedding of heuristics into search algorithms

Improves search efficiency in AI problems

Abstract

The task of artificial intelligence is to provide representation techniques for describing problems, as well as search algorithms that can be used to answer our questions. A widespread and elaborated model is state-space representation, which, however, has some shortcomings. Classical search algorithms are not applicable in practice when the state space contains even only a few tens of thousands of states. We can give remedy to this problem by defining some kind of heuristic knowledge. In case of classical state-space representation, heuristic must be defined so that it qualifies an arbitrary state based on its "goodness," which is obviously not trivial. In our paper, we introduce an algorithm that gives us the ability to handle huge state spaces and to use a heuristic concept which is easier to embed into search algorithms.