Have a little patience

Let planners play cards

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

As every card player knows, most existing card games share a large number of actions and situations. This is the case, for instance, for stacking cards in columns according to some allowed sequence or taking cards from a deal. This is true for both multi-player and solitaire patience games. Although they have such strong similarities, every game also has some peculiarity making it different, and affecting its complexity and -at the end of the day- its enjoyability. Interestingly, from an AI planning perspective, most of the differences emerge from the problem description: domain models tend to be very similar because of the similar actions that can be performed. In this paper we envisage the exploitation of solitaire card games as a pool of interesting benchmarks. In order to "access" such benchmarks, we exploit state-of-the-art tools for automated domain model generation -LOCM and ASCoL- for creating domain models corresponding to a number of solitaires, and extracting the underlying game constraints (e.g., the initial setup, stacking rules etc.) which come from problem models. The contribution of our work is twofold. On the one hand, the analysis of the generated models, and the learning process itself, gives insights into the strengths and weaknesses of the approaches, highlighting lessons learned regarding sensitivity to observed traces. On the other hand, an experimental analysis shows that generated solitaires are challenging for the state-of-the-art of satisficing planning: therefore solitaires can provide a set of interesting and easy-to-extract benchmarks.

Original languageEnglish
Title of host publicationProceedings of the 34th Workshop of the UK Planning and Scheduling Special Interest Group
Subtitle of host publication(PlanSIG 2016)
EditorsLukas Chrpa, Simon Parkinson, Mauro Vallati
Number of pages7
Volume1782
Publication statusPublished - 2017

Publication series

NameUK Planning and Scheduling Special Interest Group 2016.
ISSN (Print)1613-0073

Fingerprint

Planning

Cite this

Jilani, R., Crampton, A., Kitchin, D., & Vallati, M. (2017). Have a little patience: Let planners play cards. In L. Chrpa, S. Parkinson, & M. Vallati (Eds.), Proceedings of the 34th Workshop of the UK Planning and Scheduling Special Interest Group: (PlanSIG 2016) (Vol. 1782). (UK Planning and Scheduling Special Interest Group 2016.).
Jilani, Rabia ; Crampton, Andrew ; Kitchin, Diane ; Vallati, Mauro. / Have a little patience : Let planners play cards. Proceedings of the 34th Workshop of the UK Planning and Scheduling Special Interest Group: (PlanSIG 2016). editor / Lukas Chrpa ; Simon Parkinson ; Mauro Vallati. Vol. 1782 2017. (UK Planning and Scheduling Special Interest Group 2016.).
@inproceedings{5089ddc7f2e948d583b9f6bf75bc4b47,
title = "Have a little patience: Let planners play cards",
abstract = "As every card player knows, most existing card games share a large number of actions and situations. This is the case, for instance, for stacking cards in columns according to some allowed sequence or taking cards from a deal. This is true for both multi-player and solitaire patience games. Although they have such strong similarities, every game also has some peculiarity making it different, and affecting its complexity and -at the end of the day- its enjoyability. Interestingly, from an AI planning perspective, most of the differences emerge from the problem description: domain models tend to be very similar because of the similar actions that can be performed. In this paper we envisage the exploitation of solitaire card games as a pool of interesting benchmarks. In order to {"}access{"} such benchmarks, we exploit state-of-the-art tools for automated domain model generation -LOCM and ASCoL- for creating domain models corresponding to a number of solitaires, and extracting the underlying game constraints (e.g., the initial setup, stacking rules etc.) which come from problem models. The contribution of our work is twofold. On the one hand, the analysis of the generated models, and the learning process itself, gives insights into the strengths and weaknesses of the approaches, highlighting lessons learned regarding sensitivity to observed traces. On the other hand, an experimental analysis shows that generated solitaires are challenging for the state-of-the-art of satisficing planning: therefore solitaires can provide a set of interesting and easy-to-extract benchmarks.",
author = "Rabia Jilani and Andrew Crampton and Diane Kitchin and Mauro Vallati",
note = "No record of this in Eprints. HN 17/10/2017",
year = "2017",
language = "English",
volume = "1782",
series = "UK Planning and Scheduling Special Interest Group 2016.",
editor = "Lukas Chrpa and Simon Parkinson and Mauro Vallati",
booktitle = "Proceedings of the 34th Workshop of the UK Planning and Scheduling Special Interest Group",

}

Jilani, R, Crampton, A, Kitchin, D & Vallati, M 2017, Have a little patience: Let planners play cards. in L Chrpa, S Parkinson & M Vallati (eds), Proceedings of the 34th Workshop of the UK Planning and Scheduling Special Interest Group: (PlanSIG 2016). vol. 1782, UK Planning and Scheduling Special Interest Group 2016.

Have a little patience : Let planners play cards. / Jilani, Rabia; Crampton, Andrew; Kitchin, Diane; Vallati, Mauro.

Proceedings of the 34th Workshop of the UK Planning and Scheduling Special Interest Group: (PlanSIG 2016). ed. / Lukas Chrpa; Simon Parkinson; Mauro Vallati. Vol. 1782 2017. (UK Planning and Scheduling Special Interest Group 2016.).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Have a little patience

T2 - Let planners play cards

AU - Jilani, Rabia

AU - Crampton, Andrew

AU - Kitchin, Diane

AU - Vallati, Mauro

N1 - No record of this in Eprints. HN 17/10/2017

PY - 2017

Y1 - 2017

N2 - As every card player knows, most existing card games share a large number of actions and situations. This is the case, for instance, for stacking cards in columns according to some allowed sequence or taking cards from a deal. This is true for both multi-player and solitaire patience games. Although they have such strong similarities, every game also has some peculiarity making it different, and affecting its complexity and -at the end of the day- its enjoyability. Interestingly, from an AI planning perspective, most of the differences emerge from the problem description: domain models tend to be very similar because of the similar actions that can be performed. In this paper we envisage the exploitation of solitaire card games as a pool of interesting benchmarks. In order to "access" such benchmarks, we exploit state-of-the-art tools for automated domain model generation -LOCM and ASCoL- for creating domain models corresponding to a number of solitaires, and extracting the underlying game constraints (e.g., the initial setup, stacking rules etc.) which come from problem models. The contribution of our work is twofold. On the one hand, the analysis of the generated models, and the learning process itself, gives insights into the strengths and weaknesses of the approaches, highlighting lessons learned regarding sensitivity to observed traces. On the other hand, an experimental analysis shows that generated solitaires are challenging for the state-of-the-art of satisficing planning: therefore solitaires can provide a set of interesting and easy-to-extract benchmarks.

AB - As every card player knows, most existing card games share a large number of actions and situations. This is the case, for instance, for stacking cards in columns according to some allowed sequence or taking cards from a deal. This is true for both multi-player and solitaire patience games. Although they have such strong similarities, every game also has some peculiarity making it different, and affecting its complexity and -at the end of the day- its enjoyability. Interestingly, from an AI planning perspective, most of the differences emerge from the problem description: domain models tend to be very similar because of the similar actions that can be performed. In this paper we envisage the exploitation of solitaire card games as a pool of interesting benchmarks. In order to "access" such benchmarks, we exploit state-of-the-art tools for automated domain model generation -LOCM and ASCoL- for creating domain models corresponding to a number of solitaires, and extracting the underlying game constraints (e.g., the initial setup, stacking rules etc.) which come from problem models. The contribution of our work is twofold. On the one hand, the analysis of the generated models, and the learning process itself, gives insights into the strengths and weaknesses of the approaches, highlighting lessons learned regarding sensitivity to observed traces. On the other hand, an experimental analysis shows that generated solitaires are challenging for the state-of-the-art of satisficing planning: therefore solitaires can provide a set of interesting and easy-to-extract benchmarks.

UR - http://www.scopus.com/inward/record.url?scp=85013249170&partnerID=8YFLogxK

UR - http://ceur-ws.org/Vol-1782/

M3 - Conference contribution

VL - 1782

T3 - UK Planning and Scheduling Special Interest Group 2016.

BT - Proceedings of the 34th Workshop of the UK Planning and Scheduling Special Interest Group

A2 - Chrpa, Lukas

A2 - Parkinson, Simon

A2 - Vallati, Mauro

ER -

Jilani R, Crampton A, Kitchin D, Vallati M. Have a little patience: Let planners play cards. In Chrpa L, Parkinson S, Vallati M, editors, Proceedings of the 34th Workshop of the UK Planning and Scheduling Special Interest Group: (PlanSIG 2016). Vol. 1782. 2017. (UK Planning and Scheduling Special Interest Group 2016.).