Manufacturing in the Digital Age: Exploiting Information Technologies for Product Realization
Information Systems Frontiers
Will Domain-Specific Code Synthesis Become a Silver Bullet?
IEEE Intelligent Systems
A review of machine learning in dynamic scheduling of flexible manufacturing systems
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Learning Control Knowledge for Forward Search Planning
The Journal of Machine Learning Research
Journal of Artificial Intelligence Research
Learning Linear Ranking Functions for Beam Search with Application to Planning
The Journal of Machine Learning Research
Refinement planning: status and prospectus
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Machine learning for intelligent systems
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
RECYCLE: Learning looping workflows from annotated traces
ACM Transactions on Intelligent Systems and Technology (TIST)
Optimized look-ahead tree search policies
EWRL'11 Proceedings of the 9th European conference on Recent Advances in Reinforcement Learning
Online speedup learning for optimal planning
Journal of Artificial Intelligence Research
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From the Publisher:Research on planning systems has shown that domain knowledge is crucial for effectively coping with complex, changing environments. Unfortunately, acquiring and incorporating the necessary domain knowledge can be a significant problem when building a practical planning system. The knowledge engineering process is typically time-consuming and expensive. Furthermore, if a human expert is not available it may be extremely difficult to obtain the necessary knowledge. One solution is for a system to automatically acquire domain-specific knowledge through learning. The idea of a planning system that can improve its performance with experience is very attractive. Furthermore, advances in machine learning have provided a deeper understanding of learning mechanisms relevant to acquiring such knowledge. For this reason, there is a great deal of interest in this area of artificial intelligence. This book brings together, in one volume, a set of chapters from the primary researchers in the field, presenting a picture of its current state and its likely areas for application. The chapters describe a variety of learning methods-including analogical, case-based, explanation-based, decision-tree, and reinforcement techniques-and a wide range of planning architectures, running the gamut from STRIPS-like systems to problem-reduction architectures to reactive agents. It will draw the interest of AI researchers and system developers, especially those in machine learning, planning, and scheduling, as well as researchers from other fields, such as operations research, that focus on automated planning.