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Goal Recognition via LP-Constraints

Build Status

DOI

A goal recognizer that uses Linear Programming Heuristics from Automated planning to compute most likely goals. If you find this research useful in your research, place cite the following paper:

@inproceedings{Santos2021,
author = {Lu\'{i}sa R. de A. Santos and Felipe Meneguzzi and Ramon F. Pereira and Andr\'{e} Pereira},
title = {{An LP-Based Approach for Goal Recognition as Planning}},
booktitle = {Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI)},
year = {2021},
publisher = {AAAI Press}
}

Installation

LP-Recognizer is written mostly in Python, but we rely heavily in the constraints generated by the heurstic functions in the Fast Downward planner, so configuration is a two-step process.

  1. Configure IBM CPLEX (we need this to enable the OperatorCounting heuristic plugin in Fast Downward)
  2. Download and configure Fast Downward and apply a patch with our modifications for it.

We use a customized build of Fast Downward, so we provide automated scripts to download and build the requirements for a successful compilation (since there is a sign up required for CPLEX, we cannot automate that). Once you have CPLEX configure, you need to run the follow commands to be able to run LP-Recognizer:

git clone https://github.com/pucrs-automated-planning/lp-recognizer.git
cd lp-recognizer
bash prepare-fd.sh #This should download Fast Downward (and dependencies) apply patches and compile

Note that the above command will download many dependencies, including the Open Solver Interface (OSI) and SoPlex, compile them, and link them to Fast Downward.

Running LP-Recognizer

Running plan recognition in a single problem

Our Python code was based off of Ramirez and Geffner's original recognizer, so experiments are in the form of a tar.bz2 file containing:

  • A PDDL Domain
  • An observation sequence
  • A number of hypotheses (possible goal formulas)
  • A PDDL Problem Template (containing the initial state)

This recognizer is compatible with all the domains in this publicly available dataset of Goal and Plan Recognition Datasets. We currently implement three different approaches to goal recognition based on operator counts:

  1. Comparing overlap of observations and operator counts, accessible with the hvalue heuristic
  2. Minimizing the operator counts subject to constraints derived from the observations (i.e. all observations must be included in the counts), accessible with the hvaluec and hvaluecu (measuring uncertainty) heuristics
  3. Minimizing the difference between the operator counts with the observation constraints and the operator counts with then, accessible with the delta and deltau (measuring uncertainty) heuristics

It is also possible to add parameters in the heuristic name:

  1. Using noisy filters by adding -f followed by a number x that means 0.x of noise. Example: delta-f2 for 20% of noise
  2. Change the constraint sets by adding -c followed by l (landmarks), s (state equation), p (post-hoc), or combations. Example: delta-clp for landmarks with post-hoc constraints
  3. Use IP instead of LP by adding -i. Example: delta-i-cs for IP using only state equation constraints

To run any experiment, just run:

python test_instance.py -r <heuristics> -e <experiment_file>

Where <experiment_file> is one of the experiments in your dataset. For example, with the experiments we provide here, we could run sokoban with the hard constraints strategy as follows:

./test_instance.py -r deltac -e experiments/small-sokoban-optimal/10/sokoban_p01_hyp-1_10_1.tar.bz2

Running plan recognition in a set of experiments

In order to run experiments for an entire domain organized in a folder named domain, you need to run:

./test_domain.py <path> <domain> <heuristics>

For example, to run the sokoban sample using all the heuristics, you need to run:

./test_domain.py "experiments/" small-sokoban-optimal "delta deltau delta-f2"

Running plan recognition experiments from AAAI paper

In order to run all of the experiments in our paper, you need to run get_results.sh from the experiments folder, which will run every single domain for all approaches.

cd experiments;./get_results.sh -rerun

Note that since the dataset is pretty large, this takes a very long time to finish.

Commit hacks (changes) to Fast Downward here

We store our changes to Fast Downward in the fd-patch.diff patch file in this repository. Whenever you change Fast Downward for LP-Recognizer, ensure these changes are stored here by running:

bash make-fd-patch.sh
git commit -m "Storing patches to Fast Downward" fd-patch.diff fd-patch-rev.txt
git push