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	<html>
	<head>
	<title>LISP Tutorial Lecture 3: Data Abstraction</title>
	</head>
	<body bgcolor=ffffff>
	<h1>LISP Tutorial Lecture 3: Data Abstraction</h1>

	<h2>Binary Trees</h2>

	<p>Suppose we want to create a new kind
	of <em>recursive data type</em>, our familiar binary trees.
	The first thing we have to do is to define the data type in terms of
	its <em>constructors</em>, <em>selectors</em> and <em>recognizers</em>.
	In the case of binary trees, we have the following:
	<ol>
	<li><em>Constructors</em>: We have two kinds of binary trees, <em>leaves</em>
	and <em>nodes</em>. Accordingly, we need a constructor for each
	kind:
	<ul>
	<li><tt>(make-bin-tree-leaf <em>E</em>)</tt>: A leaf is a composite
	object with one component, the <em>element</em> <em>E</em>.
	<li><tt>(make-bin-tree-node <em>E</em>
	<em>B1</em> <em>B2</em>)</tt>: A node consists of three components,
	an element <em>E</em>, a <em>left subtree</em> <em>B1</em> and
	a <em>right subtree</em> <em>B2</em>. Each of <em>B1</em>
	and <em>B2</em> is a binary tree.
	</ul>
	Notice that the definition of binary tree is inherently recursive
	(as in the case of nodes).
	Larger binary trees can be composed from smaller ones.
	<li><em>Selectors</em>: We need to define a selector for each
	component of each kind of binary tree.
	<ul>
	<li><tt>(bin-tree-leaf-element <em>L</em>)</tt>: Retrieve the element
	of a leaf <em>L</em>.
	<li><tt>(bin-tree-node-element <em>N</em>)</tt>: Retrieve the element
	of a node <em>N</em>.
	<li><tt>(bin-tree-node-left <em>N</em>)</tt>: Retrieve the
	left subtree of a node <em>N</em>.
	<li><tt>(bin-tree-node-right <em>N</em>)</tt>: Retrieve the right
	subtree of a node <em>N</em>.
	</ul>
	<li><em>Recognizers</em>: We define one recognizer for each kind
	of binary tree.
	<ul>
	<li><tt>(bin-tree-leaf-p <em>B</em>)</tt>: Test if a given binary tree
	<em>B</em> is a leaf.
	<li><tt>(bin-tree-node-p <em>B</em>)</tt>: Test if a given binary tree
	<em>B</em> is a node.
	</ul>
	</ol>

	<p>Notice that we have not written a line of code yet, and still we
	are able to write down the function signature of all the constructors,
	selectors and recognizers. The process is more or less mechanical:
	<ol>
	<li>Define a constructor for each variant of the recursive data type.
	The parameters for a constructor defines the components of
	a composite object.
	<li>For each parameter of each constructor, define a selector to
	retrieve the corresponding component.
	<li>For each constructor, define a corresponding recognizer.
	</ol>

	<p>The next question is how we are to <em>represent</em> a binary tree
	as a LISP object. Of course, a list is the first thing that comes to
	our mind:
	<ul>
	<li>We represent an leaf with element <em>E</em> by a singleton list
	containing <em>E</em> (i.e. <tt>(list <em>E</em>)</tt>).
	<li>A node with element <em>E</em>, left subtree <em>B1</em>
	and right subtree <em>B2</em> is represented as a list containing
	the three components (i.e.
	<tt>(list <em>E</em> <em>B1</em> <em>B2</em>)</tt>).
	</ul>
	Fixing the representation, we can thus implement the recursive
	data type functions:
	<pre>
	;;
	;; Binary Trees
	;;

	;;
	;; Constructors for binary trees
	;;

	(defun make-bin-tree-leaf (E)
	"Create a leaf."
	(list E))

	(defun make-bin-tree-node (E B1 B2)
	"Create a node with element K, left subtree B1 and right subtree B2."
	(list E B1 B2))

	;;
	;; Selectors for binary trees
	;;

	(defun bin-tree-leaf-element (L)
	"Retrieve the element of a leaf L."
	(first L))

	(defun bin-tree-node-element (N)
	"Retrieve the element of a node N."
	(first N))

	(defun bin-tree-node-left (N)
	"Retrieve the left subtree of a node N."
	(second N))

	(defun bin-tree-node-right (N)
	"Retrieve the right subtree of a node N."
	(third N))

	;;
	;; Recognizers for binary trees
	;;

	(defun bin-tree-leaf-p (B)
	"Test if binary tree B is a leaf."
	(and (listp B) (= (list-length B) 1)))

	(defun bin-tree-node-p (B)
	"Test if binary tree B is a node."
	(and (listp B) (= (list-length B) 3)))
	</pre>

	<p>The representation scheme works out like the following:
	<pre>
	USER(5): (make-bin-tree-node '*
	(make-bin-tree-node '+
	(make-bin-tree-leaf 2)
	(make-bin-tree-leaf 3))
	(make-bin-tree-node '-
	(make-bin-tree-leaf 7)
	(make-bin-tree-leaf 8)))
	(* (+ (2) (3)) (- (7) (8)))
	</pre>
	The expression above is a binary tree node with element <tt>*</tt> and
	two subtrees. The left subtree is itself a binary tree node with
	<tt>+</tt> as its element and leaves as its subtress. The right
	subtree is also a binary tree node with <tt>-</tt> as its element
	and leaves as its subtrees. All the leaves are decorated by
	numeric components.
	<pre>
	*
	/ \
	/ \
	/ \
	+ -
	/ \ / \
	2 3 7 8
	</pre>

	<h2>Searching Binary Trees</h2>

	<p>As discussed in previous tutorials, having recursive data structures
	defined in the way we did streamlines the process of formulating
	structural recursions. We review this concept in the following examples.

	<p>Suppose we treat binary trees as containers. An
	expression <em>E</em> is a member of a binary tree <em>B</em> if:
	<ol>
	<li><em>B</em> is a leaf and its element is <em>E</em>.
	<li><em>B</em> is a node and either its element is <em>E</em> or
	<em>E</em> is a member of one of its subtrees.
	</ol>
	For example, the definition asserts that the members of
	<tt>(* (+ (2) (3)) (- (7) (8)))</tt> are <tt>*</tt>, <tt>+</tt>,
	<tt>2</tt>, <tt>3</tt>, <tt>-</tt>, <tt>7</tt> and <tt>8</tt>.
	Such a definition can be directly implemented by our recursive data
	type functions:
	<pre>
	(defun bin-tree-member-p (B E)
	"Test if E is an element in binary tree B."
	(if (bin-tree-leaf-p B)
	(equal E (bin-tree-leaf-element B))
	(or (equal E (bin-tree-node-element B))
	(bin-tree-member-p (bin-tree-node-left B) E)
	(bin-tree-member-p (bin-tree-node-right B) E))))
	</pre>
	The function can be made more readable by using the <tt>let</tt> form:
	<pre>
	(defun bin-tree-member-p (B E)
	"Test if E is an element in binary tree B."
	(if (bin-tree-leaf-p B)
	(equal E (bin-tree-leaf-element B))
	(let
	((elmt (bin-tree-node-element B))
	(left (bin-tree-node-left B))
	(right (bin-tree-node-right B)))
	(or (equal E elmt)
	(bin-tree-member-p left E)
	(bin-tree-member-p right E)))))
	</pre>
	<p>Tracing the execution of <tt>bin-tree-member-p</tt>, we get:
	<pre>
	USER(14): (trace bin-tree-member-p)
	(BIN-TREE-MEMBER-P)
	USER(15): (bin-tree-member-p '(+ (* (2) (3)) (- (7) (8))) 7)
	0: (BIN-TREE-MEMBER-P (+ (* (2) (3)) (- (7) (8))) 7)
	1: (BIN-TREE-MEMBER-P (* (2) (3)) 7)
	2: (BIN-TREE-MEMBER-P (2) 7)
	2: returned NIL
	2: (BIN-TREE-MEMBER-P (3) 7)
	2: returned NIL
	1: returned NIL
	1: (BIN-TREE-MEMBER-P (- (7) (8)) 7)
	2: (BIN-TREE-MEMBER-P (7) 7)
	2: returned T
	1: returned T
	0: returned T
	T
	</pre>

	<p><hr><b>Exercise:</b>
	Let <em>size(B)</em> be the number of members in a binary tree <em>B</em>.
	Give a recursive definition of <em>size(B)</em>, and then implement a
	LISP function <tt>(bin-tree-size <em>B</em>)</tt> that
	returns <em>size(B)</em>.
	<hr>



	<h2>Traversing Binary Trees</h2>

	<p>
	Let us write a function
	that will <em>reverse</em> a tree in the sense that the left
	and right subtrees of every node are swapped:
	<pre>
	(defun binary-tree-reverse (B)
	"Reverse binary tree B."
	(if (bin-tree-leaf-p B)
	B
	(let
	((elmt (bin-tree-node-element B))
	(left (bin-tree-node-left B))
	(right (bin-tree-node-right B)))
	(make-bin-tree-node elmt
	(binary-tree-reverse right)
	(binary-tree-reverse left)))))
	</pre>
	<p>The correctness of the above implementation can be articulated
	as follows. Given a binary tree <em>B</em> and an object <em>E</em>,
	either the binary tree is a leaf or it is a node:
	<ul>
	<li><em>Case 1:</em> <em>B</em> is a leaf.<br>
	Then the reversal of <em>B</em> is simply <em>B</em> itself.
	<li><em>Case 2:</em> <em>B</em> is a node.<br>
	Then <em>B</em> has three components, namely, an element <tt>elmt</tt>,
	a left subtree <tt>left</tt> and a right subtree <tt>right</tt>.
	The reversal of <em>B</em> is a node with element <tt>elmt</tt>,
	left subtree the reversal of <tt>right</tt>, and right subtree
	the reversal of <tt>left</tt>.
	</ul>
	<p>The following shows us how the recursion unfolds:
	<pre>
	USER(21): (trace bin-tree-reverse)
	(BIN-TREE-REVERSE)
	USER(22): (bin-tree-reverse '(* (+ (2) (3)) (- (7) (8))))
	0: (BIN-TREE-REVERSE (* (+ (2) (3)) (- (7) (8))))
	1: (BIN-TREE-REVERSE (- (7) (8)))
	2: (BIN-TREE-REVERSE (8))
	2: returned (8)
	2: (BIN-TREE-REVERSE (7))
	2: returned (7)
	1: returned (- (8) (7))
	1: (BIN-TREE-REVERSE (+ (2) (3)))
	2: (BIN-TREE-REVERSE (3))
	2: returned (3)
	2: (BIN-TREE-REVERSE (2))
	2: returned (2)
	1: returned (+ (3) (2))
	0: returned (* (- (8) (7)) (+ (3) (2)))
	(* (- (8) (7)) (+ (3) (2)))
	</pre>
	The resulting expression represents the following tree:
	<pre>
	*
	/ \
	/ \
	/ \
	- +
	/ \ / \
	8 7 3 2
	</pre>

	<p>Let us implement a function that will extract the members of a given binary
	tree, and put them into a list in preorder.
	<pre>
	(defun bin-tree-preorder (B)
	"Create a list containing keys of B in preorder."
	(if (bin-tree-leaf-p B)
	(list (bin-tree-leaf-element B))
	(let
	((elmt (bin-tree-node-element B))
	(left (bin-tree-node-left B))
	(right (bin-tree-node-right B)))
	(cons elmt
	(append (bin-tree-preorder left)
	(bin-tree-preorder right))))))
	</pre>
	Tracing the execution of the function, we obtain the following:
	<pre>
	USER(13): (trace bin-tree-preorder)
	(BIN-TREE-PREORDER)
	USER(14): (bin-tree-preorder '(* (+ (2) (3)) (- (7) (8))))
	0: (BIN-TREE-PREORDER (* (+ (2) (3)) (- (7) (8))))
	1: (BIN-TREE-PREORDER (+ (2) (3)))
	2: (BIN-TREE-PREORDER (2))
	2: returned (2)
	2: (BIN-TREE-PREORDER (3))
	2: returned (3)
	1: returned (+ 2 3)
	1: (BIN-TREE-PREORDER (- (7) (8)))
	2: (BIN-TREE-PREORDER (7))
	2: returned (7)
	2: (BIN-TREE-PREORDER (8))
	2: returned (8)
	1: returned (- 7 8)
	0: returned (* + 2 3 - 7 8)
	(* + 2 3 - 7 8)
	</pre>

	<p>As we have discussed before, the <tt>append</tt> call in the code above
	is a source of inefficiency that can be obtimized away:
	<pre>
	(defun fast-bin-tree-preorder (B)
	"A tail-recursive version of bin-tree-preorder."
	(preorder-aux B nil))

	(defun preorder-aux (B A)
	"Append A to the end of the list containing elements of B in preorder."
	(if (bin-tree-leaf-p B)
	(cons (bin-tree-leaf-element B) A)
	(let
	((elmt (bin-tree-node-element B))
	(left (bin-tree-node-left B))
	(right (bin-tree-node-right B)))
	(cons elmt
	(preorder-aux left
	(preorder-aux right A))))))
	</pre>
	An execution trace of the implementation is the following:
	<pre>
	USER(15): (trace fast-bin-tree-preorder preorder-aux)
	(PREORDER-AUX FAST-BIN-TREE-PREORDER)
	USER(16): (fast-bin-tree-preorder '(* (+ (2) (3)) (- (7) (8))))
	0: (FAST-BIN-TREE-PREORDER (* (+ (2) (3)) (- (7) (8))))
	1: (PREORDER-AUX (* (+ (2) (3)) (- (7) (8))) NIL)
	2: (PREORDER-AUX (- (7) (8)) NIL)
	3: (PREORDER-AUX (8) NIL)
	3: returned (8)
	3: (PREORDER-AUX (7) (8))
	3: returned (7 8)
	2: returned (- 7 8)
	2: (PREORDER-AUX (+ (2) (3)) (- 7 8))
	3: (PREORDER-AUX (3) (- 7 8))
	3: returned (3 - 7 8)
	3: (PREORDER-AUX (2) (3 - 7 8))
	3: returned (2 3 - 7 8)
	2: returned (+ 2 3 - 7 8)
	1: returned (* + 2 3 - 7 8)
	0: returned (* + 2 3 - 7 8)
	(* + 2 3 - 7 8)
	</pre>

	<p><b>Exercise</b>: Implement a function that will create a list
	containing members of a given binary tree in postorder. Implement
	also a tail-recursive version of the same function.

	<p><b>Exercise</b>: Repeat the last exercise with inorder.

	<h2>Abstract Data Types</h2>

	<p><em>Abstract data types</em> are blackboxes. They are defined
	in terms of their external interfaces, and not their implementation.
	For example, a <em>set</em> abstraction offers the following
	operations:
	<ul>
	<li><tt>(make-empty-set)</tt> creates an empty set.
	<li><tt>(set-insert <em>S</em> <em>E</em>)</tt>
	returns a set containing all members
	of set <em>S</em> plus an additional member <em>E</em>.
	<li><tt>(set-remove <em>S</em> <em>E</em>)</tt>
	returns a set containing all members of set <em>S</em>
	except for <em>E</em>.
	<li><tt>(set-member-p <em>S</em> <em>E</em>)</tt>
	returns true if <em>E</em> is a member of set <em>S</em>.
	<li><tt>(set-empty-p <em>S</em>)</tt> returns true if set <em>S</em>
	is empty.
	</ul>

	<p>To implement an abstract data type, we need to decide on
	a representation. Let us represent a set by a list with
	no repeated members.
	<pre>
	(defun make-empty-set ()
	"Creates an empty set."
	nil)

	(defun set-insert (S E)
	"Return a set containing all the members of set S plus the element E."
	(adjoin E S :test #'equal))

	(defun set-remove (S E)
	"Return a set containing all the members of set S except for element E."
	(remove E S :test #'equal))

	(defun set-member-p (S E)
	"Return non-NIL if set S contains element E."
	(member E S :test #'equal))

	(defun set-empty-p (S)
	"Return true if set S is empty."
	(null S))
	</pre>

	<p><hr><b>Exercise:</b> Look up the definition of <tt>adjoin</tt>,
	<tt>remove</tt> and <tt>member</tt> from CLTL2. In particular,
	find out how the <tt>:test</tt> keyword is used to specify
	the equality test function to be used by the three functions.
	What will happen if we omit the <tt>:test</tt> keyword and
	the subsequent <tt>#'equal</tt> when invoking the three
	functions?
	<hr>

	<p>Notice that we have implemented an abstract data type (sets)
	using a more fundamental recursive data structure (lists) with additional
	computational constraints (no repetition) imposed by the interface
	functions.

	<h2>Binary Search Trees</h2>

	<p>Another way of implementing the same set abstraction is to use
	the more efficient <em>binary search tree (BST)</em>.
	Binary search trees are basically binary trees with the following
	additional computational constraints:
	<ul>
	<li>All the members in the left subtree of a tree node
	is no greater than the element of the node.
	<li>All the members in the right subtree of a tree node
	is greater than the element of the node.
	<li>All the leaf members are distinct.
	</ul>
	Again, we are implementing an abstract data type (sets) by a more
	fundamental recursive data structure (binary trees) with additional
	computational constraints. In particular, we use the leaves of
	a binary tree to store the member of a set, and the tree nodes
	for providing indexing information that improves search performance.
	for example, a BST representing the set {1 2 3 4} would look like:
	<pre>
	2
	/ \
	/ \
	/ \
	1 3
	/ \ / \
	1 2 3 4
	</pre>

	<p> An
	empty BST is represented by <tt>NIL</tt>, while a nonempty BST is
	represented by a binary tree.
	We begin with the constructor and recognizer for empty BST.
	<pre>
	(defun make-empty-BST ()
	"Create an empty BST."
	nil)

	(defun BST-empty-p (B)
	"Check if BST B is empty."
	(null B))
	</pre>

	<p>Given the additional computational constraints, membership test
	can be implemented as follows:
	<pre>
	(defun BST-member-p (B E)
	"Check if E is a member of BST B."
	(if (BST-empty-p B)
	nil
	(BST-nonempty-member-p B E)))

	(defun BST-nonempty-member-p (B E)
	"Check if E is a member of nonempty BST B."
	(if (bin-tree-leaf-p B)
	(= E (bin-tree-leaf-element B))
	(if (<= E (bin-tree-node-element B))
	(BST-nonempty-member-p (bin-tree-node-left B) E)
	(BST-nonempty-member-p (bin-tree-node-right B) E))))
	</pre>
	Notice that we handle the degenerate case of searching an empty BST
	separately, and apply the well-known recursive search algorithm
	only on nonempty BST.
	<pre>
	USER(16): (trace BST-member-p BST-nonempty-member-p)
	(BST-NONEMPTY-MEMBER-P BST-MEMBER-P)
	USER(17): (BST-member-p '(2 (1 (1) (2)) (3 (3) (4))) 3)
	0: (BST-MEMBER-P (2 (1 (1) (2)) (3 (3) (4))) 3)
	1: (BST-NONEMPTY-MEMBER-P (2 (1 (1) (2)) (3 (3) (4))) 3)
	2: (BST-NONEMPTY-MEMBER-P (3 (3) (4)) 3)
	3: (BST-NONEMPTY-MEMBER-P (3) 3)
	3: returned T
	2: returned T
	1: returned T
	0: returned T
	T
	</pre>

	<p>Insertion is handled by the following family of functions:
	<pre>
	(defun BST-insert (B E)
	"Insert E into BST B."
	(if (BST-empty-p B)
	(make-bin-tree-leaf E)
	(BST-nonempty-insert B E)))

	(defun BST-nonempty-insert (B E)
	"Insert E into nonempty BST B."
	(if (bin-tree-leaf-p B)
	(BST-leaf-insert B E)
	(let ((elmt (bin-tree-node-element B))
	(left (bin-tree-node-left B))
	(right (bin-tree-node-right B)))
	(if (<= E (bin-tree-node-element B))
	(make-bin-tree-node elmt
	(BST-nonempty-insert (bin-tree-node-left B) E)
	right)
	(make-bin-tree-node elmt
	left
	(BST-nonempty-insert (bin-tree-node-right B) E))))))

	(defun BST-leaf-insert (L E)
	"Insert element E to a BST with only one leaf."
	(let ((elmt (bin-tree-leaf-element L)))
	(if (= E elmt)
	L
	(if (< E elmt)
	(make-bin-tree-node E
	(make-bin-tree-leaf E)
	(make-bin-tree-leaf elmt))
	(make-bin-tree-node elmt
	(make-bin-tree-leaf elmt)
	(make-bin-tree-leaf E))))))
	</pre>
	As before, recursive insertion to nonempty BST is handled
	outside of the general entry point of BST insertion. Traversing
	down the index nodes, the recursive algorithm eventually arrives
	at a leaf. In case the element is not already in the tree, the
	leaf is turned into a node with leaf subtrees holding the inserted
	element and the element of the original leaf. For example, if
	we insert 2.5 into the tree represented by
	<tt>(2 (1 (1) (2)) (3 (3) (4)))</tt>,
	the effect is the following:
	<pre>
	2 2
	/ \ / \
	/ \ / \
	/ \ ==> / \
	1 3 1 3
	/ \ / \ / \ / \
	1 2 3 4 1 2 2.5 4
	/ \
	2.5 3
	</pre>
	A trace of the insertion operation is given below:
	<pre>
	USER(22): (trace BST-insert BST-nonempty-insert BST-leaf-insert)
	(BST-LEAF-INSERT BST-NONEMPTY-INSERT BST-INSERT)
	USER(23): (BST-insert '(2 (1 (1) (2)) (3 (3) (4))) 2.5)
	0: (BST-INSERT (2 (1 (1) (2)) (3 (3) (4))) 2.5)
	1: (BST-NONEMPTY-INSERT (2 (1 (1) (2)) (3 (3) (4))) 2.5)
	2: (BST-NONEMPTY-INSERT (3 (3) (4)) 2.5)
	3: (BST-NONEMPTY-INSERT (3) 2.5)
	4: (BST-LEAF-INSERT (3) 2.5)
	4: returned (2.5 (2.5) (3))
	3: returned (2.5 (2.5) (3))
	2: returned (3 (2.5 (2.5) (3)) (4))
	1: returned (2 (1 (1) (2)) (3 (2.5 (2.5) (3)) (4)))
	0: returned (2 (1 (1) (2)) (3 (2.5 (2.5) (3)) (4)))
	(2 (1 (1) (2)) (3 (2.5 (2.5) (3)) (4)))
	</pre>

	<p>Removal of elements is handled by the following family of functions:
	<pre>
	(defun BST-remove (B E)
	"Remove E from BST B."
	(if (BST-empty-p B)
	B
	(if (bin-tree-leaf-p B)
	(BST-leaf-remove B E)
	(BST-node-remove B E))))

	(defun BST-leaf-remove (L E)
	"Remove E from BST leaf L."
	(if (= E (bin-tree-leaf-element L))
	(make-empty-BST)
	L))

	(defun BST-node-remove (N E)
	"Remove E from BST node N."
	(let
	((elmt (bin-tree-node-element N))
	(left (bin-tree-node-left N))
	(right (bin-tree-node-right N)))
	(if (<= E elmt)
	(if (bin-tree-leaf-p left)
	(if (= E (bin-tree-leaf-element left))
	right
	N)
	(make-bin-tree-node elmt (BST-node-remove left E) right))
	(if (bin-tree-leaf-p right)
	(if (= E (bin-tree-leaf-element right))
	left
	N)
	(make-bin-tree-node elmt left (BST-node-remove right E))))))
	</pre>
	This time, removal from empty BST's and BST's with a single leaf are
	both degenerate cases. The recursive removal algorithm deals with
	BST nodes. Traversing down the index nodes, the recursive algorithm
	searches for the parent node of the leaf to be removed. In case it is
	found, the sibling of the leaf to be removed replaces its parent
	node. For example, the effect of removing 2 from the BST represented
	by <tt>(2 (1 (1) (2)) (3 (3) (4)))</tt> is depicted as follows:
	<pre>
	2 2
	/ \ / \
	/ \ / \
	/ \ ==> / \
	1 3 1 4
	/ \ / \ / \
	1 2 3 4 1 2
	</pre>
	A trace of the deletion operation is given below:
	<pre>
	USER(4): (trace BST-remove BST-node-remove)
	(BST-NODE-REMOVE BST-REMOVE)
	USER(5): (BST-remove '(2 (1 (1) (2)) (3 (3) (4))) 3)
	0: (BST-REMOVE (2 (1 (1) (2)) (3 (3) (4))) 3)
	1: (BST-NODE-REMOVE (2 (1 (1) (2)) (3 (3) (4))) 3)
	2: (BST-NODE-REMOVE (3 (3) (4)) 3)
	2: returned (4)
	1: returned (2 (1 (1) (2)) (4))
	0: returned (2 (1 (1) (2)) (4))
	(2 (1 (1) (2)) (4))
	</pre>

	<p><hr><b>Exercise:</b> A set can be implemented as a <em>sorted list</em>,
	which is a list storing distinct members in ascending order. Implement
	the sorted list abstraction.
	<hr>

	<h2>Polynomials</h2>

	<p>We demonstrate how one can perform symbolic computation using
	LISP. To begin with, we define a new recursive data type for
	<em>polynomials</em>, which is defined recursively as follows:
	<ul>
	<li>If <em>num</em> is a number, then
	<tt>(make-constant <em>num</em>)</tt> is a polynomial;
	<li>If <em>sym</em> is a symbol, then <tt>(make-variable <em>sym</em>)</tt>
	is a polynomial;
	<li>If <em>poly1</em> and <em>poly2</em> are polynomials, then
	the following are also polynomials:
	<ul>
	<li><tt>(make-sum <em>poly1</em> <em>poly2</em>)</tt>
	<li><tt>(make-product <em>poly1</em> <em>poly2</em>)</tt>
	</ul>
	<li>If <em>poly</em> is a polynomial and <em>num</em> is a number,
	then <tt>(make-power <em>poly</em> <em>num</em>)</tt> is a polynomial.
	</ul>
	One can represent polynomials in the most standard way:
	<pre>
	;;
	;; Constructors for polynomials
	;;

	(defun make-constant (num)
	num)

	(defun make-variable (sym)
	sym)

	(defun make-sum (poly1 poly2)
	(list '+ poly1 poly2))

	(defun make-product (poly1 poly2)
	(list '* poly1 poly2))

	(defun make-power (poly num)
	(list '** poly num))
	</pre>
	For example, <tt>(make-power (make-sum (make-variable 'x) (make-constant 1)) 2)</tt> is represented by the LISP form <tt>(** (+ x 1) 2)</tt>, which
	denotes the polynomail <em>(x + 1)<sup>2</sup></em> in our usual
	notation.

	<p>We then define a recognizer for each constructor:
	<pre>
	;;
	;; Recognizers for polynomials
	;;

	(defun constant-p (poly)
	(numberp poly))

	(defun variable-p (poly)
	(symbolp poly))

	(defun sum-p (poly)
	(and (listp poly) (eq (first poly) '+)))

	(defun product-p (poly)
	(and (listp poly) (eq (first poly) '*)))

	(defun power-p (poly)
	(and (listp poly) (eq (first poly) '**)))
	</pre>

	<p>We then need to define selectors for the composite polynomials.
	We define a selector for each component of each composite
	constructor.
	<pre>
	;;
	;; Selectors for polynomials
	;;

	(defun constant-numeric (const)
	const)

	(defun variable-symbol (var)
	var)

	(defun sum-arg1 (sum)
	(second sum))

	(defun sum-arg2 (sum)
	(third sum))

	(defun product-arg1 (prod)
	(second prod))

	(defun product-arg2 (prod)
	(third prod))

	(defun power-base (pow)
	(second pow))

	(defun power-exponent (pow)
	(third pow))
	</pre>
	One may ask why we define so many trivial looking functions for
	carrying out the same task (<tt>sum-arg1</tt> and <tt>product-arg1</tt>
	have exactly the same implementation). The reason is that we
	may end up changing the representation in the future, and there is
	no guarantee that sums and products will be represented similarly
	in the future. Also, programs written like this tends to be
	self-commenting.

	<p>Now that we have a completely defined polynomial data type, let us
	do something interesting with it. Let us define a function that
	carries out symbolic differentiation. In particular, we want a function
	<tt>(d <em>poly</em> <em>x</em>)</tt> which returns the derivative
	of polynomial <em>poly</em> with respect to variable <em>x</em>.
	Let us review our first-year differential calculus:
	<ul>
	<li>The derivative <em>(dC / dx)</em> of a constant <em>C</em>
	is zero.
	<li>The derivative <em>(dy/dx)</em> of a variable <em>y</em>
	is 1 if the <em>x = y</em>. Otherwise, we leave
	the derivative unevaluated. We represent unevaluated
	derivatives using the following functions
	<pre>
	;;
	;; Unevaluated derivative
	;;

	(defun make-derivative (poly x)
	(list 'd poly x))

	(defun derivative-p (poly x)
	(and (listp poly) (eq (first poly) 'd)))
	</pre>
	<li>The derivative <em>(d(F+G)/dx)</em>
	of a sum <em>(F+G)</em> is <em>(dF/dx) + (dG/dx)</em>.
	<li>The derivative <em>(d(FG)/dx)</em> of a product <em>(FG)</em>
	is <em>F(dG/dx) + G(dF/dx)</em>.
	<li>The derivative <em>(d(F<sup>N</sup>)/dx)</tt>
	of a power <em>F<sup>N</sup></em> is
	<em>N * F<sup>N-1</sup> * (dF/dx)</em>.
	</ul>

	<p>The above calculus can be encoded in LISP as follows:
	<pre>
	;;
	;; Differentiation function
	;;

	(defun d (poly x)
	(cond
	((constant-p poly) 0)
	((variable-p poly)
	(if (equal poly x)
	1
	(make-derivative poly x)))
	((sum-p poly)
	(make-sum (d (sum-arg1 poly) x)
	(d (sum-arg2 poly) x)))
	((product-p poly)
	(make-sum (make-product (product-arg1 poly)
	(d (product-arg2 poly) x))
	(make-product (product-arg2 poly)
	(d (product-arg1 poly) x))))
	((power-p poly)
	(make-product (make-product (power-exponent poly)
	(make-power (power-base poly)
	(1- (power-exponent poly))))
	(d (power-base poly) x)))))
	</pre>

	<p>Test driving the differentiation function we get:
	<pre>
	USER(11): (d '(+ x y) 'x)
	(+ 1 (D Y X))
	USER(12): (d '(* (+ x 1) (+ x 1)) 'x)
	(+ (* (+ X 1) (+ 1 0)) (* (+ X 1) (+ 1 0)))
	USER(13): (d '(** (+ x 1) 2) 'x)
	(* (* 2 (** (+ X 1) 1)) (+ 1 0))
	</pre>

	<p>The result is correct but very clumsy. We would like to
	simplify the result a bit using the following rewriting rules:
	<ul>
	<li><em>E + 0 = E</em>
	<li><em>0 + E = E</em>
	<li><em>E * 0 = 0</em>
	<li><em>0 * E = 0</em>
	<li><em>E * 1 = E</em>
	<li><em>1 * E = E</em>
	<li><em>E<sup>0</sup> = 1</em>
	<li><em>E<sup>1</sup> = E</em>
	</ul>

	<p>This can be done by defining a simplification framework, in which
	we can implement such rules:
	<pre>
	;;
	;; Simplification function
	;;

	(defun simplify (poly)
	"Simplify polynomial POLY."
	(cond
	((constant-p poly) poly)
	((variable-p poly) poly)
	((sum-p poly)
	(let ((arg1 (simplify (sum-arg1 poly)))
	(arg2 (simplify (sum-arg2 poly))))
	(make-simplified-sum arg1 arg2)))
	((product-p poly)
	(let ((arg1 (simplify (product-arg1 poly)))
	(arg2 (simplify (product-arg2 poly))))
	(make-simplified-product arg1 arg2)))
	((power-p poly)
	(let ((base (simplify (power-base poly)))
	(exponent (simplify (power-exponent poly))))
	(make-simplified-power base exponent)))
	((derivative-p poly) poly)))
	</pre>
	The <tt>simplify</tt> function decomposes a composite polynomial into
	its components, apply simplification recursively to the
	components, and then invoke the type-specific simplification
	rules (i.e. <tt>make-simplified-sum</tt>, <tt>make-simplified-product</tt>,
	<tt>make-simplified-power</tt>) based on the type of the polynomial
	being processed.

	<p>The simplification rules are encoded in LISP as follows:
	<pre>
	(defun make-simplified-sum (arg1 arg2)
	"Given simplified polynomials ARG1 and ARG2, construct a simplified sum of ARG1 and ARG2."
	(cond
	((and (constant-p arg1) (zerop arg1)) arg2)
	((and (constant-p arg2) (zerop arg2)) arg1)
	(t (make-sum arg1 arg2))))

	(defun make-simplified-product (arg1 arg2)
	"Given simplified polynomials ARG1 and ARG2, construct a simplified product of ARG1 and ARG2."
	(cond
	((and (constant-p arg1) (zerop arg1)) (make-constant 0))
	((and (constant-p arg2) (zerop arg2)) (make-constant 0))
	((and (constant-p arg1) (= arg1 1)) arg2)
	((and (constant-p arg2) (= arg2 1)) arg1)
	(t (make-product arg1 arg2))))

	(defun make-simplified-power (base exponent)
	"Given simplified polynomials BASE and EXPONENT, construct a simplified power with base BASE and exponent EXPONENT."
	(cond
	((and (constant-p exponent) (= exponent 1)) base)
	((and (constant-p exponent) (zerop exponent)) (make-constant 1))
	(t (make-power base exponent))))
	</pre>

	<p>Let us see how all these pay off:
	<pre>
	USER(14): (simplify (d '(* (+ x 1) (+ x 1)) 'x))
	(+ (+ X 1) (+ X 1))
	USER(15): (simplify (d '(** (+ x 1) 2) 'x))
	(* 2 (+ X 1))
	</pre>
	Comparing to the original results we saw before, this is a lot
	more reasonable.

	<p><hr><b>Exercise:</b> Extend the symbolic polynomial framework
	in the following ways:
	<ul>
	<li>Define a new type of polynomial --- <em>difference</em>.
	If <em>poly1</em> and <em>poly2</em> are polynomials, then
	<tt>(make-difference <em>poly1</em> <em>poly2</em>)</tt>
	is also a polynomial. Implement the constructor, recognizer
	and selectors for this type of polynomial.
	<li>The derivative <em>(d(F-G)/dx)</em> of a difference <em>(F-G)</em>
	is <em>(dF/dx) - (dG/dx)</em>. Extend the differentiation function
	to incorporate this.
	<li>Implement the following simplification rule:
	<ul>
	<li><em>E - 0 = E</em>
	</ul>
	</ul>
	<hr>

	<p><hr><b>Exercise:</b> Extend the symbolic polynomial framework
	in the following ways:
	<ul>
	<li>Define a new type of polynomial --- <em>negation</em>.
	If <em>poly1</em> is a polynomial, then <tt>(make-negation
	<em>poly</em>)</tt> is also a polynomial. Implement the
	constructor, recognizer and selectors for this type of polynomial.
	<li>The derivative <em>(d(-F)/dx)</em> of a negation <em>-F</em>
	is <em>-(dF/dx)</em>. Extend the differentiation function
	to incorporate this.
	<li>Implement the following simplification rules:
	<ul>
	<li><em>-0 = 0</em>
	<li><em>-(-E) = E</em>
	</ul>
	</ul>
	<hr>

	<p><hr><b>Exercise:</b> The simplification rules we have seen so
	far share a common feature: the right hand sides do not involve
	any new polynomial constructor. For example, <em>-(-E)</em> is
	simply <em>E</em>. However, some of the most useful simplification
	rules are those involving constructors on the right hand sides:
	<ul>
	<li><em>0 - E = -E</em>
	<li><em>E<sub>1</sub> + (-E<sub>2</sub>) = E<sub>1</sub> - E<sub>2</sub></em>
	<li><em>(-E<sub>1</sub>) + E<sub>2</sub> = E<sub>2</sub> - E<sub>1</sub></em>
	<li><em>E<sub>1</sub> - (-E<sub>2</sub>) = E<sub>1</sub> + E<sub>2</sub></em>
	<li><em>E * (-1) = -E</em>
	<li><em>(-1) * E = -E</em>
	</ul>
	Within the type-specific simplification functions,
	if we naively apply the regular constructors to build the expressions
	on the right hand sides, then we run into the risk of constructing
	polynomials that are not fully simplified. For example, <em>-x</em>
	and <em>-1</em> are both fully simplified, but if we now construct their
	product <em>(-1) * (-x)</em>, the last simplification rule above
	says that we can rewrite the product into <em>-(-x)</em>, which needs
	further simplification. One naive solution is to blindly apply full
	simplification to the newly constructed polynomials, but this is
	obviously an overkill. What then is an efficient and yet correct
	implementation of the
	above simplification rules?
	<hr>

	<p><hr><b>Exercise:</b> If all the components of a composite polynomial
	are constants, then we can actually perform further simplification.
	For example, <tt>(+ 1 1)</tt> should be simplified to <tt>2</tt>.
	Extend the simplification framework to incorporate this.
	<hr>

	<h2>Tower of Hanoi</h2>

	<p>The Tower of Hanoi problem is a classical toy problem in
	Artificial Intelligence: There are <em>N</em> disks
	<em>D<sub>1</sub></em>, <em>D<sub>2</sub></em>, ..., <em>D<sub>n</sub></em>,
	of graduated sizes and three pegs 1, 2, and 3. Initially all the disks
	are stacked on peg 1, with <em>D<sub>1</sub></em>, the smallest,
	on top and <em>D<sub>n</sub></em>, the largest, at the bottom. The
	problem is to transfer the stack to peg 3 given that only one disk
	can be moved at a time and that no disk may be placed on top of a
	smaller one. [Pearl 1984]

	<p>We call peg 1 the "from" peg, peg 3 the "to" peg. Peg 2 is
	a actually a buffer to facilitate movement of disks, and we call
	it an "auxiliary" peg. We can move <em>N</em> disks from the
	"from" peg to the "to" peg using the following recursive scheme.
	<ol>
	<li>Ignoring the largest disk at the "from" peg, treat the remaining disks
	as a Tower of Hanoi problem with <em>N-1</em> disks. Recursively
	move the top <em>N-1</em> disks from the "from" peg to the
	"auxiliary" peg, using the "to" peg as a buffer.
	<li>Now that the <em>N-1</em> smaller disks are in the "auxiliary" peg,
	we move the largest disk to the "to" peg.
	<li>Ignoring the largest disk again, treat the remaining disks
	as a Tower of Hanoi problem with <em>N-1</em> disks.
	Recursively move the <em>N-1</em> disks from the "auxiliary"
	peg to the "to" peg, using the "from" peg as a buffer.
	</ol>

	<p>To code this solution in LISP, we need to define some data structure.
	First, we represent a disk by a number, so that <em>D<sub>i</sub></em>
	is represented by <em>i</em>.
	Second, we represent a stack of disks by a <em>tower</em>, which is
	nothing but a list of numbers, with the first element representing
	the top disk. We define the usual constructors and selectors for
	the tower data type.
	<pre>
	;;
	;; A tower is a list of numbers
	;;

	(defun make-empty-tower ()
	"Create tower with no disk."
	nil)

	(defun tower-push (tower disk)
	"Create tower by stacking DISK on top of TOWER."
	(cons disk tower))

	(defun tower-top (tower)
	"Get the top disk of TOWER."
	(first tower))

	(defun tower-pop (tower)
	"Remove the top disk of TOWER."
	(rest tower))
	</pre>

	<p>Third, we define the <em>hanoi</em> data type to represent
	a Tower of Hanoi configuration. In particular, a hanoi configuration
	is a list of three towers. The elementary constructors and selectors
	are given below:
	<pre>
	;;
	;; Hanoi configuration
	;;

	(defun make-hanoi (from-tower aux-tower to-tower)
	"Create a Hanoi configuration from three towers."
	(list from-tower aux-tower to-tower))

	(defun hanoi-tower (hanoi i)
	"Select the I'th tower of a Hanoi construction."
	(nth (1- i) hanoi))
	</pre>
	<p>Working with towers within a Hanoi configuration is tedious.
	We therefore define some shortcut to capture recurring operations:
	<pre>
	;;
	;; Utilities
	;;

	(defun hanoi-tower-update (hanoi i tower)
	"Replace the I'th tower in the HANOI configuration by tower TOWER."
	(cond
	((= i 1) (make-hanoi tower (second hanoi) (third hanoi)))
	((= i 2) (make-hanoi (first hanoi) tower (third hanoi)))
	((= i 3) (make-hanoi (first hanoi) (second hanoi) tower))))

	(defun hanoi-tower-top (hanoi i)
	"Return the top disk of the I'th tower in the HANOI configuration."
	(tower-top (hanoi-tower hanoi i)))

	(defun hanoi-tower-pop (hanoi i)
	"Pop the top disk of the I'th tower in the HANOI configuration."
	(hanoi-tower-update hanoi i (tower-pop (hanoi-tower hanoi i))))

	(defun hanoi-tower-push (hanoi i disk)
	"Push DISK into the I'th tower of the HANOI configuration."
	(hanoi-tower-update hanoi i (tower-push (hanoi-tower hanoi i) disk)))
	</pre>

	<p>The fundamental operator we can perform on a Hanoi configuration
	is to move a top disk from one peg to another:
	<pre>
	;;
	;; Operator: move top disk from one tower to another
	;;

	(defun move-disk (from to hanoi)
	"Move the top disk from peg FROM to peg TO in configuration HANOI."
	(let
	((disk (hanoi-tower-top hanoi from))
	(intermediate-hanoi (hanoi-tower-pop hanoi from)))
	(hanoi-tower-push intermediate-hanoi to disk)))
	</pre>

	<p>We are now ready to capture the logic of our recursive solution
	into the following code:
	<pre>
	;;
	;; Subgoal: moving a tower from one peg to another
	;;

	(defun move-tower (N from aux to hanoi)
	"In the HANOI configuration, move the top N disks from peg FROM to peg TO using peg AUX as an auxiliary peg."
	(if (= N 1)
	(move-disk from to hanoi)
	(move-tower (- N 1) aux from to
	(move-disk from to
	(move-tower (- N 1) from to aux hanoi)))))
	</pre>

	<p>We use the driver function <tt>solve-hanoi</tt> to start up the recursion:
	<pre>
	;;
	;; Driver function
	;;

	(defun solve-hanoi (N)
	"Solve the Tower of Hanoi problem."
	(move-tower N 1 2 3 (make-hanoi (make-complete-tower N) nil nil)))

	(defun make-complete-tower (N)
	"Create a tower of N disks."
	(make-complete-tower-aux N (make-empty-tower)))

	(defun make-complete-tower-aux (N A)
	"Push a complete tower of N disks on top of tower A."
	(if (zerop N)
	A
	(make-complete-tower-aux (1- N) (tower-push A N))))
	</pre>

	<p>To solve a Tower of Hanoi problem with 3 disks, we call
	<tt>(solve-hanoi 3)</tt>:
	<pre>
	USER(50): (solve-hanoi 3)
	(NIL NIL (1 2 3))
	</pre>
	All we get back is the final configuration, which is not as
	interesting as knowing the sequence of moves taken by the
	algorithm. So we trace usage of the <tt>move-disk</tt>
	operator:
	<pre>
	USER(51): (trace move-disk)
	(MOVE-DISK)
	USER(52): (solve-hanoi 3)
	0: (MOVE-DISK 1 3 ((1 2 3) NIL NIL))
	0: returned ((2 3) NIL (1))
	0: (MOVE-DISK 1 2 ((2 3) NIL (1)))
	0: returned ((3) (2) (1))
	0: (MOVE-DISK 3 2 ((3) (2) (1)))
	0: returned ((3) (1 2) NIL)
	0: (MOVE-DISK 1 3 ((3) (1 2) NIL))
	0: returned (NIL (1 2) (3))
	0: (MOVE-DISK 2 1 (NIL (1 2) (3)))
	0: returned ((1) (2) (3))
	0: (MOVE-DISK 2 3 ((1) (2) (3)))
	0: returned ((1) NIL (2 3))
	0: (MOVE-DISK 1 3 ((1) NIL (2 3)))
	0: returned (NIL NIL (1 2 3))
	(NIL NIL (1 2 3))
	</pre>
	From the trace we can actually read off the sequence of
	operator applications necessary for one to achieve the solution
	configuration.
	This is good, but not good enough. We want to know why each move is
	being taken. So we trace also the high-level subgoals:
	<pre>
	USER(53): (trace move-tower)
	(MOVE-TOWER)
	USER(54): (solve-hanoi 3)
	0: (MOVE-TOWER 3 1 2 3 ((1 2 3) NIL NIL))
	1: (MOVE-TOWER 2 1 3 2 ((1 2 3) NIL NIL))
	2: (MOVE-TOWER 1 1 2 3 ((1 2 3) NIL NIL))
	3: (MOVE-DISK 1 3 ((1 2 3) NIL NIL))
	3: returned ((2 3) NIL (1))
	2: returned ((2 3) NIL (1))
	2: (MOVE-DISK 1 2 ((2 3) NIL (1)))
	2: returned ((3) (2) (1))
	2: (MOVE-TOWER 1 3 1 2 ((3) (2) (1)))
	3: (MOVE-DISK 3 2 ((3) (2) (1)))
	3: returned ((3) (1 2) NIL)
	2: returned ((3) (1 2) NIL)
	1: returned ((3) (1 2) NIL)
	1: (MOVE-DISK 1 3 ((3) (1 2) NIL))
	1: returned (NIL (1 2) (3))
	1: (MOVE-TOWER 2 2 1 3 (NIL (1 2) (3)))
	2: (MOVE-TOWER 1 2 3 1 (NIL (1 2) (3)))
	3: (MOVE-DISK 2 1 (NIL (1 2) (3)))
	3: returned ((1) (2) (3))
	2: returned ((1) (2) (3))
	2: (MOVE-DISK 2 3 ((1) (2) (3)))
	2: returned ((1) NIL (2 3))
	2: (MOVE-TOWER 1 1 2 3 ((1) NIL (2 3)))
	3: (MOVE-DISK 1 3 ((1) NIL (2 3)))
	3: returned (NIL NIL (1 2 3))
	2: returned (NIL NIL (1 2 3))
	1: returned (NIL NIL (1 2 3))
	0: returned (NIL NIL (1 2 3))
	(NIL NIL (1 2 3))
	</pre>
	The trace gives us information as to what subgoals each operator
	application is trying to establish. For example, the top level
	subgoals are the following:
	<pre>
	0: (MOVE-TOWER 3 1 2 3 ((1 2 3) NIL NIL))
	1: (MOVE-TOWER 2 1 3 2 ((1 2 3) NIL NIL))
	...
	1: returned ((3) (1 2) NIL)
	1: (MOVE-DISK 1 3 ((3) (1 2) NIL))
	1: returned (NIL (1 2) (3))
	1: (MOVE-TOWER 2 2 1 3 (NIL (1 2) (3)))
	...
	1: returned (NIL NIL (1 2 3))
	0: returned (NIL NIL (1 2 3))
	</pre>
	They translate directly to the following: In order to move a tower
	of 3 disks from peg 1 to peg 3 using peg 2 as a buffer
	(i.e. <tt>(MOVE-TOWER 3 1 2 3 ((1 2 3) NIL NIL))</tt>)
	we do the following:
	<ol>
	<li>
	"<tt>1: (MOVE-TOWER 2 1 3 2 ((1 2 3) NIL NIL))</tt>"<br>
	Move a tower of 2 disks from peg 1 to peg 2 using peg 3 as a buffer.
	The result of the move is the following:<br>
	"<tt>1: returned ((3) (1 2) NIL)</tt>"
	<li>
	"<tt>1: (MOVE-DISK 1 3 ((3) (1 2) NIL))</tt>"<br>
	Move a top disk from peg 1 to peg 3. The result of this move is:<br>
	"<tt>1: returned (NIL (1 2) (3))</tt>"<br>
	<li>"<tt>1: (MOVE-TOWER 2 2 1 3 (NIL (1 2) (3)))</tt>"<br>
	Move a tower of 2 disks from peg 2 to peg 3 using peg 1 as a buffer,
	yielding the following configuration:<br>
	"<tt>1: returned (NIL NIL (1 2 3))</tt>"<br>
	</ol>


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