Download Unit1_1 - คณะเทคโนโลยีสารสนเทศและการสื่อสาร มหาวิทยาลัยพะเยา

Intelligent Agents
อาจารย์อุทยั เซี่ยงเจ็น
สานักเทคโนโลยีสารสนเทศและการสื่ อสาร
มหาวิทยาลัยนเรศวร วิทยาเขตสารสนเทศพะเยา
Notion of an Agent
sensors
?
environment
agent
actuators
laser range
finder
sonars
touch sensors
Notion of an Agent
sensors
?
environment
agent
actuators
•Locality of sensors/actuators
•Imperfect modeling
•Time/resource constraints
•Sequential interaction
•Multi-agent worlds
Example: Tracking a Target
• The robot must keep
the target in view
• The target’s trajectory
is not known in advance
• The robot may not know
all the obstacles in
advance
• Fast decision is required
robot
target
What is Artificial Intelligence?
(revised)
Study of design of rational agents
agent = thing that acts in environment
Rational agent = agent that acts
rationally:
– actions are appropriate for goals and
circumstances to changing environments
and goals
– learns from experience
Goals of Artificial Intelligence
Scientific goal:
– understand principles that make rational
(intelligent) behavior possible, in natural or
artificial systems.
Engineering goal:
– specify methods for design of useful,
intelligent artifacts.
Psychological goal:
– understanding/modeling people
– cognitive science (not this course)
Goals of This Course
Introduce key methods & techniques
from AI
– searching,
– reasoning and decision making (logical and
probabilistic)
– learning language understanding,
–...
Understand applicability and limitations
of these methods
Goals of This Course
Our approach:
– Characterize Environments
– Identify agent that is most effective for
each environment
Study increasingly complicated agent
architectures requiring
– increasingly sophisticated representations,
– increasingly powerful reasoning strategies
Intelligent Agents
Definition: An Intelligent Agent perceives its
environment via sensors and acts rationally
upon that environment with its actuators.
Hence, an agent gets percepts one at a time,
and maps this percept sequence to actions.
Properties
– Autonomous
– Interacts with other agents
plus the environment
– Adaptive to the environment
– Pro-active (goal-directed)
Applications of Agents
Autonomous delivery/cleaning robot
– roams around home/office environment, delivering
coffee, parcels,. . . vacuuming, dusting,. . .
Diagnostic assistant helps a human
troubleshoot problems and suggest repairs or
treatments.
– E.g., electrical problems, medical diagnosis.
Infobot searches for information on computer
system or network.
Autonomous Space Probes
...
Task Environments: PEAS
Performance Measure
– Criterion of success
Environment
Actuators(เคลื่อนไหว)
– Mechanisms for the agent to affect the
environment
Sensors
– Channels for the agent to perceive the
environment
Example: Taxi Driving
Performance Measure
– Safe, fast, legal, comfortable trip, maximize profit
Environment
– Roads, other traffic, pedestrians, customers
Actuators
– Steering, accelerator, break, signal, horn, …
Sensors
– Cameras, sonar, speedometer, GPS, …
Types of Environments
Fully observable (accessible) or not
Episodic(ตอน) vs. sequential(ลาดับ)
Static vs. dynamic
Discrete vs. continuous
Single agent vs. multi-agent
– competitive vs. cooperative
Agent Function and Program
Agent specified by agent function
– mapping percept sequences to actions
– Aim: Concisely implement “rational agent
function”
Agent program
– Input: a single percept-vector
– Process: (keeps/updates internal state)
– Output: returns action
Skeleton Agent Program
function SkeletonAgent(percept) returns action
static: memory, [agent's memory of the world]
memory  UpdateMemory(memory,percept)
action  ChooseBestAction(memory)
memory  UpdateMemory(memory, action)
return action
Types of Agents
Simple reflex agents
– Actions are determined by sensory input only
Model-based reflex agents
– Has internal states
Goal-based agents
– Action may be driven by a goal
Utility-based agents
– Maximizes a utility function
Simple Reflex Agent
Example
A LEGO MindStormTM program:
if (isDark(leftLightSensor))
turnLeft()
else if (isDark(rightLightSensor))
turnRight()
else goStraight()
What’s the agent function?
Model-Based Agent
Goal-based Agent
Utility-based Agent
Summary
Intelligent Agent
PEAS
Types of Agents