Rational agents

“A rational agent chooses whichever action maximizes the expected value of the performance measure given the percept sequence to date and prior environment knowledge.”

Agents

agent function maps percept sequence to actions ($f: P* \rightarrow A$)

function is internally represented by agent program

program runs on physical architecture to produce f

Rationality

what is rational at a specific time depends on: * expected value of performance measure – heuristics * actions and choices – search * percept sequence to date – learning * prior environment– KR

rationality is not omniscience or perfection

Task environments

to design rational agent, we must specify environment (PEAS): * performance: safety, destination, profits, legality, comfort * environment: streets, traffic, pedestrians, weather * actuators: steering, accelerating, brake, horn, speaker/display * sensors: video, sonar, speedometer, etc.

environment types: * observable: fully (can detect all relevant aspects with sensors) or partially * deterministic: (yes or no) * static: (yes, no, semi) * discrete: (yes or no) * single-agent: (yes or no)

For Schnapsen:

• observable: not fully
• deterministic: yes
• static: yes
• discrete: yes
• single-agent: no

Agent types

Simple Reflex

select action on basis of only the current percept

large reduction in possible percept/action situations

implemented using condition-action rules

only works if environment is fully observable, otherwise may result in infinite loops.

Reflex & State

to tackle partially observable environments, maintain internal state

over time, update state using world knowledge.

Goal-Based

agent needs a goal to know the desirable situations

future is taken into account

Learning

teach agents instead of instructing them

very robust toward initially unknown environments.