ning agents LEARNING FROM OBSERVATIONS CHAPTER 18,SECTIONS 1-3 Outline Learning element ◇Learning agents nt is dictat d by nat type of performance e Decision tree learning Measuring learning performance tkindofedback9bbpresC upervised le: Learning Inductive learning (a.k.a.Science) ent Simplest form:learn a function from examples (tabula rasa) f is the target function +1 Learning modifies the agent's decision mechanisms to improve perfor m五a set of examples ts to learn /-why?) Learning from Observations Chapter 18, Sections 1–3 Chapter 18, Sections 1–3 1 Outline ♦ Learning agents ♦ Inductive learning ♦ Decision tree learning ♦ Measuring learning performance Chapter 18, Sections 1–3 2 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 Chapter 18, Sections 1–3 3 Learning agents Performance standard Agent Environment Sensors Effectors Performance element changes knowledge learning goals Problem generator feedback Learning element Critic experiments Chapter 18, Sections 1–3 4 Learning element Design of learning element is dictated by ♦ what type of performance element is used ♦ which functional component is to be learned ♦ how that functional compoent is represented ♦ what kind of feedback is available Example scenarios: Performance element Alpha−beta search Logical agent Simple reflex agent Component Eval. fn. Transition model Transition model Representation Weighted linear function Successor−state axioms Neural net Utility−based agent Dynamic Bayes net Percept−action fn Feedback Outcome Outcome Win/loss Correct action Supervised learning: correct answers for each instance Reinforcement learning: occasional rewards Chapter 18, Sections 1–3 5 Inductive learning (a.k.a. Science) Simplest form: learn a function from examples (tabula rasa) f is the target function An example is a pair x, f(x), e.g., O O X X X , +1 Problem: find a(n) hypothesis h such that h ≈ f given a training set of examples (This is a highly simplified model of real learning: – Ignores prior knowledge – Assumes a deterministic, observable “environment” – Assumes examples are given – Assumes that the agent wants to learn f—why?) Chapter 18, Sections 1–3 6