Download MCS 8100/CSC 2114 : Artificial Intelligence

MCS 8100/CSC 2114 : Artificial Intelligence
Week 1 : Introduction to Artificial Intelligence
Ernest Mwebaze
[email protected]
School of Computing & IT
Makerere University
September, 2015
What is AI
Systems that think like humans
Systems that act like humans
Systems that think rationally
Systems that act rationally
What is AI
Systems that think like humans
Systems that act like humans
Systems that think rationally
Systems that act rationally
• Acting Humanly : The Turing Test
What is AI
Systems that think like humans
Systems that act like humans
Systems that think rationally
Systems that act rationally
• Acting Humanly : The Turing Test
• Thinking Humanly : Cognitive Science
What is AI
Systems that think like humans
Systems that act like humans
Systems that think rationally
Systems that act rationally
• Acting Humanly : The Turing Test
• Thinking Humanly : Cognitive Science
• Thinking Rationally : Logic/laws of thought
What is AI
Systems that think like humans
Systems that act like humans
Systems that think rationally
Systems that act rationally
• Acting Humanly : The Turing Test
• Thinking Humanly : Cognitive Science
• Thinking Rationally : Logic/laws of thought
• Acting Rationally : Acting right !
What is AI
Systems that think like humans
Systems that act like humans
Systems that think rationally
Systems that act rationally
• Acting Humanly : The Turing Test
• Thinking Humanly : Cognitive Science
• Thinking Rationally : Logic/laws of thought
• Acting Rationally : Acting right !
What is AI
• Computational models of human behavior?
- Programs that behave (externally) like humans
What is AI
• Computational models of human behavior?
- Programs that behave (externally) like humans
• Computational models of human thought processes ?
- Programs that operate (internally) the way humans do
What is AI
• Computational models of human behavior?
- Programs that behave (externally) like humans
• Computational models of human thought processes ?
- Programs that operate (internally) the way humans do
• Computational systems that behave intelligently?
- What does it mean to behave intelligently?
What is AI
• Computational models of human behavior?
- Programs that behave (externally) like humans
• Computational models of human thought processes ?
- Programs that operate (internally) the way humans do
• Computational systems that behave intelligently?
- What does it mean to behave intelligently?
• Computational systems that behave rationally
- Agents that act right.
What is AI
• Computational models of human behavior?
- Programs that behave (externally) like humans
• Computational models of human thought processes ?
- Programs that operate (internally) the way humans do
• Computational systems that behave intelligently?
- What does it mean to behave intelligently?
• Computational systems that behave rationally
- Agents that act right.
• Loosely : AI applications
- Monitor trades, detect fraud, schedule shuttle loading, etc.
Rational Agents
An agent is an entity that perceives and acts
This course is about designing rational agents
Abstractly, an agent is a function from percept histories to actions:
f : P∗ → A
For any given class of environments and tasks, we seek the
agent (or class of agents) with the best performance.
Rational Agents
More generally :
Software that gathers information about an environment and takes
actions based on that information. Eg
• a robot
• a web shopping program
• a traffic control system
The Agent and the Environment
sensors
percepts
?
environment
actions
agent
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
The Agent and the Environment
How do we begin to formalize the problem of building an agent?
−→ Make a dichotomy between the agent and its environment.
The Agent and the Environment
How do we begin to formalize the problem of building an agent?
−→ Make a dichotomy between the agent and its environment.
The World Model
• A the action space
The World Model
• A the action space
• P the percept space
The World Model
• A the action space
• P the percept space
• E the environment: A∗ → P
The World Model
• A the action space
• P the percept space
• E the environment: A∗ → P
• Alternatively, define:
• S internal state [may not be visible to agent]
The World Model
• A the action space
• P the percept space
• E the environment: A∗ → P
• Alternatively, define:
• S internal state [may not be visible to agent]
• Perception function: S → P
The World Model
• A the action space
• P the percept space
• E the environment: A∗ → P
• Alternatively, define:
• S internal state [may not be visible to agent]
• Perception function: S → P
• World dynamics:S × A → S
The World Model
• A the action space
• P the percept space
• E the environment: A∗ → P
• Alternatively, define:
• S internal state [may not be visible to agent]
• Perception function: S → P
• World dynamics:S × A → S
Agent Design
• U utility function: S → R (or S ∗ → R )
Agent Design
• U utility function: S → R (or S ∗ → R )
• The agent design problem: Find P ∗ → A
• mapping of sequences of percepts to actions
• maximizes the utility of the resulting sequence of states (each
action maps from one state to next state).
Rationality
• A rational agent takes actions it believes will achieve its goals.
• Assume I don’t like to get wet, so I bring an umbrella. Is that
rational ?
• Depends on the weather forecast and whether I’ve heard it. If
I’ve heard the forecast for rain (and I believe it) then bringing
the umbrella is rational.
Rationality
• A rational agent takes actions it believes will achieve its goals.
• Assume I don’t like to get wet, so I bring an umbrella. Is that
rational ?
• Depends on the weather forecast and whether I’ve heard it. If
I’ve heard the forecast for rain (and I believe it) then bringing
the umbrella is rational.
• Rationality 6= omniscience .
• Assume the most recent forecast is for rain but I did not listen
to it and I did not bring my umbrella. Is that rational ?
• Yes, since I did not know about the recent forecast!
Rationality
• A rational agent takes actions it believes will achieve its goals.
• Assume I don’t like to get wet, so I bring an umbrella. Is that
rational ?
• Depends on the weather forecast and whether I’ve heard it. If
I’ve heard the forecast for rain (and I believe it) then bringing
the umbrella is rational.
• Rationality 6= omniscience .
• Assume the most recent forecast is for rain but I did not listen
to it and I did not bring my umbrella. Is that rational ?
• Yes, since I did not know about the recent forecast!
• Rationality 6= success
• Suppose the forecast is for no rain but I bring my umbrella and
I use it to defend myself against an attack. Is that rational ?
• No, although successful, it was done for the wrong reason.
Limited Rationality
• There is a big problem with our definition of rationality
Limited Rationality
• There is a big problem with our definition of rationality
• The agent might not be able to compute the best action
(subject to its beliefs and goals).
Limited Rationality
• There is a big problem with our definition of rationality
• The agent might not be able to compute the best action
(subject to its beliefs and goals).
• So, we want to use limited rationality : ”‘acting in the best
way you can subject to the computational constraints that you
have”’
Limited Rationality
• There is a big problem with our definition of rationality
• The agent might not be able to compute the best action
(subject to its beliefs and goals).
• So, we want to use limited rationality : ”‘acting in the best
way you can subject to the computational constraints that you
have”’
• The (limited rational) agent design problem:
Find P → A
• mapping of sequences of percepts to actions
• maximizes the utility of the resulting sequence of states
• subject to our computational constraints !
Limited Rationality
• There is a big problem with our definition of rationality
• The agent might not be able to compute the best action
(subject to its beliefs and goals).
• So, we want to use limited rationality : ”‘acting in the best
way you can subject to the computational constraints that you
have”’
• The (limited rational) agent design problem:
Find P → A
• mapping of sequences of percepts to actions
• maximizes the utility of the resulting sequence of states
• subject to our computational constraints !
• Learning......
Environment Types
• Accessible (vs. Inaccessible)
- Can you see the state of the world directly?
Environment Types
• Accessible (vs. Inaccessible)
- Can you see the state of the world directly?
• Deterministic (vs. Non-Deterministic)
- Does an action map one state into a single other state?
Environment Types
• Accessible (vs. Inaccessible)
- Can you see the state of the world directly?
• Deterministic (vs. Non-Deterministic)
- Does an action map one state into a single other state?
• Static (vs. Dynamic)
- Can the world change while you are thinking?
Environment Types
• Accessible (vs. Inaccessible)
- Can you see the state of the world directly?
• Deterministic (vs. Non-Deterministic)
- Does an action map one state into a single other state?
• Static (vs. Dynamic)
- Can the world change while you are thinking?
• Discrete (vs. Continuous)
- Are the percepts and actions discrete (like integers) or
continuous (like reals)?
Environment Types
Solitaire
Observable
Deterministic
Static
Discrete
Single Agent
Internet Shopping
AI-Taxi
Environment Types
Observable
Deterministic
Static
Discrete
Single Agent
Solitaire
YES
Internet Shopping
NO
AI-Taxi
NO
Environment Types
Observable
Deterministic
Static
Discrete
Single Agent
Solitaire
YES
YES
Internet Shopping
NO
NO
AI-Taxi
NO
NO
Environment Types
Observable
Deterministic
Static
Discrete
Single Agent
Solitaire
YES
YES
YES
Internet Shopping
NO
NO
NO
AI-Taxi
NO
NO
NO
Environment Types
Observable
Deterministic
Static
Discrete
Single Agent
Solitaire
YES
YES
YES
YES
Internet Shopping
NO
NO
NO
YES
AI-Taxi
NO
NO
NO
NO
Environment Types
Observable
Deterministic
Static
Discrete
Single Agent
Solitaire
YES
YES
YES
YES
YES
Internet Shopping
NO
NO
NO
YES
YES
AI-Taxi
NO
NO
NO
NO
NO
Environment Types
Observable
Deterministic
Static
Discrete
Single Agent
Solitaire
YES
YES
YES
YES
YES
Internet Shopping
NO
NO
NO
YES
YES
AI-Taxi
NO
NO
NO
NO
NO
The real world is (of course) partially observable, stochastic,
sequential, dynamic, continuous, multi-agent.
Agent Types
Four basic types can be generalized :
• Simple reflex agents
• Reflex agents with state
• Goal-based agents
• Utility-based agents
A learning component can be added to any of these to form a
learning agent.
Simple Reflex Agent
Agent
Sensors
Condition−action rules
What action I
should do now
Actuators
Environment
What the world
is like now
Reflex Agent with State
Sensors
State
How the world evolves
What my actions do
Condition−action rules
Agent
What action I
should do now
Actuators
Environment
What the world
is like now
Goal-Based Agent
Sensors
State
What the world
is like now
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
Utility-Based Agent
Sensors
State
What the world
is like now
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
Learning Agent
Performance standard
Sensors
Critic
changes
Learning
element
knowledge
Performance
element
learning
goals
Problem
generator
Agent
Actuators
Environment
feedback