First-order jk-clausal theories are PAC-learnable
Artificial Intelligence
Artificial Intelligence - Special volume on planning and scheduling
Relational reinforcement learning
Machine Learning - Special issue on inducive logic programming
Using background knowledge to speed reinforcement learning in physical agents
Proceedings of the fifth international conference on Autonomous agents
Scaling Up Inductive Logic Programming by Learning from Interpretations
Data Mining and Knowledge Discovery
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
Rescheduling Manufacturing Systems: A Framework of Strategies, Policies, and Methods
Journal of Scheduling
Scheduling: Theory, Algorithms, and Systems
Scheduling: Theory, Algorithms, and Systems
Distributed control of production systems
Engineering Applications of Artificial Intelligence
Top-down induction of first-order logical decision trees
Artificial Intelligence
A multi-objective ant colony system algorithm for flow shop scheduling problem
Expert Systems with Applications: An International Journal
Learning relational options for inductive transfer in relational reinforcement learning
ILP'07 Proceedings of the 17th international conference on Inductive logic programming
A holistic approach for the cognitive control of production systems
Advanced Engineering Informatics
Logical and Relational Learning
Logical and Relational Learning
Skill acquisition via transfer learning and advice taking
ECML'06 Proceedings of the 17th European conference on Machine Learning
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
With the current trend towards cognitive manufacturing systems to deal with unforeseen events and disturbances that constantly demand real-time repair decisions, learning/reasoning skills and interactive capabilities are important functionalities for rescheduling a shop-floor on the fly taking into account several objectives and goal states. In this work, the automatic generation and update through learning of rescheduling knowledge using simulated transitions of abstract schedule states is proposed. Deictic representations of schedules based on focal points are used to define a repair policy which generates a goal-directed sequence of repair operators to face unplanned events and operational disturbances. An industrial example where rescheduling is needed due to the arrival of a new/rush order, or whenever raw material delay/shortage or machine breakdown events occur are discussed using the SmartGantt prototype for interactive rescheduling in real-time. SmartGantt demonstrates that due date compliance of orders-in-progress, negotiating delivery conditions of new orders and ensuring distributed production control can be dramatically improved by means of relational reinforcement learning and a deictic representation of rescheduling tasks.