Post-Design Analysis for AI Planning Applications

131 Pages·2010·2.5 MB·English

by  Tiago Stegun Vaquero

Tiago Stegun Vaquero
Post-Design Analysis for AI Planning
Applications
Thesis presented at Polytechnic School of
the University of Sa˜o Paulo for the degree
ofDoctorofMechatronicsEngineering.
Sa˜oPaulo
2010

Tiago Stegun Vaquero
Post-Design Analysis for AI Planning
Applications
Thesis presented at Polytechnic School of
the University of Sa˜o Paulo for the degree
ofDoctorofMechatronicsEngineering.
Areaofconcentration:
Mechanical Control and Automation
Engineering
Supervisor:
Prof. Dr. Jose´ Reinaldo Silva
Co-supervisor:
Prof. Dr. J. Christopher Beck
Sa˜oPaulo
2010
FichaCatalogra´fica
Vaquero,TiagoStegun
Post-Design Analysis for AI Planning Applications. / T.S. Vaquero. –
Sa˜oPaulo,2010. 111p.
Tese(Doutorado) —EscolaPolite´cnicadaUniversidadedeSa˜oPaulo.
DepartamentodeEngenhariaMecatroˆnicaedeSistemasMecaˆnicos.
1. Mecatroˆnica 2. Inteligeˆncia artificial (Planejamento) 3.
Representac¸a˜o de conhecimento 4. Design I. Universidade de Sa˜o Paulo.
Escola Polite´cnica. Departamento de Engenharia Mecatroˆnica e de Sis-
temasMecaˆnicos. II.t.
Acknowledgements
First and above all, I praise God, the almighty for providing me this opportunity and
granting me the capability to proceed successfully. This thesis appears in its current
formduetotheguidanceandcontribution,bothdirectlyandindirectly,ofseveralpeo-
ple. Iwouldthereforeliketooffermysincerethankstoallofthem.
I would firstly like to thank my supervisors, Professor Jose´ Reinaldo Silva and
ProfessorJ.ChristopherBeck,forbeinggreatadvisorsandrolemodels,forallofyour
suggestions and ideas, and for taking the time to explain and discuss all aspects of
researchtome.
Thankstoallmycolleaguesinthefollowinglabs: DesignLabattheUniversityof
Sa˜o Paulo, TIDEL at the University of Toronto, and IAAA at the University Center of
FEI–youhaveallhelpedmeatsomepoint.
Thankstomyparents,GilbertoandSoˆnia,foryearsofsupportandlove.
Lastandmost,thankstomylovelysweetwifePatr´ıciaforallherloveandsupport,
andforhergreatpatienceandunderstandings.

Abstract
Sincetheendofthe1990stherehasbeenanincreasinginterestintheapplicationofAI
planning techniques to solve real-life problems. In addition to characteristics of aca-
demic problems, such as the need to reason about actions, real-life problems require
detailed knowledge elicitation, engineering, and management. A systematic design
processinwhichKnowledgeandRequirementsEngineeringtechniquesandtoolsplay
a fundamental role is necessary in such applications. Research on Knowledge En-
gineering for planning and scheduling has created tools and techniques to support the
designprocessofplanningdomainmodels. However,giventhenaturalincompleteness
of the knowledge, practical experience in real applications such as space exploration
has shown that, even with a disciplined process of design, requirements from differ-
ent viewpoints (e.g. stakeholders, experts, users) still emerge after plan generation,
analysisandexecution.
The central thesis of this dissertation is that an post-design analysis phase in the
development of AI planning applications leads to richer knowledge models and, con-
sequently, to high-performance and high-quality plans. In this dissertation, we inves-
tigate how hidden knowledge and requirements can be acquired and re-used during
a plan analysis phase that follows model design and how they affect planning per-
formance. We describe a post-design framework called postDAM that combines (1)
a knowledge engineering tool for requirements acquisition and plan evaluation, (2) a
virtualprototypingenvironmentfortheanalysisandsimulationofplans,(3)adatabase
systemforstoringplanevaluations,and(4)anontologicalreasoningsystemforknowl-
edgere-useanddiscovery.
Our framework demonstrates that post-design analysis supports the discovery of
missing requirements and guides the model refinement cycle. We present three case
studiesusingbenchmarkdomainsandeightstate-of-the-artplanners. Ourresultsdemon-
stratethatsignificantimprovementsinplanqualityandanincreaseinplanningspeedof
uptothreeordersofmagnitudecanbeachievedthroughacarefulpost-designprocess.
We also demonstrate that rationales captured during plan evaluations from users can
be useful and reusable in further plan evaluations and in new application designs. We
argue that such a post-design process is critical for deployment of planning technol-
ogyinreal-worldapplications. Toourknowledge,thisisthefirstworkthatinvestigate
post-designanalysisforAIplanningapplications.

Resumo
Desdeofinaldade´cadade1990existeuminteressecrescentenaaplicac¸a˜odete´cnicas
deplanejamentoautoma´ticoemIApararesolverproblemasreaisdeengenharia. Ale´m
das caracter´ısticas dos problemas acadeˆmicos, tais como a necessidade de raciocinar
sobreasac¸o˜es,problemasreaisrequeremelicitac¸a˜o,engenhariaegerenciamentodetal-
hado do conhecimento do dom´ınio. Para tais aplicac¸o˜es reais, um processo de design
sistema´tico e´ necessa´rio onde as ferramentas de Engenharia do Conhecimento e de
Requisitons teˆm um papel fundamental. Esforc¸os acadeˆmicos recentes na a´rea da En-
genharia do Conhecimento em planejamento automa´tico veˆm desenvolvido ferramen-
tas e te´cnicas de apoio ao processo de design de modelos do conhecimento. Pore´m,
dadaanaturalincompletudedoconhecimento,experieˆnciapra´ticaemaplicac¸o˜esreais,
comoporexemploexplorac¸a˜odoespac¸o,temmostradoque,mesmocomumprocesso
disciplinado de design, requisitos de pontos de vista diferente (por exemplo, especial-
istas,usua´riosepatrocinadores)aindasurgirgemapo´saana´lise,gerac¸a˜oeexecuc¸a˜ode
planos.
A tese central deste texto e´ que uma fase de ana´lise de po´s-design para o desen-
volvimento de applicac¸o˜es de planejamento em IA resulta em modelos do conheci-
mento mais ricos e, consequ¨entemente, aumenta a qualidade dos planos gerados e a
performance dos planejadores automa´ticos. Neste texto, no´s investigamos como os
conhecimentos e requisitos ocultos podem ser adquiridos e reutilizados durante a fase
de ana´lise de plans (posterior ao design do modelo) e como estes conhecimentos afe-
tam o desempenho do processo de planejamento automa´tico. O texto descreve um
frameworkdepost-designchamadopostDAM quecombina(1)umaferramentadeen-
genharia de conhecimento para a aquisic¸a˜o de requisitos e avaliac¸a˜o do plano, (2) um
ambientedeprototipagemvirtualparaaana´liseesimulac¸a˜odeplanos,(3)umsistema
de banco de dados para armazenamento de avaliac¸o˜es de planos, e (4) um sistema de
racionc´ınioontolo´gicoparaore-usoedescobertadeconhecimentosobreodom´ınio.
Com o framework postDAM demostramos que a ana´lise de po´s-design auxilia a
descoberta da requisitos ocultos e orienta o ciclo de refinamento do modelo. Este
trabalho apresenta treˆs estudos de caso com dom´ınios conhecidos na literatura e oito
planejadores do estado da arte. Nossos resultados demonstram que melhorias signi-
ficativas na qualidade do plano e um aumento na velocidade dos planejadores de ate´
treˆs ordens de grandeza pode ser alcanc¸ada atrave´s de um processo disciplinado e
cuidadose de po´s-design. No´s demonstramos tambe´m que rationales provenientes dos
usua´rioscapturadosduranteasavaliac¸o˜esdeplanospodemseru´teisereutiliza´veisem
novas avaliac¸o˜es de plano e em novos projetos. No´s argumentamos que esse processo
de po´s-design e´ fundamental para a implantac¸a˜o da tecnologia de planejamento au-
toma´tico em aplicac¸o˜es do mundo real. Ate´ onde sabemos, este e´ o primeiro trabalho
que investiga a ana´lise de po´s-design em aplicac¸o˜es de planejamento automa´tico da
IA.