Learning from Observations Chapter 18 Section 1 -3
Learning from Observations Chapter 18 Section 1 – 3
Outline ·Learning agents ·Inductive learning Decision tree learning
Outline • Learning agents • Inductive learning • Decision tree learning
Learning Learning is essential for unknown environments, i.e.,when designer lacks omniscience Learning is useful as a system construction method, -i.e.,expose the agent to reality rather than trying to write it down Learning modifies the agent's decision mechanisms to improve performance
Learning • Learning is essential for unknown environments, – i.e., when designer lacks omniscience • Learning is useful as a system construction method, – i.e., expose the agent to reality rather than trying to write it down • Learning modifies the agent's decision mechanisms to improve performance
Learning agents Performance standard Critic Sensors feedback changes Learning Performance element element knowledge learn ing Environment goals Problem experiments generator Agent Effectors
Learning agents
Learning element Design of a learning element is affected by -Which components of the performance element are to be learned What feedback is available to learn these components -What representation is used for the components ·Type of feedback: Supervised learning:correct answers for each example Unsupervised learning:correct answers not given Reinforcement learning:occasional rewards
Learning element • Design of a learning element is affected by – Which components of the performance element are to be learned – What feedback is available to learn these components – What representation is used for the components • Type of feedback: – Supervised learning: correct answers for each example – Unsupervised learning: correct answers not given – Reinforcement learning: occasional rewards