Intelligent Agents
Chapter 2
Modified by Vali Derhami
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Outline
• Agents and environments
• Rationality
• PEAS (Performance measure,
Environment, Actuators, Sensors)
• Environment types
• Agent types
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Agents
• An agent is anything that can be viewed as perceiving its
environment through sensors and acting upon that
environment through actuators
•
• Human agent: eyes, ears, and other organs for sensors;
hands,legs, mouth, and other body parts for actuators
• Robotic agent: cameras and infrared range finders for
sensors;various motors for actuators
•
• General assumption: agent can perceive its own action
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Agents (cont.)
• Percept sequence is the complete history of
everything the agent has ever perceived.
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Agents and environments
• Agent's behavior is described by the agent function that maps any
given percept sequence to an action.
•
‫ در حالیکه برنامه عامل پیاده‬. ‫تابع عامل یک شرح ریاضی انتزاع گونه است‬
.‫سازی کامل آن است که در داخل سیستم فیزیکی اجرا می شود‬
• The agent function maps from percept histories to
actions:
• [f: P*  A]
•
 Tabular representation: a large table for most agents
• The agent program runs on the physical architecture to
produce f
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• agent = architecture + program
Vacuum-cleaner world
• Percepts: location and contents, e.g.,
[A,Dirty]
• Actions: Left, Right, Suck, NoOp
•
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A vacuum-cleaner agent
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Rational agents
• An agent should "do the right thing", based on what it can perceive
and the actions it can perform. The right action is the one that will
cause the agent to be most successful
•
• Performance measure: An objective criterion for success of an
agent's behavior
• E.g., performance measure of a vacuum-cleaner agent could be
amount of dirt cleaned up, amount of time taken, amount of
electricity consumed, amount of noise generated, etc.
• Note: Design performance measures according to what
one actually wants in the environment, rather than
according to how one thinks the agent should behave.
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What is rational
• The performance measure that defines the
criterion of success.
• The agent's prior knowledge of the
environment.
• The actions that the agent can perform.
• The agent's percept sequence to date.
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Rational agents
• Rational Agent: For each possible percept
sequence, a rational agent should select
an action that is expected to maximize its
performance measure, given the evidence
provided by the percept sequence and
whatever built-in knowledge the agent has.
•
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Omniscience, learning, and
autonomy
Rationality is distinct from omniscience •
(all-knowing with infinite knowledge)
‫• عقالنیت بیشینه میکند راندمانی که امید می رود در حالیکه کامل‬
.‫( راندمان واقعی را بیشینه میکند‬perfection) ‫بودن‬
• Agents can perform actions in order to
modify future percepts so as to obtain
useful information (information gathering,
exploration)
• An agent is autonomous if its behavior is
determined by its own experience (with
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ability to learn and adapt)
Successful agents split the task of
computing the agent function into three
different periods:
 During designing: some of the computation is
done by its designers;
 During deliberating (thinking) on its next
action, the agent does more computation;
 During learning from experience: it does even
more computation to decide how to modify its
behavior.
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Task Environment (PEAS)
• PEAS: Performance measure,
Environment, Actuators, Sensors
• Consider, e.g., the task of designing an
automated taxi driver:
•
– Performance measure
– Environment
– Actuators
– Sensors
–
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PEAS
• Consider, e.g., the task of designing an
automated taxi driver:
•
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PEAS
• Agent: Medical diagnosis system
 Performance measure: Healthy patient,
minimize costs, lawsuits
 Environment: Patient, hospital, staff
 Actuators: Screen display (questions, tests,
diagnoses, treatments, referrals)
 Sensors: Keyboard (entry of symptoms,
findings, patient's answers)
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PEAS
• Agent: Part-picking robot
 Performance measure: Percentage of parts in
correct bins
 Environment: Conveyor belt with parts, bins
 Actuators: Jointed arm and hand
 Sensors: Camera, joint angle sensors
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PEAS
• Agent: Interactive English tutor
 Performance measure: Maximize student's
score on test
 Environment: Set of students, testing agency
 Actuators: Screen display (exercises,
suggestions, corrections)
 Sensors: Keyboard entry
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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.
• Deterministic (vs. stochastic): The next state of the
environment is completely determined by the current
state and the action executed by the agent. (If the
environment is deterministic except for the actions of
other agents, then the environment is strategic)
•
‫(محیطی که کامال مشاهده پذیر نباشد یا قطعی نباشد نایقین‬uncertaion)‫گفته می شود‬
• 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.
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•
Environment types
• Static (vs. dynamic): The environment is
unchanged while an agent is deliberating. (The
environment is semidynamic if the environment
itself does not change with the passage of time
but the agent's performance score does)
• Discrete (vs. continuous): A limited number of
distinct, clearly defined percepts and actions.
• Single agent (vs. multiagent): An agent
operating by itself in an environment.
•
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Environment types
Fully observable
Deterministic
Episodic
Static
Discrete
Single agent
Chess with
a clock
Yes
Strategic
No
Semi
Yes
No
Chess without
a clock
Yes
Strategic
No
Yes
Yes
No
Taxi driving Image
Analysis
No
Yes
No
Yes
No
Yes
No
Semi
No
No
No
Yes
• The environment type largely determines the agent design
•
• The real world is (of course) partially observable, stochastic, sequential,
dynamic, continuous, multi-agent
•
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Structure of Agents
• The job of AI: Design the agent program
that implements the agent function
mapping percepts to actions.
• Agent= Architecture +Program
• Program has to be appropriate for the
architecture
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Agent functions and programs
• Agent programs: they take the current percept
as input from the sensors and return an action to
the actuators.
 Difference between agent program and agent
function:
o Agent program: takes the current percept as input
o Agent function: takes the entire percept history.
• Aim: find a way to implement the rational agent
function concisely
•
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Table-lookup agent
Drawbacks:
Huge table
Take a long time to build the table
No autonomy
Even with learning, need a long time to learn the table entries
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Agent types
Four basic types in order of increasing
generality:
• Simple reflex agents
• Model-based reflex agents
• Goal-based agents
• Utility-based agents
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Simple reflex agents
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Vacuum agent
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Simple reflex agents
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Model-based reflex agents
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Model-based reflex agents
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Goal-based agents
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Utility-based agents
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Learning agents
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Learning agents
Four conceptual components
• Learning element: making improvements.
• Performance element: selecting external actions.
• Critic: tells the learning element how well the
agent is doing with respect to a fixed performance
standard
• Problem generator: suggesting actions that will
lead to new and informative experiences.
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‫تمرین دوم از وب سایت برداشته شود‪.‬‬
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