期刊论文详细信息
Brazilian Journal of Chemical Engineering | |
Learning to repair plans and schedules using a relational (deictic) representation | |
关键词: Automated planning; Artificial intelligence; Batch plants; Reinforcement learning; Relational modeling; Rescheduling; | |
DOI : 10.1590/S0104-66322010000300006 | |
来源: DOAJ |
【 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】
Unknown