Macro-operators: a weak method for learning
Artificial Intelligence - Lecture notes in computer science 178
O-Plan: the open planning architecture
Artificial Intelligence
Why EBL produces overly-specific knowledge: a critique of the PRODIGY approaches
ML92 Proceedings of the ninth international workshop on Machine learning
The computational complexity of propositional STRIPS planning
Artificial Intelligence
Lazy Incremental Learning of Control Knowledge for EfficientlyObtaining Quality Plans
Artificial Intelligence Review - Special issue on lazy learning
Engineering and compiling planning domain models to promote validity and efficiency
Artificial Intelligence
Learning action strategies for planning domains
Artificial Intelligence
Using temporal logics to express search control knowledge for planning
Artificial Intelligence
Artificial Intelligence - Special issue on heuristic search in artificial intelligence
Planning and Learning by Analogical Reasoning
Planning and Learning by Analogical Reasoning
Learning Search Control Knowledge: An Explanation-Based Approach
Learning Search Control Knowledge: An Explanation-Based Approach
A Constraint-Based Method for Project Scheduling with Time Windows
Journal of Heuristics
Using genetic programming to learn and improve control knowledge
Artificial Intelligence
Explanation-Based Generalization: A Unifying View
Machine Learning
Knowledge Representation Issues in Control Knowledge Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Learning Declarative Control Rules for Constraint-BAsed Planning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
PRODIGY 4.0: The Manual and Tutorial
PRODIGY 4.0: The Manual and Tutorial
Flexible reuse and modification in hierarchical planning: a validation structure-based approach
Flexible reuse and modification in hierarchical planning: a validation structure-based approach
Automated Planning: Theory & Practice
Automated Planning: Theory & Practice
Planning graph as a (dynamic) CSP: exploiting EBL, DDB and other CSP search techniques in Graphplan
Journal of Artificial Intelligence Research
Learning to improve both efficiency and quality of planning
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
The FF planning system: fast plan generation through heuristic search
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research
Fast planning through planning graph analysis
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Assisting Data Mining through Automated Planning
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
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Artificial intelligence (AI) planning solves the problem of generating a correct and efficient ordered set of instantiated activities, from a knowledge base of generic actions, which when executed will transform some initial state into some desirable end-state. There is a long tradition of work in AI for developing planners that make use of heuristics that are shown to improve their performance in many real world and artificial domains. The developers of planners have chosen between two extremes when defining those heuristics. The domain-independent planners use domain-independent heuristics, which exploit information only from the ‘syntactic’ structure of the problem space and of the search tree. Therefore, they do not need any ‘semantic’ information from a given domain in order to guide the search. From a knowledge engineering (KE) perspective, the planners that use this type of heuristics have the advantage that the users of this technology need only focus on defining the domain theory and not on defining how to make the planner efficient (how to obtain ‘good’ solutions with the minimal computational resources). However, the domain-dependent planners require users to manually represent knowledge not only about the domain theory, but also about how to make the planner efficient. This approach has the advantage of using either better domain-theory formulations or using domain knowledge for defining the heuristics, thus potentially making them more efficient. However, the efficiency of these domain-dependent planners strongly relies on the KE and planning expertise of the user. When the user is an expert on these two types of knowledge, domain-dependent planners clearly outperform domain-independent planners in terms of number of solved problems and quality of solutions. Machine-learning (ML) techniques applied to solve the planning problems have focused on providing middle-ground solutions as compared to the aforementioned two extremes. Here, the user first defines a domain theory, and then executes the ML techniques that automatically modify or generate new knowledge with respect to both the domain theory and the heuristics. In this paper, we present our work on building a tool, PLTOOL (planning and learning tool), to help users interact with a set of ML techniques and planners. The goal is to provide a KE framework for mixed-initiative generation of efficient and good planning knowledge.