Intelligent Agents
Artificial Intelligence
Santa Clara University 2016
Agents and Environments
•
Agents interact with environments through sensors and
actuators
Agent
Sensors
Percepts
Environment
?
Actuators
Actions
Vacuum Cleaner World
A
B
Percept sequence
Action
{A, Clean}
Right
{A, Dirty}
Suck
{B, Clean}
Left
{A, Clean},{A, Clean}
Right
{A, Clean},{A Dirty}
Suck
…
…
Rationality
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What is rational depends on
• 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
Rationality
•
Definition of a rational agent
• For each possible percept sequence, a rational
agent should select inaction that is expected to
maximize its performance measure, given the
evidence provided by the percept sequence and
whatever built-in knowledge the agent has
Vacuum Cleaner World
•
•
•
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Performance measure:
• One point for each clean room over a life time of
10000 time steps
Geography is knows a priori, but not the dirt
distribution and location
Only actions are left, right, and suck
The agent correctly perceives its location and
whether the location is dirty
Rationality
•
•
•
•
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Rational =/= Omniscient
Rational =/= Perfect
Information gathering is part of being rational
Agents need to learn from its perceptions
Autonomy is limited by previous built-in knowledge
Task environment
•
Specify
• Performance
• Environment
• Actuators
• Sensors
Task environment
•
•
Group quiz:
Specify PEAS for
1. an automated taxi
2. interactive English tutor
3. medical diagnostic system
Task environment
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•
•
•
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Fully observable vs. partially observable
Single agent vs. multi agent
Deterministic vs. stochastic
Episodic vs. sequential
Static vs. dynamic
Discrete vs. continuous
Known vs. unknown
• Agent’s knowledge about the laws of the
environment
Structure of Agents
•
Agent = Architecture + Program
Table Driven Agent
class Agent:
bigTable = {}
def init():
percepts = []
def action(self, percept):
percepts.append(self.percept)
action = Agent.bigTable[self.percept]
Cannot be an efficient implementation because
the table size is to big
Agent Architecture
•
Alternatives
• Single reflex agent
• Agent reacts to current precept
• Model based agent
• Agent maintains a model of its world
• Goal based agent
• Agent uses goals in order to plan actions
• Utility based agent
• Agent evaluates goals using a utility function
• Learning agents
• Agents change program through learning
Reflex agent
State
How the world evolves
Sensors
What my actions do
Condition-action rules
Agent
What action I
should do now
Actuators
Environment
What the world
is like now
Reflex Agents
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Reflex agents do not maintain history
•
Action is only based on current percept
•
•
Autonomic taxi cannot distinguish for sure between
a blinking light and a constant light
Can enter into an infinite loop
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Vacuum in the vacuum world that only knows
whether the room is dirty
•
When the room is clean, should it go left or right?
Can use randomization to escape loops, but then
the agent cannot be perfectly rational
Reflex Agent
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•
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Maintains a state
• Uses percepts and own actions to update state
• State reflects the agent’s guess of what the world is like
• State also reflects the current goal of the agent
• E.g.: autonomic taxi behaves differently at the
same location depending on whether it wants to
go to the garage for maintenance or whether it
wants to pick up clients.
• States can be atomic, factored or structured
Matches a set of rules against the state
Action taken based on the selected rule
Goal based agents
•
•
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Instead of trying to build goals into states (and explode the
state space)
• Maintain goals explicitly
In a given state, evaluate the actions that can be done and
how they lead towards goals
• AI topics: Searching & Planning
Less efficient than reflex agents but more flexible
• New knowledge can be used more easily
• E.g. Autonomic taxi in rain: Braking now works differently
• Reflex agent would need to rewrite many conditionaction rules
Goal based agent
Sensors
State
What my actions do
What it will be like
if I do action A
Goals
What action I
should do now
Agent
Actuators
Environment
How the world evolves
What the world
is like now
Utility based agent
•
Goals are binary:
• You are in a good state or in a bad state
Reality is more complex:
•
Autonomic taxi might deliver your to where you want to go,
but you also want to minimize costs
Utility measures how good a goal state is
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•
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Utility based agents try to maximize the expected utility of the
action outcomes
One can show that a racial agent must behave as if it
possesses a utility function whose expected value it tries to
maximize
Utility based agent
Sensors
State
What my actions do
What it will be like
if I do action A
Utility
How happy I will be
in such a state
What action I
should do now
Agent
Actuators
Environment
How the world evolves
What the world
is like now
Learning Agents
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Agents can be programmed by hand
Turing (1950): ""Some more expeditious method
seems desirable”.
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Implemented as learning agents
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Learning
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Components
• Learning Element
• makes improvements
• Performance Element = the whole agent we considered before
• selects actions
• Critic
• provides feedback to the learning element
• tells agent how well agent is doing according to a performance standard
• Problem generator:
• In order for learning to take place
• agent must try out new things
• 4 years old: “Did you know that peanut butter on salt crackers
tastes horrible?"
Learning agent
Performance standard
Sensors
Critic
Learning
element
learning
goals
changes
knowledge
Performance
element
Problem
generator
Agent
Actuators
Environment
feedback
States
B
C
B
(a) Atomic
C
(b) Factored
(b) Structured