# SLIM: Semi-Lazy Inference Mechanism for Plan Recognition

arXiv: https://arxiv.org/abs/1703.00838 · HTML: https://mlsanity.com/paper/1703.00838/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Mar 2017.

## Built on by (most cited first, 3 of 3)

- [ODGR: Online Dynamic Goal Recognition](https://mlsanity.com/paper/2407.16220/) (2024)
- [GRAIL: Goal Recognition Alignment through Imitation Learning](https://mlsanity.com/paper/2602.14252/) (2026)
- [A Probabilistic Framework for Hierarchical Goal Recognition](https://mlsanity.com/paper/2604.22256/) (2026)

## Abstract (arXiv)

Plan Recognition algorithms require to recognize a complete hierarchy explaining the agent's actions and goals. While the output of such algorithms is informative to the recognizer, the cost of its calculation is high in run-time, space, and completeness. Moreover, performing plan recognition online requires the observing agent to reason about future actions that have not yet been seen and maintain a set of hypotheses to support all possible options. This paper presents a new and efficient algorithm for online plan recognition called SLIM (Semi-Lazy Inference Mechanism). It combines both a bottom-up and top-down parsing processes, which allow it to commit only to the minimum necessary actions in real-time, but still provide complete hypotheses post factum. We show both theoretically and empirically that although the computational cost of this process is still exponential, there is a significant improvement in run-time when compared to a state of the art of plan recognition algorithm.