The web is a huge and highly dynamic environment which is growing exponentially in content and developing fast in structure. No search engine can cover the whole web, thus it has to focus on the most valuable pages for crawling. So an efficient crawling algorithm for retrieving the most important pages remains a challenging issue. Several algorithms like PageRank and OPIC have been proposed. Unfortunately, they have high time complexity and low throughput. In this paper, an intelligent crawling algorithm based on reinforcement learning, called FICA is proposed that models a random surfing user. The priority for crawling pages is based on a concept we call logarithmic distance. FICA is easy to implement and its time complexity is O (E* logV) where V and E are the number of nodes and edges in the web graph respectively. Comparison of FICA with other proposed algorithms shows that FICA outperforms them in discovering highly important pages. Furthermore, FICA computes the importance (ranking) of each page during the crawling process. Thus, we can also use FICA as a ranking method for computation of page importance. A nice property of FICA is its adaptability to the web in that it adjusts dynamically with changes in the web graph. We have used UK's web graph to evaluate our approach.