The document discusses agents and environments. An agent is anything that perceives its environment through sensors and acts upon the environment through actuators. A rational agent acts to achieve the best outcome or expected outcome based on its environment. Environments can vary in terms of observability, determinism, structure, dynamics, and continuity. Examples of agents discussed include a vacuum cleaner agent and an email spam filter agent. The key components of an agent and its environment are also outlined.

1. Agents

2. Intelligent Agents
• Rational agent: one that behaves as well as
possible
• This behavior depends on the environment
• Some environments are more difficult than
others

3. Agents and Environments
• An agent is anything that can be viewed as
perceiving its environment through sensors
and acting upon that environment through
actuators
• An agents behavior is described by the agent
function that maps any given percept
sequence to an action

5. Rational Agents
• In this course we will focus on Rational
Agents
An agent is just something that acts (agent
comes from the Latin agere, to do).
A rational agent is one that acts so as to
achieve the best outcome or, when there is
uncertainty, the best expected outcome

6. How to describe an Agent
• What is the Environment?
• What type of Sensors it requires?
• Which Actuators are required?
• What Percepts it is getting via sensors from
environment?
– Percept Sequence
• Agent Function (map percepts or percept sequence to
action)?
– Agent Program
• Performance Measure: that evaluated the effect of
actions

7. Example of Agent
• Agent: Vacuum Cleaner
• Environment: Area A and B
• Sensor: Camera
• Percept: Area clean or not
• Actuator:
• Action: Move left, Move Right,
– Start cleaning
• Agent Function: on next slide
• Performance Measure?

8. Vacuum-cleaner world
• Percepts:
Location and status,
e.g., [A,Dirty]
• Actions:
Left, Right, Suck, NoOp
Example vacuum agent program:
function Vacuum-Agent([location,status]) returns an action
• if status = Dirty then return Suck
• else if location = A then return Right
• else if location = B then return Left

10. Example of Agent
• Agent: Email Spam filter
• Environment: Inbox
• Sensor:
• Percept: Email
• Actuator:
• Action: Move email to
spam or inbox
• Agent Function:
Classification Model
• Performance Measure:
Accuracy, Precision, Recall

11. Agent: Spam filter
• Performance measure
– Minimizing false positives, false negatives
• Environment
– A user’s email account
• Actuators
– Mark as spam, delete, etc.
• Sensors
– Incoming messages, other information about
user’s account

12. Some more examples

13. Some
More
Examples

14. Properties of Environment
• Fully observable vs. Partially observable
• Deterministic vs. stochastic
• Episodic vs. Sequential
• Static vs. Dynamic
• Discrete vs. Continuous
• Single vs. Multivalent

15. Types of Envitonment
• Reading Assignment
• Section 2.3 (Stuart Russell)

16. • hardest case is partially observable, multiagent, stochastic, sequential,
dynamic, continuous, and unknown

17. Four kinds of Agents
• Simple Reflex Agent
– act only on current percept.
• Model Based Reflex Agent.
– How the world works. Percept sequence.
• Goal based Agent
– Act to fulfill some goal.
• Utility agent
– Act to maximize a utility function.
• Learning Agent
– Learn from environment and feed back on actions

18. Notes for further reading

19. Agents and Environments