Automated Task Rescheduling using Relational Markov Decision Processes with Logical State Abstractions

Generating and representing knowledge about heuristics for repair-based scheduling is a key issue in any rescheduling strategy to deal with unforeseen events and disturbances. Resorting to a feature-based representation of schedule states is very inefficient and generalization to unseen states is hi...

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Detalles Bibliográficos
Autores principales: Palombarini, Jorge, Martínez, Ernesto
Formato: Objeto de conferencia
Lenguaje:Inglés
Publicado: 2012
Materias:
Ciencias Informáticas
Rescheduling
Relational Markov Decision Process
Manufacturing Systems
Reinforcement Learning
Abstract States
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/123726
https://41jaiio.sadio.org.ar/sites/default/files/6_ASAI_2012.pdf
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-123726
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
Rescheduling
Relational Markov Decision Process
Manufacturing Systems
Reinforcement Learning
Abstract States
spellingShingle Ciencias Informáticas
Rescheduling
Relational Markov Decision Process
Manufacturing Systems
Reinforcement Learning
Abstract States
Palombarini, Jorge
Martínez, Ernesto
Automated Task Rescheduling using Relational Markov Decision Processes with Logical State Abstractions
topic_facet Ciencias Informáticas
Rescheduling
Relational Markov Decision Process
Manufacturing Systems
Reinforcement Learning
Abstract States
description Generating and representing knowledge about heuristics for repair-based scheduling is a key issue in any rescheduling strategy to deal with unforeseen events and disturbances. Resorting to a feature-based representation of schedule states is very inefficient and generalization to unseen states is highly unreliable whereas the acquired knowledge is difficult to transfer to similar scheduling domains. In contrast, first-order relational representations enable the exploitation of the existence of domain objects and relations over these objects, and enable the use of quantification over objectives (goals), action effects and properties of states. In this work, a novel approach which formalizes the rescheduling problem as a Relational Markov Decision Process integrating first-order (deictic) representations of (abstract) schedule states is presented. The proposed approach is implemented in a real-time rescheduling prototype, allowing an interactive scheduling strategy that may handle different repair goals and disruption scenarios. The industrial case study vividly shows how relational abstractions provide compact repair policies with less computational efforts.
format Objeto de conferencia
Objeto de conferencia
author Palombarini, Jorge
Martínez, Ernesto
author_facet Palombarini, Jorge
Martínez, Ernesto
author_sort Palombarini, Jorge
title Automated Task Rescheduling using Relational Markov Decision Processes with Logical State Abstractions
title_short Automated Task Rescheduling using Relational Markov Decision Processes with Logical State Abstractions
title_full Automated Task Rescheduling using Relational Markov Decision Processes with Logical State Abstractions
title_fullStr Automated Task Rescheduling using Relational Markov Decision Processes with Logical State Abstractions
title_full_unstemmed Automated Task Rescheduling using Relational Markov Decision Processes with Logical State Abstractions
title_sort automated task rescheduling using relational markov decision processes with logical state abstractions
publishDate 2012
url http://sedici.unlp.edu.ar/handle/10915/123726
https://41jaiio.sadio.org.ar/sites/default/files/6_ASAI_2012.pdf
work_keys_str_mv AT palombarinijorge automatedtaskreschedulingusingrelationalmarkovdecisionprocesseswithlogicalstateabstractions
AT martinezernesto automatedtaskreschedulingusingrelationalmarkovdecisionprocesseswithlogicalstateabstractions
bdutipo_str Repositorios
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