Intelligent Agents
CPS 4801-01
Outline
• Agents and environments
• PEAS (Performance measure, Environment,
Actuators, Sensors)
• Environment types
• Agent types
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
Application of Intelligent Agents
• AI has successfully been used in
o Finance?
o Robotics?
o Games?
o Medicine?
o The Web?
AI in Finance
Trading Agent
Rates
News
Stock Market
Bonds
Commodities
Market
Trades
AI in Robotics
Robot
Cameras
Microphones
Touch
Environment
Motors: wheels,
legs, arms, grippers
Voice
• Physical actuators
AI in Games
• Chess game
Game Agent
Your
moves
You
Its own
moves
• Characters in games
AI in Medicine
Diagnostic Agent Vital Signals:
blood pressures,
heart signals
Diagnostics
You
Doctor
• Simple Diagnostic Expert System:
• http://familydoctor.org/familydoctor/en/healthtools/search-by-symptom.html
AI and the Web
Crawler
Web Pages
World
Wide
Web
DB
Query
You
• 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)
Vacuum-Cleaner World
• Percepts: location and contents, e.g., [A, Dirty]
• Actions: Left, Right, Suck, NoOp
A Vacuum-Cleaner Agent
Percept sequence
Action
[A, Clean]
Right
[A, Dirty]
Suck
[B, Clean]
Left
[B, Dirty]
Suck
[A, Clean] [A, Clean]
Right
[A, Clean] [A, Dirty]
Suck
…
…
[A, Clean] [A, Clean] [A, Clean]
Right
[A, Clean] [A, Clean] [A, Dirty]
Suck
…
…
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
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…
Internet Shopping Agent
•
•
•
•
Performance measure?
Environment?
Actuators?
Sensors?
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)
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.
o 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.
o 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.
o Chess and taxi driving
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.)
o 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.
o Chess vs. taxi driving (infinite)
• Single agent (vs. multiagent): An agent operating by
itself in an environment.
o Crossword puzzle vs. chess
Solitaire
Observable?
Deterministic?
Episodic?
Static?
Discrete?
Single-agent?
Chess with a
clock
Internet
Shopping
Taxi
Solitaire
Chess with a
clock
Internet
Shopping
Taxi
Observable?
Yes
Yes
Yes
No
Deterministic?
Yes
Yes
No
No
Episodic?
No
No
No
No
Static?
Yes
Semi
Semi
No
Discrete?
Yes
Yes
Yes
No
Single-agent?
Yes
No
Yew
No
The real world is (of course) partially observable,
stochastic, sequential, dynamic, continuous, multi-agent.
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.
Simple Reflex Agents
Model-Based Reflex Agents
Goal-Based Agents
Utility-Based Agents
Learning Agents
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:
o observable? deterministic? episodic? static? discrete?
single-agent?
• Several basic agent architectures exist:
o reflex, reflex with state, goal-based, utility-based
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: http://alice.pandorabots.com/
o Won the Loebner Prize three times (in 2000, 2001 and 2004)
• ELIZA: http://nlp-addiction.com/eliza/
o One of the classic chat bots, written at MIT by Joseph
Weizenbaum between 1964 and 1966
• Automated online assistants