This Python package computes several diversity models using different distance metrics for plan sets in SAS+ format. Also it wraps IBM's diversescore.
- MaxSum: The sum of pairwise distance between plans in a provided set of plans.
- MaxMean: Average sum of the pairwise distance between plans in a provided set of plans.
- MaxMin: The minimum distance between a plans in a provided set of plans.
- Stability: Jaccard measure between two plan's actions.
- States: Average jaccard measure between two plan's states.
- Uniqueness: Set difference between two plan's actions.
- Causal-links: Jaccard measure between two plan's casual links.
python -m pip install git+
DiverseScore has a cli, which can be invoked as follows:
diversescorecli [-h] [-domain DOMAIN] [-problem problem.pddl] [-diversity-model {MaxSum,MaxMin,MaxMean}] [-metric {States,Stability,Uniqueness}] [-plansdir PLANSDIR]
DiverseScore can be integrated into any Python project, here is a simple code to compute the MaxSum score using Stability Metric.
import os
from unified_planning.shortcuts import *
from diversescore.shortcuts import *
domain=<Path-to-domain-PDDL-file>
problem=<Path-to-problem-PDDL-file>
plansdir=<Path-to-plans-dir>
# Read the domain and problem PDDL files
task = PDDLReader().parse_problem(domain, problem)
# Ground the problem
with Compiler(problem_kind=task.kind, compilation_kind=CompilationKind.GROUNDING) as grounder:
groudned_task = grounder.compile(task, compilation_kind=CompilationKind.GROUNDING)
# Load the plans from directory
plans = loadPlansDir(plansdir)
maxsum_stability_score = MaxSum(Stability(groudned_task))
This package implements the distance fucntions proposed by:
@article{nguyen2012generating,
title={Generating diverse plans to handle unknown and partially known user preferences},
author={Nguyen, Tuan Anh and Do, Minh and Gerevini, Alfonso Emilio and Serina, Ivan and Srivastava, Biplav and Kambhampati, Subbarao},
journal={Artificial Intelligence},
volume={190},
pages={1--31},
year={2012},
publisher={Elsevier}
}
As for the diversity models are based on:
@article{parreno2021measuring,
title={Measuring diversity. A review and an empirical analysis},
author={Parre{\~n}o, Francisco and {\'A}lvarez-Vald{\'e}s, Ram{\'o}n and Mart{\'\i}, Rafael},
journal={European Journal of Operational Research},
volume={289},
number={2},
pages={515--532},
year={2021},
publisher={Elsevier}
}
@inproceedings{Sandoya2018DiversityAE,
title={Diversity and Equity Models},
author={Fernando Sandoya and Anna Mart{\'i}nez-Gavara and Ricardo Aceves and Abraham Duarte and Rafael Mart{\'i}},
booktitle={Handbook of Heuristics},
year={2018}
}
If you are using IBM's score computation you should cite:
@InProceedings{katz-sohrabi-aaai2020,
title = "Reshaping diverse planning",
author = "Michael Katz and Shirin Sohrabi",
booktitle = "Proceedings of the Thirty-Fourth {AAAI} Conference on
Artificial Intelligence ({AAAI} 2020)",
publisher = "{AAAI} Press",
pages = "9892--9899",
year = "2020"
}
@InProceedings{katz-et-al-aaai2022,
title = "Bounding Quality in Diverse Planning",
author = "Michael Katz and Shirin Sohrabi and Octavian Udrea",
booktitle = "Proceedings of the Thirty-Sixth {AAAI} Conference on
Artificial Intelligence ({AAAI} 2022)",
publisher = "{AAAI} Press",
year = "2022"
}
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