One of the most promising trends in Domain-Independent AI Planning, nowadays, is state-space heuristic planning. The planners of this category construct general but efficient heuristic functions, which are used as a guide to traverse the state space either in a forward or in a backward direction. Although specific problems may favor one or the other direction, there is no clear evidence why any of them should be generally preferred. This paper presents Hybrid-AcE, a domain-independent planning system that combines search in both directions utilizing a complex criterion that monitors the progress of the search, to switch between them. Hybrid AcE embodies two powerful domain-independent heuristic functions extending one of the AcE planning systems. Moreover, the system is equipped with a fact-ordering technique and two methods for problem simplification that limit the search space and guide the algorithm to the most promising states. The bi-directional system has been tested on a variety of problems adopted from the AIPS planning competitions with quite promising results. Motivated by the work of Drew McDermott in the mid-90’s on heuristic state-space planning, a number of researchers turned to this direction. During the last few years a great amount of work has been done in the area of domain-independent, state-space, heuristic planning, and a significant number of planning systems with remarkable performance were developed. Hector Geffner in his recent work on HSP-2 (Bonet and Geffner 2001) studies the matter of search direction. The HSP-2 planning system enables the user to decide for the direction of the search. It is clear from the experimental results that there are specific problems, which favor one or the other search directions, but in general there is no clear evidence why any of the two directions should be preferred. In this paper, we present a planning system for domain-independent, heuristic planning called Hybrid AcE that combines both progression (forward chaining) and regression (backward chaining). The system utilizes an improved version of the bi-directional search strategy of the BP (bi-directional planner) planning system among with two powerful heuristic functions based on the AcE (action evaluation) planning system. The search begins from the Initial State and proceeds with a weighted A ∗ search until the heuristic function is no longer capable of guiding the search. At that point the algorithm changes direction and regress the Goals trying to reach the best state found at the previous step. The direction of the search may change several times before a solution can be found. Each time the system changes direction, the appropriate heuristic is reconstructed and this enables the system to update the heuristic functions when needed and attack each part of the problem searching in the most appropriate direction. The heuristic functions of Hybrid AcE work in two phases. The larger part of the calculations is performed off-line in a preplanning phase where each action is graded based on the goals of the problem. During search, these grades are used for estimating the distances between intermediate states and the goals. Apart from the heuristic functions, Hybrid AcE embodies two powerful domain-independent heuristic functions extending the heuristic function of the AcE planning system. The planning system is also equipped with two methods, which reduce the quantity of information provided to the search algorithms to the minimum required to solve the problem.