1. Review Midterm 1

2. Artificial Intelligence: An Introduction
Definition of AI
Foundations of AI
History of AI
Advanced Techniques

3. Definitions of AI Some accepted definitions of AI:
“The effort to make computers think…”
“The study of the design of intelligent agents…”
“The study of mental faculties through …
computational models.”
Dilemma: acting humanly vs acting rationally.

4. Acting Humanly
To pass the Turing test, a computer needs to
display the following abilities:
Natural language processing
Knowledge representation
Automated Reasoning
Machine Learning
Computer Vision
Robotics

5. Acting Rationally
Modern View.
More current view is to build rational agents.
Agents are autonomous, perceive, adapt,
change goals and deal with uncertainty.
It is easier to evaluate and more general.
The focus of this course is on Rational Agents.

6. Intelligent Agents Rationality Nature of the Environment
Introduction
Rationality
Nature of the Environment
Structure of Agents
Summary

7. Introduction What is an agent? Perceives environment through sensors
(cameras, keystrokes, etc.)
percept: single input
percept sequence: sequence of inputs
b. Acts upon environment through actuators
(motors, displays info.)

8. Figure 2.1

9. Definition A rational agent selects an action that
maximizes its performance measure given
The percept sequence
b. Built-in knowledge
Rational agents should be autonomous.
(learn under incomplete knowledge).

10. Properties of Environments
How do we define an environment?
Fully or partially observable
(Do sensors capture all relevant info.?)
Deterministic or stochastic
(Is next state completely determined by
the current state and action?)
Episodic or Sequential
(Is experience divided into atomic episodes?)

11. Properties of Environments
Static vs Dynamic
(Can environment change while taking
action?)
Discrete vs Continuous
(Is there a finite or infinite number of states?)
Single Agent vs Multiagent
(single-player game? or to-player game?)

12. Agents Reflex-based Model-Based Goal-based Utility-based
Learning Agents

13. Problem Solving By Searching
Introduction
Solutions and Performance
Uninformed Search Strategies
Avoiding Repeated States
Partial Information
Summary

14. Solutions We search through a search tree
We expand new nodes to grow the tree
There are different search strategies
Nodes contain the following:
state
parent node
action
path cost
depth

15. Search Tree
Initial state
Expanded nodes

16. Performance Four elements of performance:
Completeness (guaranteed to find solution)
Optimality (optimal solution?)
Time Complexity
Space Complexity

17. Performance Complexity requires three elements: Branching factor b
Depth of the shallowest node d
Maximum length of any path m

18. Techniques Breadth-first search Uniform-cost search Depth-first search
Depth-limited
Iterative deepening
Bi-directional search

19. Informed Search and Exploration
Search Strategies
Heuristic Functions
Local Search Algorithms
Summary

20. Greedy Best-First Search
Expand node with lowest evaluation
function f(n)
Function f(n) estimates the distance to
the goal.
Simplest case: f(n) = h(n) estimates cost of
cheapest path from node n to the goal.
** HEURISTIC FUNCTION **

21. A* Search Evaluation Function: F(n) = g(n) + h(n) Estimated cost of
cheapest path from
node n to goal
Path cost from root
to node n

22. Effective Branching Factor
N: total number of nodes generated by A*
d: solution depth
b* : branching factor a uniform tree of depth
d would have to contain N+1 nodes.
N + 1 = 1 + b* + (b*)2 + … + (b*)d

23. Effective Branching Factor
Example: N = 6; d = 2; b* = 2
Example: N = 3; d = 2; b* = 1

24. Local Search Hill Climbing Simulated Annealing Local Beam search
Genetic Algorithms

25. Figure 4.10

26. Logical Agents Logic Propositional Logic Summary
Knowledge-Based Agents
Logic
Propositional Logic
Summary

27. Knowledge-Based Algorithm
Function KB (percept) returns action
TELL (KB; PERCEPT-SENTENCE(percept,t))
action = ASK(KB,MAKE-ACTION-QUERY(t))
TELL (KB;ACTION-SENTENCE(action,t))
T = t + 1
Return action

28. Logic Important terms: Syntax .- rules to represent well-formed
sentences
Semantics.- define the truth of each sentence.
Model.- Possible world (m is a model of a)
Entailment:
a |= b a entails b

29. Logic Sentence a is derived from KB by algorithm i: KB |=i a
Algorithm i is called sound (truth preserving) if
it derives only entailed sentences.
Algorithm i is called complete if it derives all
entailed sentences.

30. Syntax Sentence  Atom | Complex Structure
Atom  True | False | Symbol
Symbol  P | Q | R
Complex S  sentence
| ( sentence ^ sentence )
| ( sentence V sentence )
| ( sentence  sentence)
| ( sentence  sentence)

31. Semantics How to define the truth value of statements?
Connectives associate to truth tables.
The knowledge base of an agent grows by
telling it of new statements:
TELL(KB,S1), … TELL(KB,Sn)
KB = S1 ^ … ^ Sn

32. Some concepts Equivalence: a = b  a |= b and b |= a Validity
A sentence is valid if it is true in all models
b |= a  a  b is valid
Satisfiability
A sentence is satisfiable if it is true in some
model

33. Proofs Reasoning Patterns Resolution Horn Clauses Forward Chaining
Backward Chaining

34. First-Order Logic Introduction Syntax and Semantics
Using First-Order Logic
Summary

35. Syntax Sentence  AtomicSentence | ( Sentence Connective Sentence )
| ( Quantifier Variable,… Sentence )
| ~Sentence
AtomicSentence  Predicate(Term) | Term = Term
Term  Function(Term)
| Constant
| Variable

36. Universal Quantifiers
How do we express properties of entire
collections of objects?
Universal quantification V
All stars are burning hydrogen:
V x Star(x)  burning-hydrogen(x)
True in all extended interpretations.

37. Existential Quantifiers
x Star(x) ^ burning-hydrogen(x)
Normally:
Universal quantifier connects with 
Existential quantifiers connect to ^

38. Classical Planning The Problem Syntax and Semantics
Forward and Backward Search
Partial Order Planning
History and Summary

39. The Problem Goal: Find sequence of actions to achieve a goal.
Search  Logic
Problems with previous search methods:
Irrelevant actions
Heuristic functions
Problem decomposition

40. Syntax States: Conjunction of literals Goals: One particular state
Actions: Preconditions and Effects
Example:
Action(Fly(p,from,to)),
PreCondition: At(p,from) ^ Plane(p) ^
Airport(from) ^ Airport(to)
Effect: ~At(p,from) ^ At(p,to)

41. Semantics Airport: At(P1,JFK) ^ At(P2,SFO) ^ Plane(P1) ^
Plane(P2) ^ Airport(JFK) ^ Airport(SFO)
Satisfies:
At(p,from) ^ Plane(p) ^ Airport(from) ^
Airport(to)
With Θ: {p/P1, from/JFK, to/SFO}

42. Types of state-space search
Forward search
Also called progressive planning
Ignores the irrelevant action problem
Branching factor is huge

43. Types of state-space search
Backward search
Also called regression planning
Needs predecessor function
Only relevant actions
Actions need to be consistent
(avoid undoing desired literals)

44. Backward state-space search
Strategy:
Given goal description G
Let A be an action (relevant and consistent)
Predecessor:
Delete positive effects of A
Add preconditions of A

45. Example Goal: At(P1,JFK) Action: Fly(P1,CHI,JFK)) Delete:(At(P1,JFK))
Add: At(P1,CHI)
SFO
CHI
JFK

46. Partial-Order Planning
Forward and Backward search look
for totally ordered plans.
Start  action1  action2  …  Goal
But ideally we wish to do the following:
Goal
subgoal1 subgoal2 subgoal3
combine

47. Consistency A consistent plan is one in which there
are no cycles in the ordering constraints
and no conflicts with casual links.
A consistent plan with no open
preconditions is a solution.

48. Pseudo Code Initial plan has Start, Finish, and
Start < Finish. (preconditions Finish).
Pick one precondition p and
find action A that achieves p.
Check whether the plan is a solution
(are there open preconditions?)
If not return to 2.

49. Enforced Consistency Consistency is enforced as follows:
Link A p B and constraint A < B
are added to the plan. If A is new add
Start < A , A < Finish
If conflict exists between A p B and C
Then make B < C or C < A.

50. Example Start: At(Flat,Axle) ^ At(Spare,Trunk) Goal: At(Spare,Axle)
Actions:
Remove(Spare,Trunk)
Precond: At(Spare,Trunk)
Effect: ~At(Spare,Trunk) ^ At(Spare,Ground)
Remove(Flat,Axle)
PutOn(Spare,Axle)
LeaveOvernight

51. Example Actions Remove(Flat,Axle) precond: At(Flat,Axle)
effect: ~At(Flat,Axle) ^ At(Flat,Ground)
PutOn(Spare,Axle)
precond: At(Spare,Ground) ^ ~At(Flat,Axle)
effect: ~At(Spare,Ground) ^ At(Spare,Axle)
LeaveOvernight
effect: ~At(Spare,Ground) ^ …

52. Example Plan Start Remove(Spare,Trunk) Finish PutOn(Spare,Axle)
At(Spare,Trunk)
Start
At(Flat,Axle)
At(Spare,Trunk)
At(Spare,Axle)
Remove(Spare,Trunk)
Finish
At(Spare,Ground)
PutOn(Spare,Axle)
~At(Flat,Axle)

53. Example Plan Conflict PutOn(Spare,Axle) Remove(Spare,Trunk) ?
At(Spare,Ground)
At(Spare,Trunk)
PutOn(Spare,Axle)
Remove(Spare,Trunk) ?
~At(Flat,Axle)
~At(Flat,Axle)
LeaveOvernight
~At(Spare,Ground)
~At(Spare,Trunk)
Fails

54. Example Final Plan Start Remove(Spare,Trunk) Finish PutOn(Spare,Axle)
At(Spare,Trunk)
Start
At(Flat,Axle)
At(Spare,Trunk)
At(Spare,Axle)
Remove(Spare,Trunk)
Finish
At(Spare,Ground)
PutOn(Spare,Axle)
~At(Flat,Axle)
At(Flat,Axle)
Remove(Flat,Axle)

55. Planning in the Real World
Time and Resources
Hierarchical Task Network
Conditional Planning
Execution Monitoring and Replanning
Continuous Planning
MultiAgent Planning
Summary

56. Fig 12.2

57. Resource Constraints Resource(k): k units of resource are needed
by the action.
The resource is reduced by k during the
duration of the action.

58. Fig. 12.4

59. Hierarchical Task Network
High Order Goal
Subgoal Subgoal 2 … Subgoal n
Build House
Get Land Construct Pay Builder

60. Fig. 12.5

61. Hierarchical Task Network
HTN is complicated (undecidable).
Recursion is a problem.
Allows subtask sharing.
It is in general more efficient than naïve
planning (linear rather than exponential).
Successful story: O-Plan (Bell and Tate ‘85)
It helps develop production plans for
Hitachi (350 products, 35 assembly
machines, operations).

62. Conditional Planning What to do with incomplete and incorrect
information?
Assume “bounded indeterminacy”
Solution: Construct a conditional plan
with branches to consider all
sorts of contingencies
(include sensing actions)

63. Actions Actions can have disjunctive effects: Example: Vaccum Cleaner
Action(Left,
Precond: At Right
Effect: At Left V AtRight)

64. Effects Also add conditional effects: Example: Vaccum Cleaner
Action(Suck,
Precond:
Effect: When (At Left) CleanL
When (AtRight) CleanR)

65. Fig. 12.9

66. Execution Monitoring Check to see if all is going according
to the plan.
Re-planning agents repair old plans if
something goes wrong.
Uses action monitoring to repair and
continue with the plan.

67. Fig

68. Continuous Planning The agent persists in the environment
indefinitely.
The agent is part of the way through
executing a plan.
Example:
Blocks-world problem.

69. Fig

70. MultiAgent Planning Interaction can be Cooperative Joint plan
A goal is achieved when each agent
performs its assigned actions.
Competitive

71. Cooperative Play Tennis (doubles) Plan 1:
A: [Go(A, [Right,Baseline]), Hit(A,Ball)]
B: [NoOp(B), NoOp(B)]

72. Cooperative A common solution is to have a
convention (constraint on joint plan).
Conventions arise in evolutionary
processes.
Example:
An ant colony.
Flocking behavior of birds
Besides convention you may have
communication.

73. Maximum Expected Utility
EU(A|E) = Σ P(Resulti(A)) U(Resulti(A))
Principle of Maximum Expected Utility:
Choose action A with highest EU(A|E)

74. Example Robot Turn Right Hits wall (P = 0.1; U = 0)
Turn Left
Hits wall (P = 0.1; U = 0)
Finds target (P = 0.9; U = 10)
Falls water (P = 0.3; U = 0)
Finds target (P = 0.7; U = 10)
Choose action “Turn Right”

75. Utility Functions Television Game Show:
Assume you already have won $1,000,000
Flip a coin:
Tails (P = 0.5)  $3,000,000
Head (P = 0.5)  $0

76. Utility Functions EU(Accept) = 0.5 U(Sk) + 0.5 U(Sk + 3M)
EU(Decline) = U(Sk + 1M)
Assume:
Sk = 5
Sk + 1M = 8
Sk + 3M = 10

77. Fig. 16.2

78. Risk-Averse Positive part: slope decreasing.
Utility is less than expected monetary value U $

79. Risk-Seeking Negative part: Linear curve: desperate region.
risk neutral U $ U $

80. Connection to AI Choices are as good as the preferences
they are based on.
If user embeds in our intelligent agents :
contradictory preferences
Results may be negative
reasonable preferences
Results may be positive

81. Assessing Utilities Best possible outcome: Amax
Worst possible outcome: Amin
Use normalized utilities:
U(Amax) = 1 ; U(Amin ) = 0

82. Decision Networks It’s a mechanism to make rational decisions
Also called influence diagram
Combine Bayesian Networks with
other nodes

83. Types of Nodes Chance Nodes. Represent random variables (like BBN)
Decision Nodes
Choice of action
Utility Nodes
Represent agent’s utility function

84. Fig. 16.5

85. The Value of Information
Important aspect of decision making:
What questions to ask.
Example:
Oil company.
Wishes to buy n blocks of ocean drilling rights.

86. The Value of Information
Exactly one block has oil worth C dollars.
The price of each block is C/n.
A seismologist offers the results
of a survey of block number 3.
How much would you pay for the info?

87. The Value of Information
Expected improvement in utility compared
with making a decision without that information.