Using Infeasibility to Improve Abstraction-Based Heuristics

The contribution of our research is to show that the accuracy of the heuristics generated by abstraction can be improved by checking for infeasibility. What do we mean by infeasible heuristics? For a state t, the heuristic value h is infeasible if it is proved that the cost of a solution for t cannot be h. Take the sliding puzzle for example, assuming that the manhattan heuristic for state t is md(t), if md(t) is even, any odd number is infeasible. To substantiate our approach, we begin with formal definitions and lemmas. Then empirical results show the effectiveness of the approach. For more details please refer to our longer work[5].