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

Brazilian Journal of Chemical Engineering

J. Palombarini²  E. Martínez¹ 

[1] ,UTN-Fac. Reg. V. María,Argentina

关键词: Automated planning;    Artificial intelligence;    Batch plants;    Reinforcement learning;    Relational modeling;    Rescheduling;   

DOI  :  10.1590/S0104-66322010000300006

来源: SciELO

【 摘 要 】

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.

【 授权许可】

CC BY   
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