Transcript Belief-optimal Reasoning for Cyber

CS B551: Elements of Artificial
Intelligence
Instructor: Kris Hauser
http://cs.indiana.edu/~hauserk
1
Recap
http://cs.indiana.edu/classes/b551
 Brief history and philosophy of AI
 What is intelligence? Can a machine
act/think intelligently?

• Turing machine, Chinese room
2
Agenda
Agent Frameworks
 Problem Solving and the Heuristic
Search Hypothesis

3
Agent Frameworks
4
Definition of Agent

Anything that:
• Perceives its environment
• Acts upon its environment

A.k.a. controller,
robot
5
Definition of “Environment”
The real world, or a virtual world
 Rules of math/formal logic
 Rules of a game
 …
 Specific to the problem domain

6
Agent
Percepts
Sensors
Actions
Actuators
Environment
?
7
Agent
Percepts
Sensors
Actions
Actuators
Environment
?
Sense – Plan – Act
8
“Good” Behavior
Performance measure (aka reward,
merit, cost, loss, error)
 Part of the problem domain

9
Exercise

Formulate the problem domains for:
• Tic-tac-toe
• A web server
• An insect
• A student in B551
• A doctor diagnosing
a patient
• IU’s basketball team
• The U.S.A.
What is/are the:
• Environment
• Percepts
• Actions
• Performance measure
How might a “goodbehaving” agent process
information?
10
Types of agents
Simple reflex (aka reactive, rulebased)
 Model-based
 Goal-based
 Utility-based (aka decision-theoretic,
game-theoretic)
 Learning (aka adaptive)

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Simple Reflex
Percept
Interpreter
State
Rules
Action
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Simpl(est) Reflex
State
Observable
Environment
Interpreter
State
Rules
Action
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Simpl(est) Reflex
State
Observable
Environment
Rules
Action
14
Rule-based Reflex Agent
A
B
if DIRTY = TRUE then SUCK
else if LOCATION = A then RIGHT
else if LOCATION = B then LEFT
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Model-Based Reflex
Percept
Interpreter
State
Rules
Action
Action
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Model-Based Reflex
Percept
Model
State
Rules
Action
Action
17
Model-Based Reflex
Percept
Model
State
State
estimation
Rules
Action
Action
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Model-Based Agent
State:
LOCATION
HOW-DIRTY(A)
Rules:
if LOCATION = A then
if HAS-SEEN(B) = FALSE then RIGHT
HOW-DIRTY(B)
else if HOW-DIRTY(A) > HOW-DIRTY(B) then SUCK
HAS-SEEN(A)
HAS-SEEN(B)
A
else RIGHT
…
B
Model:
HOW-DIRTY(LOCATION) = X
HAS-SEEN(LOCATION) = TRUE
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Model-Based Reflex Agents
Controllers in cars, airplanes,
factories
 Robot obstacle avoidance, visual
servoing

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Goal-Based, Utility-Based
Percept
Model
State
Rules
Action
Action
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Goal- or Utility-Based
Percept
Model
State
Decision Mechanism
Action
Action
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Goal- or Utility-Based
State
Decision Mechanism
Action Generator
Percept Model
Model
Simulated State
Performance tester
Best Action
Action
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Goal-Based Agent
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Big Open Questions:
Goal-Based Agent = Reflex Agent?
Physical Environment
“Mental Environment”
Percept
Model
Mental Model
State
Mental State
DM Rules
Action
Action
Mental Action
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Big Open Questions:
Goal-Based Agent = Reflex Agent?
Physical Environment
“Mental Environment”
Percept
Model
Mental Model
State
Mental State
DM Rules
Action
Action
Mental Action
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With Learning
Percept
Model/Learning
State/Model/DM specs
Decision Mechanism
Action
Action
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Big Open Questions: Learning Agents
The modeling, learning, and decision
mechanisms of artificial agents are
tailored for specific tasks
 Are there general mechanisms for
learning?
 If not, what are the limitations of the
human brain?

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Types of Environments
Observable / non-observable
 Deterministic / nondeterministic
 Episodic / non-episodic
 Single-agent / Multi-agent

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Observable Environments
Percept
Model
State
Decision Mechanism
Action
Action
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Observable Environments
State
Model
State
Decision Mechanism
Action
Action
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Observable Environments
State
Decision Mechanism
Action
Action
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Nondeterministic Environments
Percept
Model
State
Decision Mechanism
Action
Action
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Nondeterministic Environments
Percept
Model
Set of States
Decision Mechanism
Action
Action
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Agents in the bigger picture


Binds disparate fields
(Econ, Cog Sci, OR,
Control theory)
Framework for technical
components of AI
• Components are useful and
rich topics themselves
• Rest of class primarily
studies components

Casting problems in the
framework sometimes
brings insights
Agent
Robotics
Reasoning
Search
Perception
Learning
Knowledge Constraint
rep.
satisfaction
Planning
Natural
language
...
Expert
Systems
35
Problem Solving and the
Heuristic Search Hypothesis
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Example: 8-Puzzle
8
2
3
4
5
1
1
2
3
7
4
5
6
6
7
8
Initial state
Goal state
State: Any arrangement of 8 numbered tiles and an empty tile on a 3x3 board
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Successor Function: 8-Puzzle
2
3
4
5
1
6
8
2
7
8
6
3
8
2
3
4
7
3
4
5
1
6
5
1
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SUCC(state)  subset of states
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The successor function is knowledge
about the 8-puzzle game, but it does
not tell us which outcome to use, nor to
which state of the board to apply it.
5
2
7
4
1
6
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Across history, puzzles and games
requiring the exploration of alternatives
have been considered a challenge for
human intelligence:
 Chess originated in Persia and India
about 4000 years ago
 Checkers appear in 3600-year-old
Egyptian paintings
 Go originated in China over 3000 years
ago
So, it’s not surprising that AI uses games
to design and test algorithms
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Exploring Alternatives
Problems that seem to require
intelligence require exploring
multiple alternatives
 Search: a systematic way of
exploring alternatives

40
Defining a Search Problem
S
 State space S
 Successor function:
x  S  SUCC(x)  2S
 Initial state s0
 Goal test:
xS  GOAL?(x) =T or F
 Arc cost
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Problem Solving Agent
Algorithm
1. I  sense/read initial state
2. GOAL?  select/read goal test
3. SUCC  select/read successor
function
4. solution  search(I, GOAL?, SUCC)
5. perform(solution)
42
State Graph



Each state is
represented by a
distinct node
An arc (or edge)
connects a node s
to a node s’ if
s’  SUCC(s)
The state graph may
contain more than one
connected component
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Solution to the Search Problem




A solution is a path
connecting the initial
node to a goal node
(any one)
The cost of a path is
the sum of the arc
costs along this path
An optimal solution is
a solution path of
minimum cost
There might be
no solution !
G
I
44
Pathless Problems




Sometimes the path
doesn’t matter
A solution is any goal
node
Arcs represent
potential state
transformations
E.g. 8-queens,
Simplex for LPs, Map
coloring
G
I
45
8-Queens Problem

State repr. 1
• Any nonconflicting
placement of
0-8 queens

State repr. 2
• Any placement
of 8 queens
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Intractability



It may not be
feasible to
construct the
state graph
completely
n-puzzle:
(n+1)! states
k-queens:
kk states
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Heuristic Search Hypothesis
(Newell and Simon, 1976)
“The solutions to problems are represented as symbol
structures. A physical symbol system exercises its intelligence
in problem solving by search - that is, by generating and
progressively modifying symbol structures until it produces a
solution structure.”


Intelligent systems must use heuristic
search to find solutions efficiently
Heuristic: knowledge that is not presented
immediately by the problem specification
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Example

I’ve thought of a number between 1
and 100. Guess it.
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Example

I’ve picked a password between 3
and 8 alphanumeric characters that
I’ll never forget. Guess it.
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Discussion

Debated whether all intelligence is
modifying symbol structures…
• e.g., Elephants don’t play chess, Brooks ’91

But for those tasks that do require
modifying symbol structures,
hypothesis seems true
• Perhaps circular logic?
51
Topics of Next 3-4 Classes

Blind Search
• Little or no knowledge about how to
search

Heuristic Search
• How to use heuristic knowledge

Local Search
• With knowledge about goal distribution
52
Recap
Agent: a sense-plan-act framework
for studying intelligent behavior
 “Intelligence” lies in sophisticated
components

53
Recap

General problem solving framework
• State space
• Successor function
• Goal test
• => State graph

Search is a methodical way of
exploring alternatives
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Homework
Register!
 Readings: R&N Ch. 3.1-3.3

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What is a State?

A compact representation of
elements of the world relevant to
the problem at hand
• Sometimes very clear (logic, games)
• Sometimes not (brains, robotics, econ)
History is a general-purpose state
representation: [p1,a1,p2,a2,…]
 State should capture how history
affects the future

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