This is Pusdo Code for AI logic

1. 1 INTRODUCTION
1

2. 2 INTELLIGENT AGENTS
function TABLE -D RIVEN -AGENT( ¨§¦¤¢
© ¡¥£¡
) returns an action
static: ¨§¦¤¢
© ¡¥£¡
, a sequence, initially empty
¤¨©
¡
, a table of actions, indexed by percept sequences, initially fully specified
append §¦§¢
© ¡¥£¡ to the end of¨§¦¤¢
© ¡¥£¡
% # quot; © ¥
$!! L OOKUP( , ¡ © ¡ ¥ £ ¡
(§'¨© ¨§¦¤¢ )
return $!¤
# quot; ©¥
Figure 2.8
function R EFLEX -VACUUM -AGENT( [ 4!$¨2 $!10)
3© © # quot; © ¥ quot;
, ]) returns an action
if 7© £
!§6 5 4!$¨2= 3© © then return 9¥
¤¢3 8
else if # quot; © ¥ quot;
$!!$0)@ = A then return © D C
'1@ B
# quot; © ¥ quot;
E $!!$0)@
else if = then return ©G
0H¡ F
Figure 2.10
function S IMPLE -R EFLEX -AGENT( §¦§¢
© ¡¥£¡
) returns an action
static: §(I§£
¡ 3
, a set of condition–action rules
P1¨0
% ¡© © I NTERPRET-I NPUT( 02§¢
© ¡¥£¡ )
% ¡ 3
P(I§£ RULE -M ATCH( , ¡ 3 ¡ © ©
¤4§£ $Q0 )
%$!!
# quot; © ¥ RULE -ACTION[ ] I§£
¡ 3
return $!¤
# quot; ©¥
Figure 2.13
2

3. 3
function R EFLEX -AGENT-W ITH -S TATE( ¨§¦¤¢
© ¡¥£¡ ) returns an action
static: $Q0
¡© ©
, a description of the current world state
§(I§£
¡ 3
, a set of condition–action rules
#$!¤
quot; ©¥
, the most recent action, initially none
% ¡© ©
P1¨0 §¦§¢ $!¤ $Q0
© ¡¥£¡ # quot; ©¥ ¡© ©
U PDATE -S TATE( , , )
% ¡ 3
P(I§£ RULE -M ATCH( , ¡ 3 ¡ © ©
¤4§£ $Q0
)
%$!!
# quot; © ¥ RULE -ACTION[ ] I§£
¡ 3
return $!¤
# quot; ©¥
Figure 2.16

4. 3 SOLVING PROBLEMS BY
SEARCHING
function S IMPLE -P ROBLEM -S OLVING -AGENT( R§¦¤¢
© ¡¥£¡
) returns an action
inputs: R§¦¤¢
© ¡¥£¡, a percept
static: 2
S¡
, an action sequence, initially empty
$Q0
¡© ©
, some description of the current world state
1)'C
quot;
, a goal, initially null
§(§'¦
T ¡ quot; £
, a problem formulation
U PDATE -S TATE( , )
P1¨0
© ¡¥£¡ ¡© ©
% ¡© ©
02§¢ $Q0
if is empty then do 2¤
S¡
F ORMULATE -G OAL( $)C
% quot;
) 1¨0
¡© ©
%UT¤¤2R
¡ quot;£
F ORMULATE -P ROBLEM( quot; ¡© ©
$)'C $Q0
, )
T§(§'quot;¦£
¡ S EARCH( ) V2¤
% S¡
S2¡
F IRST( ) $!!
% # quot; © ¥
R EST( ) S¦¡ W2
% S¡
return $!¤
# quot; ©¥
Figure 3.2
function T REE -S EARCH( , T ¡ quot; £
§(§'¦ 7 C¡© £©
$Q12!0
) returns a solution, or failure
initialize the search tree using the initial state of §(§'¦
T ¡ quot; £
loop do
if there are no candidates for expansion then return failure
choose a leaf node for expansion according to $Q12!0
7 C¡© £©
if the node contains a goal state then return the corresponding solution
else expand the node and add the resulting nodes to the search tree
Figure 3.9
4

5. Figure 3.17
if then return £ else return
¡0I3 6$¤G qquot; 3
sQ©4¤¥ r a¡££ 3 ¥¥ qquot;© 3
¢22§I0)quot; s¨I¤¥
else if then return © 42¤2£
3¡ 0I3 6$¤G v© I0¤2£
¡ £ uw 3 ¡
if = then true Pr¢22§£I0)quot; qsquot;¨©I3¤¥
% a¡£ 3 ¥¥ s¨I¤¥ © I0¤2£
qquot;© 3 3¡
R ECURSIVE -DLS( © T , , )
6p6 §¡(§'¦ $¤0§¡§0¢30
T quot;£ £ quot; ¥¥ RtI0§2£
% © 3 ¡
else for each in E XPAND( , ) do
T¡ quot;£
¤¤'¦ ¡')# a quot; £$¤2¤§0¢0
quot;¡¥¥ 3
else if D EPTH[ ]= then return quot;© 3
qsQ4¤¥ © 6pTH ¡ a quot;
')b#
if G OAL -T EST[ ¡ a quot;
')# ](S TATE[ ]) then return S OLUTION( )
¡ a quot;
')b# T ¡ quot; £
§(§'¦
false P¢22§I0)quot; ¨I¥
% r a¡££ 3 ¥¥ qquot;© 3
function R ECURSIVE -DLS( , , ) returns a solution, or failure/cutoff
© T
6p6 T ¡ quot; £ ¡ a quot;
§(§'¦ ')b#
return R ECURSIVE -DLS(M AKE -N ODE(I NITIAL -S TATE[
6p6
© T T ¡ quot; £
§(§'¦ T ¡ quot; £
§(§'¦ ]), , )
function D EPTH -L IMITED -S EARCH( , ) returns a solution, or failure/cutoff
© T
6pH T ¡ quot; £
¤¤2R
Figure 3.12
return §$¤0§§0¢0
£ quot;¡¥¥ 3
add to £ quot;¡¥¥ 3
§$¤0§§0¢0
D EPTH[ ] D EPTH[ ]+1 ')#
¡ a quot; %
, ) $!¤ ')b#
# quot; ©¥ ¡ a quot; PATH -C OST[ ] PATH -C OST[ ] + S TEP -C OST(
¡ a quot;
')# , %
ACTION[ ] # quot; ©¥
$!¤i%
PARENT-N ODE[ ] ¡'a)quot;b#h%
S TATE[ ] © I0¤2g%
3¡£
a new N ODE % f
]) do )b#
¡ a quot; for each ,
¤¤'¦
T ¡ quot; £in S UCCESSOR -F N[ ](S TATE[ e 3¡ # quot; ©¥
¦© I0¤2£ $!¤ d
the empty set % £ quot;¡¥¥ 3
c§$¤0§§0¢0
) returns a set of nodes T ¡ quot; £ X ¡ a quot;
¤¤'¦¨)b# function E XPAND(
'I6§0G
¡ C # £ I NSERT-A LL(E XPAND(
¤¤'¦ ')#
T ¡ quot; £ ¡ a quot; , ), ) `'I6§0G
% ¡ C # £
then return S OLUTION( ) ¡ a quot;
')#
if G OAL -T EST[
')b#
¡ a quot; ] applied to S TATE[ ] succeeds ¤¤'¦
T ¡ quot; £
R EMOVE -F IRST( ) I'H§0G
¡ C # £ % ¡ a quot;
P')#
if E MPTY ?( ) then return failure ¡ C # £
I'6§§G
loop do
) 'I6§0G
¡ C # £ I NSERT(M AKE -N ODE(I NITIAL -S TATE[
T ¡ quot; £
§(§'¦ ]), % ¡ C # £
`I'H§0G
) returns a solution, or failure ¡ C # £ GX T ¡ quot; £
'I6§0HY§(§'¦ function T REE -S EARCH(
5

6. 6 Chapter 3. Solving Problems by Searching
function I TERATIVE -D EEPENING -S EARCH( ¤¤'¦
T ¡ quot; £ ) returns a solution, or failure
inputs: §(§'¦
T ¡ quot; £
, a problem
for xHa
% D© ¡ 0 to y do
RtI0§2£
% © 3 ¡ D EPTH -L IMITED -S EARCH( ¤¤'¦
T ¡ quot; £ , !R'a
D© ¡ )
uv© I0¤2£
w 3¡
if cutoff then return tI0§0£
© 3 ¡
Figure 3.19
function G RAPH -S EARCH( 'I6§0H€¤¤'¦
¡ C # £ GX T ¡ quot; £ ) returns a solution, or failure
2$@¥
% a ¡ quot; an empty set
% ¡ C # £
`I'H§0G I NSERT(M AKE -N ODE(I NITIAL -S TATE[ ]), ) T ¡ quot; £
§(§'¦ ¡ C # £
'I6§0G
loop do
if E MPTY ?( I'6§§G
¡ C # £
) then return failure
P')#
% ¡ a quot; R EMOVE -F IRST( ) I'H§0G
¡ C # £
if G OAL -T EST[ T ¡ quot; £
¤¤'¦
](S TATE[ ¡ a quot;
')#
]) then return S OLUTION( ¡ a quot;
')b# )
if S TATE[ ¡ a quot;
')#
] is not in then a ¡ quot;
2$@¥
add S TATE[ ] to )b#
¡ a quot; 2$@¥
a ¡ quot;
% ¡ C # £
`'I6§0G
I NSERT-A LL(E XPAND( , ), ) T ¡ quot; £ ¡ a quot;
¤¤2R ')# ¡ C # £
'I6§0G
Figure 3.25

7. 7
Figure 4.13
£ quot; D C ¡ # % © #¡££ 3
$2¢1§‰hRR§0§I¤¥
] © #¡££ 3
R¤2§I¥ if VALUE[neighbor] VALUE[current] then return S TATE[ j
a highest-valued successor of
© #¡££ 3
¢¤2§I¥ % £ quot; D C ¡
V$24$@¤b#
loop do
]) T ¡ quot; £
¤¤'¦ M AKE -N ODE(I NITIAL -S TATE[ % © #¡££ 3
R¢¤2§I¥
, a node £$24$@¤b#
quot; D C ¡
local variables: , a node R§0§I¤¥
© #¡££ 3
inputs: , a problem ¤¤'¦
T ¡ quot; £
function H ILL -C LIMBING( ) returns a state that is a local maximum
¤¤'¦
T ¡ quot; £
Figure 4.6
if then return © 42¤2£
3¡ 0I3 6$¤G v© I0¤2£
¡ £ uw 3 ¡
, [ ] RBFS( , ,
1$6$§¤© $i§6pH ¤c@gƒ 0¤2 ¤¤2R
‘ ¡ d © # £ ¡ X © T G ˆ hf © ¡ T ¡ quot; £ ) % 0§0 ‚ tI0§2£
©¡ © 3 ¡
the second-lowest -value among
£ quot;¡¥¥ 3
§$¤0§§0¢0 ‚ Pe61b§¤¨t1
% ¡ d © # £ ¡ ©
if then return ©¡ , [
0§0 ‚ 2It6$¤G ]
¡ £ 3 6p6t g™0¤0 P‚
© T G ˜ —©¡ –
the lowest -value node in £ quot;¡¥¥ 3
§$¤0§§0¢0 ‚ % ©¡
R0¤2
repeat
[s] ‘— ¡ a quot; – X ‘ ˆ ” ’ ‘ ˆ ˆ ‡ … ƒ
H¤')# P‚ §)s“•“)sbH‰1†„% ‚
for each in do £ quot;¡¥¥ 3
§1¤0¤00¢0
if is empty then return , y ¡2£43 6$¤G
§$§0¤§2R2
£ quot;¡¥¥ 3
E XPAND( , ) T¤¡¤'¦ ')#
quot;£ ¡ a quot; c§$¤0§§0¢0
% £ quot;¡¥¥ 3
if G OAL -T EST[ ]( ) then return¡ a quot;
')b# ¡© ©
$Q0 ¤¤'¦ T ¡ quot; £
‚
function RBFS( , , ) returns a solution, or failure and a new -cost limit
© T
Hp6 G ')b# ¤¤'¦
¡ a quot; T ¡ quot; £
RBFS( y
, M AKE -N ODE(I NITIAL -S TATE[
T ¡ quot; £
§(§'¦ ]), ) ¤¤'¦
T ¡ quot; £
function R ECURSIVE -B EST-F IRST-S EARCH( ) returns a solution, or failure
T ¡ quot; £
§(§'¦
EXPLORATION 4
INFORMED SEARCH AND

8. 8 Chapter 4. Informed Search and Exploration
function S IMULATED -A NNEALING( , T ¡ quot; £
§(§'¦ ¡ 3 a ¡ D ¥
I$2¢§¤
) returns a solution state
inputs: ¤¤'¦
T ¡ quot; £
, a problem
(I$¦¢¤¤
¡ 3 a ¡ D ¥
, a mapping from time to “temperature”
local variables: R§0§I¤¥
© #¡££ 3
, a node
, a node 10b#
© k¡
l
, a “temperature” controlling the probability of downward steps
M AKE -N ODE(I NITIAL -S TATE[ R¢¤2§I¥
% © #¡££ 3
¤¤'¦
T ¡ quot; £ ])
for 1 to do y R©
%
[ ] © 431a2¢¤§g% l
¡ ¡ D¥
R§2£§43¤¥
© #¡ £ l
if = 0 then return
R#§2§43¤¥
© ¡££ a randomly selected successor of 10b#
% © k¡
©R#§¡2£§£43¤¥
VALUE[ ] – VALUE[ 10¡b#
© k ] % om n
if 0 then©$k2¡‰#hR©R§¡0§£I¤¥
% # £ 3 ˜ gm n
tsfrPip
s q else $2‰#hRR§0§I¤¥
© k¡
only with probability % © #¡££ 3
Figure 4.17
function G ENETIC -A LGORITHM( $!!$I04
# quot; © 3 quot;
, F ITNESS -F N) returns an individual
inputs: $!1I§4
# quot; © 3 quot;
, a set of individuals
F ITNESS -F N, a function that measures the fitness of an individual
repeat
empty set $!$@I§4 ¤b#
% # quot; © 3 quot; u ¡
$!!$I04
# quot; © 3 quot;
loop for from 1 to S IZE( ) do
1quot;!©$@4R§4
# 3 quot;
R ANDOM -S ELECTION( vk
%
, F ITNESS -F N)
#$!$@I§4
quot; © 3 quot;
R ANDOM -S ELECTION( w7
%
, F ITNESS -F N)
7 k
R EPRODUCE( , ) 6'§¥
% a D
a D
%v@6'¤¥
if (small random probability) then M UTATE( a D
@H¤¥ )
add to $!$@I§4 ¤‰#
# quot; © 3 quot; u ¡ @6'¤¥
a D
$!$@I§4 ¤bgx$!!$I04
# quot; © 3 quot; u ¡ # % # quot; © 3 quot;
until some individual is fit enough, or enough time has elapsed
return the best individual in $!!$I04
# quot; © 3 quot;
, according to F ITNESS -F N
7 k
function R EPRODUCE( , ) returns an individual
7 k
inputs: , , parent individuals
% # L ENGTH( ) k
% `¥
random number from 1 to #
k
return A PPEND(S UBSTRING( , 1, ), S UBSTRING( , ¥ # {y¥ 7
z ’ , ))
Figure 4.21

9. 9
function O NLINE -DFS-AGENT( ) returns an action |
|
inputs: , a percept that identifies the current state
static: © I0¤2£
3¡
, a table, indexed by action and state, initially empty
a22£$@§2‰I3
¡ quot; k¡ #
, a table that lists, for each visited state, the actions not yet tried
a2¡e9¤¦!1¤e2‰I3
¥ £© 9 ¥ #
, a table that lists, for each visited state, the backtracks not yet tried
, , the previous state and action, initially null
|
if G OAL -T EST( ) then return §¨0
quot;©
|
if is a new state then % | 22$@00‰I3
a ¡ £ quot; k ¡ #
[ ] ACTIONS( ) |
if is not null then do
[ , ] | h% tI0§2£
© 3 ¡
| 2¡e¤¦!©19¤¥e2‰I3
a 9¥ £
add to the front of # [ ]
if [ ] is empty then | 22$R§0bI3
a ¡ £ quot; k ¡ #
§quot;¨©2
if | 2e¤¦!1¤e2‰I3
a¡ 9 ¥ £© 9 ¥ #
[ ] is empty then return
else 3
| © I0¤¡2£
an action such that [ , ] = P OP( % w | 2e¤¦!1¤e2‰I3
a¡ 9 ¥ £© 9 ¥ #
[ ])
else P OP( [ ]) ¡ quot; k¡ #
| a22£$@§2‰I3 v
%
| h`
%
return
Figure 4.25
function LRTA*-AGENT( ) returns an action |
|
inputs: , a percept that identifies the current state
static: © I0¤2£
3¡
, a table, indexed by action and state, initially empty
}
, a table of cost estimates indexed by state, initially empty
, , the previous state and action, initially null
|
if G OAL -T EST( ) then return §¨0
quot;©
| } } | ~% |
”
if is a new state (not in ) then [ ] ( )
unless is null
| h% tI0§2£
[ , ] © 3¡
h
[ ] foƒ % }
LRTA*-C OST( , , 3¡
© 42¤2£
[ , ],
}
)
ƒs
‚ ¦
€ ACTIONS
v
% | } © I0¤2£
| 3¡ |
an action in ACTIONS( ) that minimizes LRTA*-C OST( , , [ , ], )
| h`
%
return
} |
function LRTA*-C OST( , , , ) returns a cost estimate
| iQc”
‘ „ˆ
if is undefined then return
else return — | „ – ˆg‘ | 02eQˆ …
‡ ’ „X †X „
Figure 4.29

10. CONSTRAINT
5 SATISFACTION
PROBLEMS
¥
function BACKTRACKING -S EARCH( ) returns a solution, or failure
return R ECURSIVE -BACKTRACKING( , ) ¥ W‰
Š
function R ECURSIVE -BACKTRACKING( , Q¥ ©R§¡gTRe@02$
# # C
) returns a solution, or failure
if is complete then return R¤g¢e@001
© #¡ T # C
©R§¡gRe@02$
# T # C
S ELECT-U NASSIGNED -VARIABLE(VARIABLES[ ], © #¡ T # C
¥ R¤g¢e@001 Q¤¥
, ) % £
V$$d
for each in O RDER -D OMAIN -VALUES( , , ) do © #¡ T # C £
¥ R§gR$20$ $1d
3
¡Rt1$d
if is consistent with R#§gR$20$
© ¡ T # C Q¥
according to C ONSTRAINTS[ ] then ¡ 3
Rt1$d
add = to #¡ T C
©¢¤g¢#e@00$ Š R3 $$d £$1d ‰
¡
C
Q¤¥ ©¢#¤¡gT¢#e@00$
R ECURSIVE -BACKTRACKING( , ) RtI0§2£
% © 3 ¡
if © I30¤2£
¡
then return 2£It6$¤G uvtI0§0£
¡ 3 w © 3 ¡
remove = T # C
©¢#¤¡gR$@00$
from £
Š ¡R3 $$d $$d ‰
return 2It6$¤G
¡ £ 3
Figure 5.4
10

11. 11
function AC-3( Q¥
) returns the CSP, possibly with reduced domains
inputs:
Q¥
, a binary CSP with variables X    Ž
Š‘o‹ X g‹ X i‰
Œ ‹
local variables: R¤R¤S
¡ 3¡ 3
, a queue of arcs, initially all the arcs in Q¥
while §RS
¡ 3¡ 3 is not empty do
% “
R‘ p‹ X o‹ ˆ
’ R EMOVE -F IRST( ) ¡ 3¡ 3
R¤RS
if R EMOVE -I NCONSISTENT-VALUES( “ ‹ X’ ‹ ) then
for each in N EIGHBORS[ ] do
” †‹ ’ o‹
add ( ’ ‹ X ™‹
) to” R¤RS
¡ 3¡ 3
function R EMOVE -I NCONSISTENT-VALUES( “ ‹ X’ ‹ ) returns true iff we remove a value
$¤•v2$$o¤2£
¡ G % a¡ d quot; T¡
k
for each in D OMAIN[ ] do ’ ‹
if no value in D OMAIN[ ] allows ( , ) to satisfy the constraint between
– “ ‹ 7 k ’ ‹ and “ ‹
k
then delete from D OMAIN[ ]; ’ ‹ ¡R3§£©h%va2¡$d$quot;o¤2£
T¡
return 2$$o¤2£
a¡ d quot; T¡
Figure 5.9
function M IN -C ONFLICTS( , ¨0 ioT ¥
¡© k
) returns a solution or failure
inputs: Q¥
, a constraint satisfaction problem
¨0 ioT
¡© k
, the number of steps allowed before giving up
R¢¤2§I¥
% © #¡££ 3
an initial complete assignment for ¥
for = 1 to ¨0 ioT
¡© k
do
¥
if is a solution for © #¡££ 3
¢¤2§I¥
then return © #¡££ 3
R§0§I¤¥
V$1d
% £
a randomly chosen, conflicted variable from VARIABLES[ ]¥
£ $1d d
the value for `3 $1d
% ¡
that minimizes C ONFLICTS( , , 3, £
Q¤¥ ©R#¤¡2£§£I¥ d 1$d )
set R§¡2£§£43¤¥
© # in Rt1$d w 1$d
¡ 3 £
return 2It6$¤G
¡ £ 3
Figure 5.11

12. 6 ADVERSARIAL SEARCH
function M INIMAX -D ECISION( 1¨0
¡© ©
) returns $!¤—$
# quot; ©¥ #
inputs: ¨$¨0
¡© ©
, current state in game
% wd M AX -VALUE(state)
return the # quot; ©¥
$!¤
in S UCCESSORS( $Q0
¡© © ) with value d
function M AX -VALUE( 1¨0
¡© © ) returns Rt1$˜!6t6Ip
¡ 3 d 7© © 3
if T ERMINAL -T EST( 1¨0
¡© © ¡© ©
¨$¨2
) then return U TILITY( )
y gwd
™ %
X
R
for in S UCCESSORS( ) do¡© ©
$Q0
% wd M AX(v, M IN -VALUE( ))
return d
function M IN -VALUE( $Q0
¡© © ) returns Rt$1˜!H 6Ix
¡ 3 d 7© © 3
if T ERMINAL -T EST( ¡© ©
1¨0 ) then return U TILITY( ¡© ©
¨$¨2 )
y wd %
X
R
for in S UCCESSORS( ) do¡© ©
$Q0
% wd M IN(v, M AX -VALUE( ))
return d
Figure 6.4
12

13. 13
function A LPHA -B ETA -S EARCH( ) returns an action ¨$¨2
¡© ©
inputs: ¨$¨0
¡© ©
, current state in game
% wd M AX -VALUE( , , ) ¡© ©
y ™ ¨$¨0 y ’
return the # quot; ©¥
$!¤
in S UCCESSORS( $Q0
¡© © ) with value d
function M AX -VALUE( › š 1¨0
¡© ©
, , ) returns R3 $$p6t6!4x
¡ d 7© © 3
inputs: ¨$¨0
¡© ©
, current state in game
, the value of the best alternative for
š MAX
1¨0
¡© ©
along the path to
, the value of the best alternative for
› MIN along the path to ¨$¨2
¡© ©
if T ERMINAL -T EST( ¡© ©
1¨0 ) then return U TILITY( ¡© ©
¨$¨2 )
y gwd
™ %
for X
R in S UCCESSORS( ) do ¡© ©
$Q0
wd
% M AX(v, M IN -VALUE( , , )) › š
›Ÿžœ if then return d
% šM AX( , v) š
d
return
function M IN -VALUE( › š $Q0
¡© ©
, , ) returns 3 $1˜6 6Ip
¡ d 7© © 3
inputs: ¨$¨0
¡© ©
, current state in game
, the value of the best alternative for
š MAX
1¨0
¡© ©
along the path to
, the value of the best alternative for
› MIN along the path to ¨$¨2
¡© ©
if T ERMINAL -T EST( ¡© ©
1¨0 ) then return U TILITY( ¡© ©
¨$¨2 )
y ~wd
’ %
for X
R in S UCCESSORS( ) do ¡© ©
$Q0
wd
%
M IN(v, M AX -VALUE( , , )) › š
š jžœif then return d
› % ›
M IN( , v)
d
return
Figure 6.9

14. 7 LOGICAL AGENTS
function KB-AGENT( §¦§¢
© ¡¥£¡
) returns an $!!
# quot; © ¥
static: E, a knowledge base
¢¡
©
, a counter, initially 0, indicating time
T ELL( E ¢¡ , M AKE -P ERCEPT-S ENTENCE( , )) © ¨§¦¤¢
© ¡¥£¡
$!!
% # quot; © ¥A SK( E , M AKE -ACTION -Q UERY( ))
¢¡ ©
T ELL( E¢¡ , M AKE -ACTION -S ENTENCE( , )) © 1!
# quot; © ¥
+1 h©
© %
#$!©¤¥
quot;
return
Figure 7.2
function TT-E NTAILS ?( , ) returns
E ¢¡ or š ¡ 3£
R§© ¡
t1G
inputs: , the knowledge base, a sentence in propositional logic
E ¢¡
š , the query, a sentence in propositional logic
quot; T 7
%P $2g10a list of the proposition symbols in E ¢¡ and š
return TT-C HECK -A LL( , , , )E x¡ quot; T 7
— – $2g10 š
function TT-C HECK -A LL( , , , E ¢¡ ¤')oT $2g10 š
¡ a quot; quot; T 7
) returns ¡ 3£
R§© or $¤G
¡
if E MPTY ?( $2g10
quot; T 7
) then
if PL-T RUE ?( E ,
x¡ ¡ a quot;
¤)oT
) then return PL-T RUE ?( , ¡ a quot;
§')oT š )
else return R§©
¡ 3£
else do
% £ F IRST( $2g10
); quot; T 7
R EST( ) % ©¡
R0§2£ quot; T 7
$2g10
return TT-C HECK -A LL( , , , E XTEND( 0¤2£ š ¢¡
©¡ E ¤')o§eR§§X £
¡ a quot; TX ¡ 3£© ) and
TT-C HECK -A LL( , , ©0¤2£ š ¢¡
, E XTEND(¡ E ¤¡')o§¤t$¤HX £
a quot; T X ¡ G )
Figure 7.12
14

15. 15
function PL-R ESOLUTION( , ) returns E ¢¡or š ¡ 3£
R§© ¡
$¤G
inputs: , the knowledge base, a sentence in propositional logic
E ¢¡
š , the query, a sentence in propositional logic
%f¤I$@¥
¡ 3 the set of clauses in the CNF representation of W€•¢¡
š ¥ ¤ E
ŠW‰ ¦§‰# % u¡
loop do
for each , in ¡ 3
§I$@¥do
“ § ’ §
f!R¤$t1¤¤2£
% © # ¡ d quot; ¡
PL-R ESOLVE( , ) “ § ’ §
if R§ed $¤§0£
© #¡ quot;¡
contains the empty clause then return R§©
¡ 3£
©¢#¤¡ed $¤§¡2£©¨—§‰h¦§‰#
quot; u¡ # % u¡
¡ $¤G
if §I$@•«§‰#
¡ 3 ¥ ª u ¡
then return
§‰#™c§I$@¬`¤¤41¤¥
u ¡ ¨ ¡ 3 ¥ % ¡ 3
Figure 7.15
function PL-FC-E NTAILS ?( , ) returns E ¢¡ or S R§!©
¡ 3£ $¤G
¡
inputs: E, the knowledge base, a set of propositional Horn clauses
¢¡
S
, the query, a proposition symbol
local variables: RI$2¥
© # 3 quot;
, a table, indexed by clause, initially the number of premises
22§§)6
a¡££¡ G #
, a table, indexed by symbol, each entry initially t1G
¡
¤I
a #¡ C
, a list of symbols, initially the symbols known to be true in E ¢¡
while eb¤')
a #¡ C
is not empty do
P OP(b§I)
a #¡ C ) % ­
unless 22§¤H
a¡££¡ G #
[ ] do
¡R3§£©g% 22§¤)6
[ ]a¡££¡ G #
for each Horn clause in whose premise ¥ appears do
decrement ¥ RI$0¥
[ ] © # 3 quot;
if ¥ ¢410¥
© # 3 quot;
[ ] = 0 then do
¥
if H EAD[ ] = then return S R§!©
¡ 3£
P USH(H EAD[ ], ) ¥ b§I)
a #¡ C
return $¤G
¡
Figure 7.18

16. 16 Chapter 7. Logical Agents
function DPLL-S ATISFIABLE ?( ) returns ¡ 3£
R§!© or $¤G
¡
inputs: , a sentence in propositional logic
f¤I$@¥
% ¡ 3
the set of clauses in the CNF representation of
P $2g10
% quot; T 7
a list of the proposition symbols in
return DPLL( , , ) — – t12g$2 §I$¤¥
quot; T 7 ¡ 3
function DPLL( §')oT $2g10 §I$@¥
¡ a quot; quot; T 7 ¡ 3
, , ) returns ¡ 3£
§!© or ¡
¤t$¤G
if every clause in is true in ¡ 3
¤I$@¥
then return ¡ 3£
R§© ¡ a quot;
§')oT
if some clause in is false in ¡ 3
§I$¤¥
then return ¡
¤t$¤G ¡ a quot;
§')oT
® PRt1$d
% ¡ 3 ¤')oT ¤¤41¤¥ t$¦g$0
¡ a quot; ¡ 3 quot; T 7
, F IND -P URE -S YMBOL( , , )
® ® t12g$2 ¤I$@¥
quot; T 7 ¡ 3 ¤)o§Rt$1§X £
¡ a quot; T X ¡ 3 d
if is non-null then return DPLL( , – , E XTEND( )
® PRt1$d
% ¡ 3 §')oT §I$@¥
¡ a quot; ¡ 3
, F IND -U NIT-C LAUSE( , )
® ® t12g$2 ¤I$@¥
quot; T 7 ¡ 3 ¤¡a)quot;oT§X¡Rt$1d§X £
3
if is non-null then return DPLL( , – , E XTEND( )
% ® F IRST( ); % ©¡
R0¤2£ $2g10
R EST( quot; T 7
) quot; T 7
t$¦g$0
return DPLL( , 0§2£ §I$¤¥
© ¡ ¡ 3
, E XTEND( , , )) or §')oT R§© ®
¡ a quot; ¡ 3£
DPLL( , 0§2£ §I$¤¥
© ¡ ¡ 3
, E XTEND( , , )) ¤')oT $¤G ®
¡ a quot; ¡
Figure 7.21
function WALK SAT( , , k ¡ 3
y¯ ioT §I$@¥
) returns a satisfying model or ¡ £ 3
2It6$¤G
inputs: ¤I$@¥
¡ 3
, a set of clauses in propositional logic
, the probability of choosing to do a “random walk” move, typically around 0.5
y¯ ioT
k
, number of flips allowed before giving up
a random assignment of % ¡ a quot;
e¤')oT
/ to the symbols in ¡ ¡ 3£
$¤G R§!© ¡ 3
¤I$@¥
for to k
R y¯ ioT
do z w
§¡I3$¤¥
if satisfies ¡ a quot;
§')oT
then return ¡ a quot;
¤)oT
% ¡ 3
P¤41¤¥
a randomly selected clause from that is false in ¡ 3
¤I$@¥ ¤')oT
¡ a quot;
with probability flip the value in of a randomly selected symbol from ¡ a quot;
§')oT I$@¥
¡ 3
else flip whichever symbol in I$¤¥
¡ 3
maximizes the number of satisfied clauses
return 2It6$¤G
¡ £ 3
Figure 7.23

17. 17
function PL-W UMPUS -AGENT( 02§¢
© ¡¥£¡
) returns an $!¤
# quot; ©¥
inputs: © ¡¥£¡
R§¦¤¢
, a list, [ , £¡©© ¡ °¡¡£ D¥ #¡©
¤¨6 eC i020§ ¤b§0
, ]
static: , a knowledge base, initially containing the “physics” of the wumpus world
E ¢¡
, , quot; £
#$!©$Q©R#§¡Q§1quot; 7 k
, the agent’s position (initially 1,1) and orientation (initially © D C
$@§£ )
2¨606id
a¡©
, an array indicating which squares have been visited, initially $¤G
¡
$!¤
# quot; ©¥
, the agent’s most recent action, initially null
$@
#
, an action sequence, initially empty
update , , a¡© # quot; © © #¡ £
2¨606id $!1¨R§§$quot; 7 k
, based on # quot; © ¥
$!e
if then T ELL(
´ $c± ¢¡
³ ² E, ) else T ELL( , )¤¤2
D¥ #¡© ´ $²“—¥ ¢¡
³ ± E
if then T ELL(
´ $xµ ¢¡
³ ² , E ) else T ELL( , ) i002§
¡ °¡¡£ ´ ³$¢µ—¥ ¢¡
² E
if then '¦$~­$!¤
£ C % # quot; ©¥ ¤!6t$C
£ ¡©©
%#1quot;!©¥
else if is nonempty then P OP( #
$R
) #
$@
else if for some fringe square [ , ], A SK(
¶ , ) is or ‘ ³“ ’ —„¤ ³“ ’ £ ¥ ˆ ¢¡
· ¥ ¡ 3£
R§!©
E
for some fringe square [ , ], A SK(
¶ , ) is ¡t1G
then do ‘ ³“ ’ ·  ³“ ’ £ ˆ ¢¡
¸ E
% #
$R
A*-G RAPH -S EARCH(ROUTE -P ROBLEM([ , ], #$!$Q©R§Q§1quot; 7 k
quot; © #¡ £
, [ , ], ¶ a¡©
¦606id ))
P OP( ) $@
# ­$!e
% # quot; © ¥
else a randomly chosen move $!!
% # quot; © ¥
return $!¤
# quot; ©¥
Figure 7.26

18. 8 FIRST-ORDER LOGIC
18

19. 9 INFERENCE IN
FIRST-ORDER LOGIC
¹ 7 k
function U NIFY( , , ) returns a substitution to make and identical k 7
k
inputs: , a variable, constant, list, or compound
7
, a variable, constant, list, or compound
, the substitution built up so far (optional, defaults to empty)
¹
if = failure then return failure
¹
k
else if = then return 7 ¹
k
else if VARIABLE ?( ) then return U NIFY-VAR( , , ) ¹ 7 k
7
else if VARIABLE ?( ) then return U NIFY-VAR( , , ) ¹ k 7
else if C OMPOUND ?( ) and C OMPOUND ?( ) then k 7
return U NIFY(A RGS[ ], A RGS[ ], U NIFY(O P[ ], O P[ ], ))k 7 k 7 ¹
k
else if L IST ?( ) and L IST ?( ) then 7
k
return U NIFY(R EST[ ], R EST[ ], U NIFY(F IRST[ ], F IRST[ ], )) 7 k 7 ¹
else return failure
function U NIFY-VAR( , , ) returns a substitution ¹ k $1d
£
inputs: $$d
£
, a variable
k
, any expression
, the substitution built up so far
¹
if ¹ U»¦Š d º £
$$I$$1d ‰
then return U NIFY( , , ) ¹ k $$d
¹U»¦Š $14º )‰
else if d ¼ then return U NIFY( , , ) ¹
$1d £$$d
else if O CCUR -C HECK ?( k $$d
£
, ) then return failure
else return add / to ¹ Š k $$d ‰
£
Figure 9.2
19

20. 20 Chapter 9. Inference in First-Order Logic
function FOL-FC-A SK( , ) returns a substitution or E x¡ š ¡
t1G
inputs: , the knowledge base, a set of first-order definite clauses
E ¢¡
, the query, an atomic sentence
š
local variables: ¤b#
u¡
, the new sentences inferred on each iteration
repeat until §‰#
u¡ is empty
Š W‰ —¤b#
% u¡
for each sentence Ex¡ £ in do
%R$S ‘ ½•‘ ¤  `Œ §ˆ
  ¤
S TANDARDIZE -A PART( ) £
¤  ¤ Œ ¹
for each such that S UBST( ,¹ ) = S UBST( , ‘ ¤ |Œ ¹  
| ‘ ¤ ) )
Ex¡ | ‘ sXX |Œ
 for some in
S ¹
S UBST( , ) % | S
| S
if is not a renaming of some sentence already in or E ¢¡ u¡
¤b# then do
add to §‰# | S
u¡
š | S
U NIFY( , ) % ¾
¾ 6$¤G
¾
if is not then return
add to ¢¡
E ¤‰#
u¡
return $¤G
¡
Figure 9.5
function FOL-BC-A SK( , , ) returns a set of substitutions
E ¢¡ quot;
¹ t$)C
inputs: , a knowledge base
E ¢¡
$)'C
quot;
, a list of conjuncts forming a query ( already applied) ¹
, the current substitution, initially the empty substitution
¹ Š W‰
local variables: §¤y0R1
£¡ u #
, a set of substitutions, initially empty
$)C
if quot; is empty then return '1‰
Š ¹
% | S
S UBST( , F IRST( )) ¹ $)C
quot;
for each sentence in where S TANDARDIZE -A PART( ) =
E
¢¡ £ £ €Œ §ˆ
¤ ‘ Àž‘ ¤ )
¿ ½  
% | ¹
and U NIFY( , ) succeeds | S S
% quot; u ¡
ft1)'C ¤b# R EST( ) Á X   
f‘ sX Œ – — $)'C
quot;
% £¡ u #
f§¤y0R1
FOL-BC-A SK( , , C OMPOSE( , )) E ¢¡ $)quot;C §‰#u¡ ¹ | ¹ £¡ u #
§§y0R$¨
return §¤y0¢$
£¡ u #
Figure 9.9
procedure A PPEND( # quot; © 3 # © # quot; ° k
$!$¢R6!R$0¥ i 7 )
, , , )
6$¦©
% £ G LOBAL -T RAIL -P OINTER()
if k ) —–
= and U NIFY( , ) then C ALL( ) ° i 7 # quot; © 3 # © # quot;
$!1R¢6R10¥
R ESET-T RAIL( ) 6$¦©
£
% v N EW-VARIABLE(); N EW-VARIABLE(); N EW-VARIABLE() % Ãk % y°
k i k
if U NIFY( , [ — ]) and U NIFY( , [ — ]) then A PPEND( , , , ° i ° $!!$¢R6¢$0¥ ° 7 k
# quot; © 3 # © # quot; )
Figure 9.12

21. 21
procedure OTTER( , ) ¤'§43 $¤
¡ quot;
inputs: $¤
quot;
, a set of support—clauses defining the problem (a global variable)
¤'§43
¡
, background knowledge potentially relevant to the problem
repeat
%
clause the lightest member of $§
quot;
move from ¡ 3
I$@¥
to ¡
¤¤I3 quot;
$¤
P ROCESS(I NFER( , ), $quot;¤ ¤'§43 I$@¥
) ¡ ¡ 3
until $¤
quot; —–
= or a refutation has been found
function I NFER( (§'¤I3 I$@¥
¡ ¡ 3
, ) returns clauses
resolve I$@¥
¡ 3
with each member of ¤'§43
¡
return the resulting clauses after applying F ILTER
procedure P ROCESS( $¤ ¤I$@¥
quot; ¡ 3
, )
¡ 3
I$@¥
for each in do ¡ 3
¤I$@¥
P¤41¤¥
% ¡ 3
S IMPLIFY( I$@¥
¡ 3
)
merge identical literals
discard clause if it is a tautology
[% quot;
`$§ — I$@¥
¡ 3
] quot;
$§
I$@¥
¡ 3
if has no literals then a refutation has been found
¡I$@¥
if 3 has one literal then look for unit refutation
Figure 9.19

22. 10 KNOWLEDGE
REPRESENTATION
22

23. 23
Figure 11.3
‘ ‘ quot;© X ̈ A
¦'Qy“@¤© ɤ †$¦0b“@¤© ¦¥ E FFECT:
‘ T quot; £ G X ̈ A
‘ quot;©ˆ©£ quot; £ A ‘ T quot; £ Gˆ © £ quot; £ A
¨s¤§$4¨6 Ǥ †$¦§@¤§$4¨6 Ǥ @'b$@ ® ¤ ™120c@¤© A ‘ ̈ ¡ # ‘ T quot; £ G X ̈ P RECOND :
§'Qy€$¦0b“@b7 Å P$!¤¥ A
X ‘ quot;© X T quot; £ G X ̈ ˆ # quot; ©
‘‘ Ì ˆ ¥
¦X … P# Ä „¤ 4PX … ¤© A E FFECT:
‘ † ˆ
‘ †ˆ©£ quot; £ A ‘ ̈ ¡ # ˆ quot; C£ Ê
4¤§$4¨6 Ǥ b@‰$@ ® ¤ ‘ … ¢'$ ˆ¤ I`“@¤© Ǥ bX … P# Ä ‘ † X ̈ A ‘ Ì ˆP RECOND :
§4P“X … “)@Rf$P$!¤¥ A
X ‘ † X Ì ˆ a quot; # Í ˆ # quot; ©
‘‘ Ì ˆ
¦X … P# Ä ¤ 4PX … ¤© ¦¥ E FFECT:
‘ † ˆ A
£ quot; £ A ‘ ̈ ¡ # ˆ quot; C£ Ê
‘4†ˆ¤©§$4¨6 Ǥ @'b$@ ® ¤ ‘ … ¢'$ ˆ¤ 4`“@¤© Ǥ IPX … ¤© A ‘ † X ̈ A ‘ † ˆ P RECOND :
X‘ † X Ì ˆ a ˆ # quot; ©
§4`“bX … “e)quot; F P$!¤¥ A
‘ Æ Å Ž ˆ A ¡ È Œ ˆ ˆ
¦‘ vI8 X v§ ¤© ɤ ‘ PÅ yX v§ ¤© A 1)quot; Ë
‘ Æ Å ˆ ©£ quot; £ A
¦‘ w48 §14¨H ɤ ‘ PÅ ¦¤§$4¨6 ¦¤ ¡ Ȉ©£ quot; £ A
ˆ ¡ # ˆ ¡ # Ž
‘ Ž £ ‰$@ ® ¤ ‘ Œ £ ‰1 ® ¤ ‘ v§ ¢Q1 Ÿ¤ ‘ v§ R$ •¤ˆ quot; C£ Ê Œ ˆ quot; C£ Ê
¡ È ˆ A Æ Å ˆ A ¡ È ˆ A
‘ PÅ yX Ž £ ¤© ɤ ‘ w48 X Œ £ ¤© Ǥ ‘ PÅ yX Ž § ¤© Ǥ ‘ v48 X Œ § ¤© A 6¢# Ä Æ Å ˆ ˆ ©
PLANNING
11

24. Figure 11.7
‘ Æ ˆ £ ¡ Ê
¦‘ ¼ XiÑQˆ`# •¥„¤ ‘ ¼ I$2 Ÿ¤ 1¤ l iQ`# Æ ‘ ¡ X ш E FFECT:
, ‘ ¼ u{Qˆ ¤ ¤Qˆb¤)@ Eɤ ¤ÑQI$2 Ÿ¤ ‘ ¼ iQP# Æ
w Ñ ‘Ñ 9¥ quot; ‘ ˆ £ ¡ Ê X ш P RECOND :
X X ш ¡
§‘ ¼ iQ(§' l quot; l e1ШP$!¤¥ A ¡ d quot; ψ # quot; ©
¥
‘ ˆ £ ¡
¦‘ – I$2 ʕɤ ‘ ¼ iÑQP# •¥„¤ ‘ ¼ ˆ412( ˆ¤ ‘ – iQ`# Æ
X ˆ Æ £ ¡ Ê X ш E FFECT:
,‘ – u w ¼ ˆ ¤ ‘ – Qˆ ¤ ‘ ¼ {Qˆ
uw Ñ uw Ñ
¤ ‘¤Qb9§) Eɤ ‘ – 4$¦( ˆ¤ ‘¤QˆI$2¡ Ÿ¤ ‘ ¼ iQP# Æ
ш ¥ quot; ˆ £ ¡ Ê Ñ £ Ê X ш P RECOND :
X §‘ – X ¼ iQe1ШP$!¤¥ A
X ш ¡ d quot; ψ # quot; ©
‘
¦‘ § X µ `# ˆ¤ ‘ µ P# Æ 1)quot;
ˆ Æ X Έ ˆ
‘ ˆ £ ¡ Ê
¦‘ § 4$¦( ˆ¤ ‘ µ I$2 Ÿ¤ x4$¦( ˆ¤ Ë
ˆ £ ¡ Ê ‘ Έ £ ¡ Ê
‘ § b¤)@ ɤ ‘
ˆ 9¥ quot; E µ ˆ 9¥ quot; E
b¤)@ ɤ xb¤)@ ɤ ‘ Έ 9¥ quot; E
‘ ¡ ˆ Æ ‘ ¡
i(§' l X § P# ˆ¤ i¤' l X ˆ Æ
µ P# ˆ¤ 1¤ `# Æ 6¢# ‘ ¡ l X Έ ˆ © Ä
Figure 11.5
‘ ‘ ¡ X ©
¦i(1k A §1 Å ˆ A ¥
¤© ¦„¤ R412£ §$ Å ¤© ¦Y¤
‘ a # 3 quot; X© ˆ A ¥
‘ 9 # 3 X ¡£ ˆ A ¥ ‘ ¡ X ¡ £
444§£ l 2$4 8 ¤© ¦„¤ i$k A 2$4 8 ¤© ¦„¤ R412£ Ë 2$4 8 ¤© ¦¥
ˆ A ¥ ‘ a # 3 quot; Ë X ¡£ ˆ A E FFECT:
P RECOND :
,
© D C #£¡
'1@R§¤$d Æ $$2¡ F P$!¤¥ A
¡ d ˆ # quot; ©
‘ ¡ X ¡ £ ˆ A ‘ a # 3 quot; X ¡£ ˆ A
¦‘)(1k A e2$4 8 ¤© ɤ R412£ Ë 2$4 8 ¤© ¦¥ E FFECT:
‘ X© ˆ A ¥ ‘ a # 3 quot; X ¡£ ˆ
)¡(1k A §$@ Å ¤© ¦É¤ RbI$¦£ Ë 2$4 8 ¤© A P RECOND :
‘ ¡, X ¡£ ˆ © ˆ # quot; ©
i$k A 2$4 8 P# Æ I3 ® P$!¤¥ A
‘‘ # quot; X© ˆ A ‘ ¡ X © ˆ A
¦¢abI3$¦£ Ë §$@ Å ¤© Ǥ )(1k A §$@ Å ¤© ¦¥ E FFECT:
‘ ¡ X © ˆ
i$k A ¤1 Å ¤© A P RECOND :
,
‘ ¡ X © ˆ ¡ d quot; T ˆ # quot; ©
1$k A §$ Å ee$o§¡ B P$!¤¥ A
a quot; ˆ A ‘ 9 # 3 X ¡£ ˆ A
‘¦‘¢b#I3$¦£ Ë X¡2£$4 8 ¤© Ǥ ¢4I§£ l e2$4 8 ¤© ¦¥ E FFECT:
‘ 9 # 3 X ¡£ ˆ
4¢I§£ l 2$4 8 ¤© A P RECOND :
,
‘ 9 # 3 X ¡£ ˆ ¡ d quot; T ˆ # quot; ©
444§£ l 2$4 8 ee$o§¡ B P$!¤¥ A
‘ ‘ ¡ X ¡ £ ˆ ˆ
¦)(1k A e014 8 ¤© A s1)quot; Ë
‘‘ 9 # 3 X ¡£ ˆ A ‘ ¡ X © ˆ ˆ ©
¦4¢I§£ l 2$4 8 ¤© ɤ i$k A §$@ Å ¤© A 6¢# Ä
Planning 11. Chapter 24