Many real-world planning problems require sophisticated reasoning about numeric resources, including sharing of resources by different actions. Constraintbased planners offer machinery to represent complex constraints and dependencies between actions. However, such planners usually consider a single partial plan at a time, and limit themselves with finding a feasible solution. Alternatively, pl...

The paper introduces a novel technique for improving the performance and scalability of best-first progression-based conformant planners. The technique is inspired by different wellknown techniques from classical planning, such as landmark and stratification. Its most salient feature is that it is relatively cheap to implement yet quite effective when applicable. The effectiveness of the propos...

Planning under uncertainty is a research subject that has attracted big attention in the past few years. A planner that operates under uncertainty must take into consideration that actions may have several different outcomes, some of which may be more desirable than others. The first developed decision support planners (DSP), use conventional Operations Research (OR) tools such as decision theo...

General planners enable to describe unrestricted classes of planning problems, but have the drawback of being relatively slow when compared to the performance of domain-specific planners within their domains. This slowness proves to be a major handicap when faced with planning and executing in a real world environment. In order to overcome this, we have investigated the idea of using domain-spe...

Despite their near dominance, heuristic state search planners still lag behind disjunctive planners in the generation of parallel plans in classical planning. The reason is that directly searching for parallel solutions in state space planners would require the planners to branch on all possible subsets of parallel actions, thus increasing the branching factor exponentially. We present a varian...