Comprehending action preconditions and effects is an essential step in modeling the dynamics of the world. In this paper, we express the semantics of precondition relations extracted from text in terms of planning operations. The challenge of modeling this connection is grounding language at the level of relations. This type of grounding enables us to create high-level plans based on language abstractions. Our model jointly learns to predict precondition relations from text and to perform high-level planning guided by those relations. We implement this idea in the reinforcement learning framework using feedback automatically obtained from plan execution attempts. When applied to a complex virtual world and text describing that world, our relation extraction technique performs on par with a supervised baseline, yielding an F-measure of 66% compared to the baseline's 65%. Additionally, we show that a high-level planner utilizing these extracted relations significantly outperforms a strong, text unaware baseline as measured by completed plans - successfully completing 80% as compared to 69% for the baseline.

Our method performs high-level planning guided by information extracted from text documents. These high-level plans are then converted to executable low-level plans using a standard classical planning algorithm — specifically, using Metric-FF. Depending on the characteristics of the low-level planning task, the computation time required by Metric-FF can be long. To keep experiment run-times manageble, we employ a distributed farm of Metric-FF instances, and cache the low-level plans returned by Metric-FF to avoid redundant recomputations. The following diagram shows the experimental framework — the individual components of which are described below:

Language-Aware Planner

This is our language-aware high-level planning algorithm.

Feature Computation

This is simply a pre-processing step which uses a simple text-overlap heuristic to identify any sentences that might mention multiple objects in the world, and converts the text of the sentence into the feature representation used by our method. Our model operates on this representation to identify valid precondition relations between objects as described in the text.

FF-Plan Cache

The FF-Plan Cache serves as the interface between the high-level planning algorithms and the low-level planner (Metric-FF). In this role, it performs three specific functions. First, it is caches each planning problem sent to Metric-FF, and the correponding output produced by Metric-FF. Subsequent calls to the cache with the same planning problem thus do not require the execution of Metric-FF. Second, it allows the high-level planner to test multiple low-level plans in parallel by distributing the low-level planning tasks to many instances of Metric-FF running on several machines. Third, the cache allows multiple high-level planners to simultaneously operate off a single pool of low-level planners. These functions significantly reduce experiment run-times.

Cache Client

This script manages the execution of Metric-FF on given low-level planning tasks.

Downloads

Data