Q1: Convert
B
(
P
∨
Q
)
to CNF?
13.1
-
13.10
;
13.13
14.1
(
no e
)
; 14.2.a;
7.1
-
7.2; 7.5; 7.7;
4.3; 4.5;
3.1, 3.6
-
3.7
Introduction:
F
our types of AI
Turing test:
怎么做的,测试什么
acting humanly
Some AI applications, intelligent systems
Agent:
Agent and Ration
ality, performance measure not unique
PEAS
Environment types:
F
ull observable
Deterministic
S
tatic
D
iscrete
S
ingle agent
Problem
-
solving agent:
Problem types:
Deterministic, fully observable
single
-
state problem
Non
-
observable
sensorless problem
No
ndeterministic and/or partially observable
contingency
problem
Unknown state space
exploration problem
Problems:
Romania
Vacuum cleaner
8
-
puzzle
Define a problem
I
nitial state
A
ctions or successor function
G
oal test
P
ath cost
Search
Tree search
:
generate successors of already
-
explored states
N
ode
is a date structure containing much more information
than
state
Search strategy: how to pick the order of node expansion
Properties of search strategy
C
ompleteness
O
ptimality
Uninformed search: only the
information in problem
definition
, no heuristics
Breadth
-
first search
: shallowest unexpanded node
Uniform
-
cost search
: least
-
path
-
cost unexpanded node
Depth
-
first search
: deepest unexpanded node
Informed search
B
est
-
first search with an evaluation functio
n (e.g.
straight
-
line distance) for each node
G
reedy search
A
*
search
:
Local search: hill
-
climbing
Logic agent:
Concepts:
Knowledge base
, syntax, semantics, entailment, models,
Entailment validation:
Check the subset relation
Inference: soundness
and completeness
P
ropositional logic: negation, conjunction, disjunction,
implication, biconditional
Model checking: The truth table for inference, namely
inference by enumeration
Application of inference rules:
L
ogic equivalent: Morgan, distributivity
…
I
nference in Horn form: forward and backward chain
Inference in CNF form: resolution
Question Q1
Probabilistic
agent:
Concepts:
sample point, some space,
probability
space, event,
random variable, syntax for probability, prior probability, posteri
or
probability,
Simple Computation:
Inference by enumeration: compute from the joint distribution
table
P(cavity
j
toothache)
Chain rule
Inference by enumeration: P(Y|E)
Modelling and Computation by conditional independence
Independence
C
onditional indep
endence
Three equivalent definition of CI.
Interprete CI
Modelling joint distribution using CIs
Why CIs make things simpler
A more systematic model: Bayesian Networks
Definition of BNs: syntax and semantics
Compactness
C
onstruction of BNs
Computation:
Bayesian Rule
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