Guide to Intelligent Systems; Second Edition; Michael Negnevitsky

LISP

1. 
   LISP
   

• 
  Second high level language developed. FORTRAN was the first.
  
• 
  Program and data take the same form.
  

Functions:

• 
  (car )
  
  • 
    (car (1 2 3)) => 1
    
• 
  (cdr )
  
  • 
    (cdr (1 2 3)) => (2 3)
    
  • 
    (cdr (1)) => NIL
    
• 
  (list )
  
  • 
    (setf fullhouse (list 2 2 3 3 3)))
    
• 
  (setf )
  
  • 
    (setf eat 8)
    
  • 
    (setf eatthis (list pie cake))
    
• 
  (length )
  
  • 
    (length fullhouse) => 5
    

• 
  (defun (exp1) (exp2) (exp3) ….(expN) )
  
• 
  (cond ( (condition 1) (exp1)..(expN)) ( (condition 2) (exp1)..(expN)) (t (exp1)..(expN)) )
  
• 
  (append (list1) (list2))
  
  • 
    (append (list 1 2 3) (list 4 5)) => (1 2 3 4 5)
    
• 
  (cons (list1) (list2))
  
  • 
    (cons (list 1 2 3) (list 4 5)) => ((1 2 3) 4 5)
    
• 
  (eval (list))
  
  • 
    (eval (car (1 2 3)) => 1
    
• 
  (mapcar ….)
  
  • 
    (mapcar 'car '((1 a) (2 b) (3 c))) => (1 2 3)
    
  • 
    (mapcar 'abs '(3 -4 2 -5 -6)) => (3 4 2 5 6)
    
  • 
    (mapcar 'cons '(a b c) '(1 2 3)) => ((A . 1) (B . 2) (C . 3))
    

Examples:

(setf big (list 1 2 3 4 5 6 7 8 9 10))

(setf fullhouse (list 2 2 2 3 3))

)
(defun lastone (list)

(cond

(( = (length list) 1)

)

( t

(lastone (cdr list))

)

)

)

(cond

(car addlist)

)

(t

(+ (car addlist)(sumList(cdr addlist)))

)

)

(/ (sumList list) (length list))

)
(defun middleNum (nums)

(setf mid (/ (length nums) 2))

(getNum mid nums)

)
(defun getNum (mid nums)

(cond

((= 1 mid)

)

(t

)

)

(append (list 'Yo 'Mamma) message)

)
Link to LISP Help

http://www.lisp.org/HyperSpec/FrontMatter/Chapter-Index.html

; Homework assigment 2

; Lisp review

; Fall 2007

(cond

rlist

( t

)

)

; (2 3)

; (append (3) (list 2)) => (3 2)

; (append (3 2) (list 1)) = > (3 2 1)
(defun countMe (rlist)

(cond

1

)

(+ 1 (countMe (cdr rlist)))

)

)

)

(cond

num

(t

(* (fact (- num 1)) num)

)

)

(setf comList (append (list op) nums))

(print comList)

(eval comList)

)

0. Classic AI Systems (Rule-Based Systems)

While important to our study, we don’t want to spend the whole semester on this topic. We will concentrate on rule-based reasoning and do some LISP.

2. 
   Knowledge (page 25)
   
   • 
     What is it?
     
   • 
     Where does it come from
     
   • 
     How do we represent it?
     
     • 
       Production rules (if – then statements)
       
3. 
   Structure of AI program (page 31)
   

The production rules are rules of thumb that we obtained through research; reading books and papers; experimentation; asking experts what they would do in a variety of situations.

• 
  Inference engine – matches facts in the short-term memory against the rules in the long-term memory.
  
• 
  User interface – communicates to the user. Typically its job would be collect facts from the user and store them in the short-term memory. When it draws conclusions from its rules, these may also be stored as facts. If the inference engine comes to impasse before drawing a conclusion, it may direct the user interface to ask the user for more information so it can continue.
  
• 
  Explanation facility – tracks the rule/fact matching process so that the system can show its line of reasoning.
  

Other modules may include:

• 
  External databases
  
• 
  Models/programs
  
• 
  Developer interface
  

The key to the functionality of the system is the inference engine. The diagram below shows the inference engine operational procedure.

4. 
   Forward chaining – The accumulation of facts to support a conclusion. We gather information and then infer whatever we can from it. Data driven reasoning.
   

Database Facts:

If (y == TRUE) && (d == TRUE) {

Z = TRUE;

}
/* Rule 2. */

If (x == TRUE) && (b == TRUE) && (e == TRUE) {

Y = TRUE;

}
/* Rule 3. */

If (a == TRUE) {

X = TRUE;

}
/* Rule 4. */

If (c == TRUE) {

L = TRUE;

}
/* Rule 5. */

If (l == TRUE) && (M == TRUE) {

N = TRUE;

}
Step 1:

Facts = {a, b, c, d, e}

Rule 3 fires => Facts = {a, b, c, d, e, x}

Facts = {a, b, c, d, e, x}

Rule 4 fires => Facts = {a, b, c, d, e, x, l}
Step 3:

Facts = {a, b, c, d, e, x, l}

Rule 2 fires => Facts = {a, b, c, d, e, x, l, y}
Step 4:

Facts = {a, b, c, d, e, x, l, y}

Rule 1 fires => Facts = {a, b, c, d, e, x, l, y, z}

• 
  Here our rule search pattern always picked up from the last rule that fired and continued down the list.
  

• 
  When hit the bottom of the list we started at the top again.
  

• 
  What would happen if we always started at rule 1 and searched downward.
  

• 
  Would the results be different?
  
• 
  Would the path of reasoning be different?
  
• 
  Does it matter?
  

2. 
   Backward chaining – Given a likely conclusion, can we find the facts to support it. Goal driven reasoning.
   

Database Facts:

If (y == TRUE) && (d == TRUE) {

Z = TRUE;

}
/* Rule 2. */

If (x == TRUE) && (b == TRUE) && (e == TRUE) {

Y = TRUE;

}
/* Rule 3. */

If (a == TRUE) {

X = TRUE;

}
/* Rule 4. */

If (c == TRUE) {

L = TRUE;

}
/* Rule 5. */

If (l == TRUE) && (M == TRUE) {

N = TRUE;

}
Step 1:

Facts = {a, b, c, d, e}

Goal = z; Rule 1 => z ; sub-goal required => y

Facts = {a, b, c, d, e}

Goal = y; Rule 2 => y ; sub-goals required => x, b, e; b and e exist, x is required
Step 3:

Facts = {a, b, c, d, e}

Goal = x; Rule 3 => x ; sub-goals required => none
Now we start forward chaining process to confirm that the goal z is supportable with the existing fact set.
Step 4:

Facts = {a, b, c, d, e}

Rule 3 fires => Facts = {a, b, c, d, e, x}
Step 5:

Facts = {a, b, c, d, e, x}

Rule 2 fires => Facts = {a, b, c, d, e, x, y}
Step 6:

Facts = {a, b, c, d, e, x, y}

Rule 1 fires => Facts = {a, b, c, d, e, x, y, z}

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