In real world planning problems, time for deliberation is often limited. Anytime planners are well suited for these problems: they find a feasi- ble solution quickly and then continually work on improving it until time runs out. In this paper we propose an anytime heuristic search, ARA, which tunes its performance bound based on available search time. It starts by finding a suboptimal solution quickly using a loose bound, then tightens the bound progressively as time allows. Given enough time it finds a provably optimal solution.

Identifying a small number of features that can represent the data is believed to be NP-hard. Previous approaches exploit algebraic structure and use randomization. We propose an algorithm based on ideas similar to the Weighted A* algorithm in heuristic search. Our experiments show this new algorithm to be more accurate than the current state of the art.