4.3 Agents, rationality and PEAS
You can write a PEAS description for a real task and judge an agent by the decisions it makes rather than the outcome it gets.
Before:01. Mathematics for Machine Learning02. Python — Basics to Advanced
An agent is anything that perceives and acts; rationality is doing the best expected thing given what it knows — not being right. The PEAS description — performance measure, environment, actuators, sensors — is the practice of writing the task down before designing anything, worked here in full for the automated taxi. The confusion the topic exists to kill is judging agents by outcomes: a rational agent can lose, and an irrational one can get lucky, and only the decision quality tells them apart.
Work through these
Define agent, sensors, actuators and percept sequence
An agent perceives through sensors and acts through actuators, and what it has perceived so far is its entire basis for deciding. Defining these four terms precisely is what makes the rest of the topic tractable.
State the four things rationality depends on
Rationality is judged against the performance measure, what the agent knows in advance, what it can perceive, and what it can do. Missing any of the four makes a judgement of rationality meaningless.
Write a PEAS description: performance measure, environment, actuators, sensors
A structured description naming the performance measure, the environment, the actuators and the sensors. Writing one forces you to make design assumptions explicit before designing anything.
Work a full PEAS for a real task — the automated taxi
A complete worked description for a self-driving taxi, which is the standard example because every part of it is contested. Doing it properly reveals how much of the difficulty is in the performance measure.
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