Learning to repair plans and schedules using a relational (deictic) representation

• Autor(a) principal: Palombarini,J.
• Data de Publicação: 2010
• Outros Autores: Martínez,E.
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Brazilian Journal of Chemical Engineering
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-66322010000300006
• topic: Automated planning; Artificial intelligence; Batch plants; Reinforcement learning; Relational modeling; Rescheduling

Unplanned and abnormal events may have a significant impact on the feasibility of plans and schedules which requires to repair them 'on-the-fly' to guarantee due date compliance of orders-in-progress and negotiating delivery conditions for new orders. In this work, a repair-based rescheduling approach based on the integration of intensive simulations with logical and relational reinforcement learning is proposed. Based on a relational (deictic) representation of schedule states, a number of repair operators have been designed to guide the search towards a goal state. The knowledge generated via simulation is encoded in a relational regression tree for the Q-value function defining the utility of applying a given repair operator at a given schedule state. A prototype implementation in Prolog language is discussed using a representative example of three batch extruders processing orders for four different products. The learning curve for the problem of inserting a new order vividly illustrates the advantages of logical and relational learning in rescheduling.