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## 1. Introduction

Application of learning techniques to planning is an area of long standing research interest. Most work in this area to-date has, however, only considered learning domain physics or search control. The relatively neglected alternative, and the focus of this work, is learning preferences. It has long been understood that users may have complex preferences on plans (c.f. [1]). An effective representation for preferences (among other possible purposes) is a Hierarchical Task Network (HTN). In addition to domain physics (in terms of primitive actions and their preconditions and effects), the planner is provided with a set of tasks (nonprimitives) and methods (schemas) for reducing each into a combination of primitives and sub-tasks (which must then be reduced in turn). A plan (sequence of primitive actions) is considered valid if and only if it (a) is executable and achieves every specified goal, and (b) can be produced by recursively reducing a specified task (the top-level task).

For the example in Figure 1 the top level task is to travel (and the goal is to arrive at some particular destination); acceptable methods reduce the travel task to either Gobytrain or *Gobybus*. In contrast, the plan of hitch-hiking (modeled as a single action), while executable and goal achieving, is not considered valid —
the user in question loathes that mode of travel. In this way we can separately model physics and (boolean) preferences; to accommodate degree of preference (i.e., more than just accept/loathe) we attach probabilities to the methods reducing tasks (and equate probable with preferred), arriving at probabilistic Hierarchical Task Networks (pHTNs).

While pHTNs can effectively model preferences, manual construction (i.e.,