In this paper, we introduce an admissible heuristic for hybrid planning with preferences. Hybrid planning is the fusion of hierarchical task network (HTN) planning with partial order causal link (POCL) planning. We consider preferences to be soft goals - facts one would like to see satisfied in a goal state, but which do not have to hold necessarily. Our heuristic estimates the best quality of any solution that can be developed from the current plan under consideration. It can thus be used by any branch-and-bound algorithm that performs search in the space of plans to prune suboptimal plans from the search space.

The implementation is based on two insights: that many of the graph construction operations can be implemented as bit-level logical operations on bit vectors, and that the graph should not be explicitly constructed beyond the x point. A more detailed discussion of the competition, from the competitors' point of view, is in preparation. First, we observe that action pre-and post-conditions can be represented using bit vectors. Checking for mutual exclusion between pairs of actions which directly interact can be implemented using logical operations on these bit vectors. Mutual exclusion (mutex relations) between facts can be implemented in a similar way. Second, we observe that there is no advantage in explicit construction of the graph beyond the stage at which the x point is reached. Since no new facts, actions or mutex relations are added beyond the x point these goal sets can be considered without explicit copying of the fact and action layers. For example, using a heuristic discussed in Section 5.1, Sta In this paper we describe the spike and wave front mechanisms and provide experimental results indicating the performance advantages obtained. The layers correspond to snapshots of possible states at instants on a time line from the initial to the goal state.

STRIPS-style planning has proven to be a helpful methodology for narrative generation, but certain assumptions about the process remain in use which inhibit the creation of interesting stories. The sequence of actions is more important than the initial and goal state of the world, so a narrative planner should first build a plot and then adapt the world to that plot. This is possible by relaxing the closed world assumption to allow revision to the initial and goal states.