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

• Autores: Palombarini, Jorge Andrés; Martínez, Ernesto Carlos
• Año de publicación: 2010
• Idioma: inglés
• Tipo de recurso: artículo
• Materia: Batch plants; Rescheduling; Reinforcement Learning; Automated planning; Artificial intelligence; Relational modeling; Rescheduling
• Institución: Consejo Nacional de Investigaciones Científicas y Técnicas

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.
Fil: Palombarini, Jorge Andrés. Universidad Tecnológica Nacional; Argentina
Fil: Martínez, Ernesto Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentina