1. Intelligent Agents
CPS

2. Outline Agents and environments
PEAS (Performance measure, Environment, Actuators, Sensors)
Environment types
Agent types

3. Agents An AI program = An intelligent Agent
An agent is anything that can be viewed as perceiving its environment though sensors and acting upon that environment though actuators.
Perception-Action Cycle

4. Application of Intelligent Agents
AI has successfully been used in
Finance?
Robotics?
Games?
Medicine?
The Web?

5. AI in Finance Trading Agent Rates Stock Market Bonds News
Commodities Market
Trading Agent
Rates
News
Trades

6. AI in Robotics Physical actuators Cameras Microphones Robot
Environment
Robot
Cameras
Microphones
Touch
Motors: wheels, legs, arms, grippers
Voice

7. AI in Games Chess game Characters in games Game Agent Your moves You
Its own moves

8. AI in Medicine You Diagnostic Agent Vital Signals: blood pressures,
heart signals
Diagnostics
Doctor

9. Simple Diagnostic Expert System:

10. AI and the Web
World
Wide
Web
Crawler
Web Pages DB
Query
You

11. Percept: the agent’s perceptual inputs at any given instant
Percept Sequence: the complete history of everything the agent has ever perceived
The agent function maps from percept histories to actions: [f: P*  A] (abstract)
The agent program runs on the physical architecture to produce f. (implementation)

12. Vacuum-Cleaner World Percepts: location and contents, e.g., [A, Dirty]
Actions: Left, Right, Suck, NoOp

13. A Vacuum-Cleaner Agent
Percept sequence
Action
[A, Clean]
Right
[A, Dirty]
Suck
[B, Clean]
Left
[B, Dirty]
[A, Clean] [A, Clean]
[A, Clean] [A, Dirty] …
[A, Clean] [A, Clean] [A, Clean]
[A, Clean] [A, Clean] [A, Dirty]

14. A Vacuum-Cleaner Agent
function REFLEX-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

15. Task Environment
PEAS: Performance measure, Environment, Actuators, Sensors
Consider the task of designing an automated taxi:
Performance measure: safety, destination, profits, legality, comfort…
Environment: US streets/freeways, traffic, pedestrians, weather…
Actuators: steering, accelerator, brake, horn, speaker/display…
Sensors: camera, sonar, GPS, odometer, engine sensor…

16. Internet Shopping Agent
Performance measure?
Environment?
Actuators?
Sensors?

17. Internet Shopping Agent
Performance measure? price, quality, appropriateness, efficiency
Environment? WWW sites, vendors, shippers
Actuators? display to user, follow URL, fill in form
Sensors? HTML pages (text, graphics, scripts)

18. Environment Types
Fully observable (vs. partially observable): An agent’s sensors give it access to the complete state of the environment at each point in time.
Card game vs. poker (needs internal memory)
Deterministic (vs. stochastic): The next state of the environment is completely determined by the current state and the action executed by the agent.
Chess vs. game with dice (uncertainty, unpredictable)
Episodic (vs. sequential): The agent’s experience is divided into atomic “episodes” (each episode consists of the agent perceiving and then performing a single action), and the choice of action in each episode depends only on the episode itself.
Chess and taxi driving

19. Environment Types
Static (vs. dynamic): The environment is unchanged while an agent is deliberation. (The environment is semidynamic if the environment itself does not change with the passage of time but the agent’s performance score does.)
Taxi driving vs. chess (when played with a clock) vs. crossword puzzles
Discrete (vs. continuous): A limited number of distinct, clearly defined percepts and actions.
Chess vs. taxi driving (infinite)
Single agent (vs. multiagent): An agent operating by itself in an environment.
Crossword puzzle vs. chess

20. Solitaire
Chess with a clock
Internet Shopping
Taxi
Observable?
Deterministic?
Episodic?
Static?
Discrete?
Single-agent?

21. Solitaire
Chess with a clock
Internet Shopping
Taxi
Observable?
Yes No
Deterministic?
Episodic?
Static?
Semi
Discrete?
Single-agent?
Yew
The real world is (of course) partially observable, stochastic, sequential, dynamic, continuous, multi-agent.

22. Agent Types Four basic types: - simple reflex agents
- model-based reflex agents
- goal-based agents
- utility-based agents
All these can be turned into learning agents.

23. Simple Reflex Agents

24. Model-Based Reflex Agents

25. Goal-Based Agents

26. Utility-Based Agents

27. Learning Agents

28. Summary
Agents interact with environments though actuators and sensors.
The agent function describes what the agent does in all circumstances.
Agent programs implement agent functions.
PEAS descriptions define task environments.
Environment are categorized along several dimensions:
observable? deterministic? episodic? static? discrete? single-agent?
Several basic agent architectures exist:
reflex, reflex with state, goal-based, utility-based

29. Try out some intelligent agents!
A Chatbot is a computer program designed to simulate an intelligent conversation with one or more human users via auditory or textual methods, primarily for engaging in small talk.
ALICE:
Won the Loebner Prize three times (in 2000, 2001 and 2004)
ELIZA:
One of the classic chat bots, written at MIT by Joseph Weizenbaum between 1964 and 1966
Automated online assistants