cs 380: artificial intelligence rational agentssanti/teaching/2013/cs380/...solitaire chess...
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CS 380: ARTIFICIAL INTELLIGENCE RATIONAL AGENTS
9/25/2013 Santiago Ontañón [email protected] https://www.cs.drexel.edu/~santi/teaching/2013/CS380/intro.html
• Do you think a machine can be made that replicates human intelligence?
Outline • What is an Agent? • Rationality • Agents and Environments • Agent Types
(these slides are adapted from Russel & Norvig’s official slides)
Outline • What is an Agent? • Rationality • Agents and Environments • Agent Types
(these slides are adapted from Russel & Norvig’s official slides)
What is an Agent? • Agent: autonomous entity which observes and acts upon
an environment and directs its activity towards achieving goals.
• Example agents: • Humans • Robots • Software agents • ATMs • etc.
What is an Agent?
• Theoretically:
• In practice: the agent runs on a physical system (e.g. a computer) to produce
Agents and environments
?
agent
percepts
sensors
actionsenvironment
actuators
Agents include humans, robots, softbots, thermostats, etc.
The agent function maps from percept histories to actions:
f : P∗ → A
The agent program runs on the physical architecture to produce f
Chapter 2 4
f : P ⇤ ! A
f
What is an Agent?
• Theoretically:
• In practice: the agent runs on a physical system (e.g. a computer) to produce
Agents and environments
?
agent
percepts
sensors
actionsenvironment
actuators
Agents include humans, robots, softbots, thermostats, etc.
The agent function maps from percept histories to actions:
f : P∗ → A
The agent program runs on the physical architecture to produce f
Chapter 2 4
f : P ⇤ ! A
f
Maps sequences of percepts to actions
Example: Vacuum Cleaner
• Percepts: • Location sensor and Dirt sensor
• Actions: • Left, Right, Suck, Wait
Room A Room B
Vacuum-cleaner world
A B
Percepts: location and contents, e.g., [A, Dirty]
Actions: Left, Right, Suck, NoOp
Chapter 2 5
Example: Vacuum Cleaner
• Percepts: • Location sensor and Dirt sensor
• Actions: • Left, Right, Suck, Wait
Room A Room B
Vacuum-cleaner world
A B
Percepts: location and contents, e.g., [A, Dirty]
Actions: Left, Right, Suck, NoOp
Chapter 2 5
[A, Clean]
Example: Vacuum Cleaner 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... ...
function Reflex-Vacuum-Agent( [location,status]) returns an action
if status = Dirty then return Suckelse if location = A then return Rightelse if location = B then return Left
What is the right function?Can it be implemented in a small agent program?
Chapter 2 6
Example: Vacuum Cleaner 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... ...
function Reflex-Vacuum-Agent( [location,status]) returns an action
if status = Dirty then return Suckelse if location = A then return Rightelse if location = B then return Left
What is the right function?Can it be implemented in a small agent program?
Chapter 2 6
Two questions:
• What is the function?
• Can it be implemented as a computer program? (How?)
Outline • What is an Agent? • Rationality • Agents and Environments • Agent Types
(these slides are adapted from Russel & Norvig’s official slides)
Rationality • As we discussed last class, “intelligence” is an ill-defined
concept.
• Let us assume a performance measure, for example: • $1 per room cleaned • $10 per room cleaned minus $1 per each movement • etc.
• Rational agent: • Chooses actions that maximize the expected value of the
performance measure given the current percept sequence.
Rationality • As we discussed last class, “intelligence” is an ill-defined
concept.
• Let us assume a performance measure, for example: • $1 per room cleaned • $10 per room cleaned minus $1 per each movement • etc.
• Rational agent: • Chooses actions that maximize the expected value of the
performance measure given the current percept sequence.
Performance measure is given (external) to the agent.
Rationality • Imagine two agents A and B whose performance measure
is defined by how many $ they make at the casino. • A and B play one game of Black Jack • A plays the move that theoretically has higher chances of winning • B plays a different, more risky move • B ends up winning
• Which agent is rational?
Rationality • Imagine two agents A and B whose performance measure
is defined by how many $ they make at the casino. • A and B play one game of Black Jack • A plays the move that theoretically has higher chances of winning • B plays a different, more risky move • B ends up winning
• Which agent is rational? A is the rational agent • Rationality does not imply clairvoyance • Rationality does not imply omniscient • Thus, rational does not mean successful
Outline • What is an Agent? • Rationality • Agents and Environments • Agent Types
(these slides are adapted from Russel & Norvig’s official slides)
How do we Design a Rational Agent? • To design a rational agent, we first need to define its
environment, performance measure, etc.
• PEAS: • Performance • Environment • Actions • Sensors (percepts)
Example (informal): Automated Taxi • Performance:
• Environment:
• Actions:
• Sensors:
Example (informal): Automated Taxi • Performance: profit, safety, legality, …
• Environment: streets, pedestrians, cars, weather, traffic lights/signs, …
• Actions: steering, throttle, break, horn, display, …
• Sensors: GPS, video, sonar, keyboard, accelerometers, microphones, …
Example: Chess Agent • Performance:
• Environment: • Actions:
• Sensors:
Example: Chess Agent • Performance:
• Environment: Chess board, and opponent.
• Actions:
• Sensors:
Example: Chess Agent • Performance: Once reached a terminal state (draw, win):
-1 for losing, 0 for draw, 1 for winning. • Environment: Chess board, and opponent.
• Actions:
• Sensors:
Example: Chess Agent • Performance: Once reached a terminal state (draw, win):
-1 for losing, 0 for draw, 1 for winning. • Environment: Chess board, and opponent.
• Actions: Pairs of coordinates: (x1,y1) à (x2, y2). Specify the source piece and the target position (for castling, the king’s move is specified).
• Sensors:
Example: Chess Agent • Performance: Once reached a terminal state (draw, win):
-1 for losing, 0 for draw, 1 for winning. • Environment: Chess board, and opponent.
• Actions: Pairs of coordinates: (x1,y1) à (x2, y2). Specify the source piece and the target position (for castling, the king’s move is specified).
• Sensors: Board perceived as a 8x8 matrix. Each element in the matrix can take one of the following values: E, BP, WP, BB, WB, BK, WN, BR, WN, BQ, WK, BK, WK
Example: Chess Agent • Performance: Once reached a terminal state (draw, win):
-1 for losing, 0 for draw, 1 for winning. • Environment: Chess board, and opponent.
• Actions: Pairs of coordinates: (x1,y1) à (x2, y2). Specify the source piece and the target position (for castling, the king’s move is specified).
• Sensors: Board perceived as a 8x8 matrix. Each element in the matrix can take one of the following values: E, BP, WP, BB, WB, BK, WN, BR, WN, BQ, WK, BK, WK
The environment might have lots of features (piece size and material,
opponent’s facial expression, etc.). When designing a rational agent, we only need to provide sensors for the
information that is necessary to achieve the agent’s goals (maximize
performance measure)
Example: Chess Agent
Environment
WR WN WB WK WQ WB WN WR
WP WP WP WP WP WP WP WP
BP BP BP BP BP BP BP BP
BR BN BB BK BQ BB BN BR
Percept
Environment Types Observable Deterministic Sequential Static Discrete Single-Agent
Solitaire
Chess
Backgammon
Taxi
StarCraft
Real Life
Environment Types Observable Deterministic Sequential Static Discrete Single-Agent
Solitaire No Yes Yes Yes Yes Yes
Chess
Backgammon
Taxi
StarCraft
Real Life
Environment Types Observable Deterministic Sequential Static Discrete Single-Agent
Solitaire No Yes Yes Yes Yes Yes
Chess Yes Yes Yes Yes Yes No
Backgammon
Taxi
StarCraft
Real Life
Environment Types Observable Deterministic Sequential Static Discrete Single-Agent
Solitaire No Yes Yes Yes Yes Yes
Chess Yes Yes Yes Yes Yes No
Backgammon Yes No Yes Yes Yes No
Taxi
StarCraft
Real Life
Environment Types Observable Deterministic Sequential Static Discrete Single-Agent
Solitaire No Yes Yes Yes Yes Yes
Chess Yes Yes Yes Yes Yes No
Backgammon Yes No Yes Yes Yes No
Taxi No No Yes No No No
StarCraft
Real Life
Environment Types Observable Deterministic Sequential Static Discrete Single-Agent
Solitaire No Yes Yes Yes Yes Yes
Chess Yes Yes Yes Yes Yes No
Backgammon Yes No Yes Yes Yes No
Taxi No No Yes No No No
StarCraft No Almost Yes No Yes No
Real Life No No (?) Yes No No (?) No
Outline • What is an Agent? • Rationality • Agents and Environments • Agent Types
(these slides are adapted from Russel & Norvig’s official slides)
Agent Types • We will cover 4 basic types:
• Reactive agents (reflex agents) • State-based agents • Goal-based agents • Utility-based agents
• They can all incorporate learning
Reactive Agents Simple reflex agents
AgentEnvironm
entSensors
What the worldis like now
What action Ishould do nowCondition−action rules
Actuators
Chapter 2 20
State-based Agents Reflex agents with state
Agent
Environment
Sensors
What action Ishould do now
State
How the world evolves
What my actions do
Condition−action rules
Actuators
What the worldis like now
Chapter 2 22
Goal-based Agents Goal-based agents
Agent
Environment
Sensors
What it will be like if I do action A
What action Ishould do now
State
How the world evolves
What my actions do
Goals
Actuators
What the worldis like now
Chapter 2 24
Utility-based Agents Utility-based agents
Agent
Environment
Sensors
What it will be like if I do action A
How happy I will be in such a state
What action Ishould do now
State
How the world evolves
What my actions do
Utility
Actuators
What the worldis like now
Chapter 2 25
Learning Agents Learning agentsPerformance standard
Agent
Environment
Sensors
Performance element
changes
knowledgelearning goals
Problem generator
feedback
Learning element
Critic
Actuators
Chapter 2 26
Summary • Agents:
• Interact with environments via actuators (actions) and sensors (percepts)
• Rational agent: maximizes expected performance
• PEAS
• Environment types
• Basic agent architectures