Situated agents which use learning real-time search are well poised to address challenges of real-time path-finding in robotic and computer game applications. They interleave a local lookahead search with movement execution, explore an initially unknown map, and converge to better paths over repeated experiences. In this paper, we first investigate how three known extensions of the most popular learning real-time search algorithm (LRTA*) influence its performance in a path-finding domain. Then, we combine automatic state abstraction with learning real-time search. Our scheme of dynamically building a state abstraction allows us to generalize updates to the heuristic function, thereby speeding up learning. The novel algorithm converges up to 80 times faster than LRTA* with only one fifth of the response time of A*.