Learning by observation and practice: a framework for automatic acquisition of planning operators
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
An integrated environment for knowledge acquisition
Proceedings of the 6th international conference on Intelligent user interfaces
Autonomous Learning from the Environment
Autonomous Learning from the Environment
PDDL2.1: an extension to PDDL for expressing temporal planning domains
Journal of Artificial Intelligence Research
Searching for planning operators with context-dependent and probabilistic effects
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Learning Action Models with Quantified Conditional Effects for Software Requirement Specification
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Theoretical and Methodological Issues
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To solve a problem with intelligent planning, an expert has to try his best to write a planning domain. It is hard and time-wasting. Considering software requirement as a problem to be solved by intelligent planning, it's even more difficult to write the domain, because of software requirement's feature, for instance, changeability. To reduce the difficulty, we divide the work into two tasks: one is to describe an incomplete domain of software requirement with PDDL(Level 1, Strips)[11]; the other is to complete the domain by learning from plan samples based on business processes. We design a learning tool (Learning Action Model from Plan Samples, LAMPS) to complete the second task. In this way, what an expert needs to do is to do the first task and give some plan samples. In the end, we offer some experiment result analysis and conclusion.