Learning Hierarchical Performance Knowledge by Observation

Developing automated agents that intelligently perform complex real world tasks is time consuming and expensive. The most expensive part of developing these intelligent task performance agents involves extracting knowledge from human experts and encoding it into a form useable by automated agents. Machine learning from a sufficiently rich and focused knowledge source can significantly reduce the cost of developing intelligent performance agents by automating the knowledge acquisition and encoding process. Potential knowledge sources include instructions from human experts, experiments performed in the task environment and observation of an expert performing the task. Observation is particularly well suited to learning hierarchical performance knowledge for tasks that require realistic, human-like behavior. Our learning by observation system, called KnoMic (Knowledge Mimic), extracts knowledge from observations of an expert performing a task and generalizes this knowledge into rules th...

• Type: Conference
• Year: 1999
• Where: ICML
• Authors: Michael van Lent, John E. Laird
• Keywords: ICML 1999, Knowledge Sources, Machine Learning, Performance Knowledge, Task Performance