Combining Ontology-based Domain Knowledge to AutoMated Planner
There are much research into Artificial Intelligence (AI) and Semantic Web over the past few years and intelligent behaviour such as learning, analysing, problem solving, planning and abstracting is displayed by modern computer systems. Automatically acquiring control-knowledge for planning, as it is the case for Machine Learning in general, strongly depends on the training material. In planning, there is a novel ways to store examples into ontology when solving problems. This Paper presents a new architecuture for the design and development of training material, where metadata and the knowledge build into them are captured and fully reusable. These System use AI Planning and Semantic Ontology technologies, allowing to construct learning rules dynamically based on the general Domain independent Planner even from disjoint learning objects, and meeting the learners profile, preferences needs and abilitity.
Planning Ontologies translate OCL PDDL Rule SHOP2 Knowledge Reasoning Evaluation
Qian Hong JiangYunfei Qian Hong SuiMingxiang ZhangDonghui
Software Research Instiitute Sun Yat-Sen (ZhongShan) University GuangZhou, China Computer Science Department ZhongShang polytechnic college ZhongShan, China
国际会议
桂林
英文
449-454
2010-11-17（万方平台首次上网日期，不代表论文的发表时间）