comp3411辅导assignment2

plan的思路很简单，就是使用深度优先，假设有一个可以通过某动作到达的中间状态，然后就枚举这个动作。
5. id_plan()其实就是迭代式的深度优先，融合了深度优先和广度优先的区别，append(Plan,,)这个用法很神奇，因为第2个和第3个变量为空，其实也就意味着Plan可以是任意长度，所以在id_plan里的append这句，依次认为Plan是空列表，一个元素的列表，2，3，4，5，6，…个元素的列表，然后去plan中深度优先，所以就设定了深度。当比如6条路还找不到时，就返回到上一层，把6条路的所有可能路径都遍历一遍。
6. test2失败需要仔细观察test1和test2的不同之处，其实在后期，test2的所有路径都是选择的path，机器人把篮子推来推去！陷入了循环，路径一直延长，最后爆栈。这里和Prolog如何选择多个可选项的某一个也有关系，总而言之，prolog每次就选push。上一条路径和这条路径陷入了同一种可以选push的情况。
7. 因为id_plan限制了深度。这里就要说说迭代试相比单纯的深度优先的优势了。

1.2.1 代码使用

关于lists的使用：

% double_digit :- 1 is 1. % =:= is  全是数字，或者是数字的表达式，用=:=，

% is_even(X) :- Y is X//2, X is 2 * Y. %10 // 2 = 5, 5*2 == 10

% is_even(X) :- Y is mod(X, 2), 0 is Y. %10 // 2 = 5, 5*2 == 10

% is_even(X) :- Y is mod(X, 2), Y is 0. %10 // 2 = 5, 5*2 == 10


% query 里，如果有变量，则用is，并且变量在前，因为要给变量赋值。只能是  变量:=表达式，不能是表达式:=变量。
% 代码里，变量和变量相比较，可以是is也可以是=:=
% 代码里，变量和非变量相比较，可以是is也可以是=:=

% List1 :- [a,b,c].

% lists的表示，[] 
% 库函数，member()    length  reverse  | append

% Get the length of a list
% length([1,2,3], X).

% Find out if a value is in a list with member
% List1 = [a,b,c].
% member(a, List1). = yes

% We could also get all members of a list with a variable
% member(X, [a, b, c, d]).

% Reverse a list
% reverse([1,2,3,4,5], X).

% Concatenate 2 lists
% append([1,2,3], [4,5,6], X).

1.1.4 test1成功，深度优先

[trace]  ?- test1(X).
   Call: (8) test1(_3818) ? creep
   Call: (9) plan(state(door, corner, floor(middle)), state(_4030, _4032, in_basket), _3818) ? creep
   Call: (10) action(_4038, state(door, corner, floor(middle)), _4062) ? creep
   Exit: (10) action(go(door, _4046), state(door, corner, floor(middle)), state(_4046, corner, floor(middle))) ? creep
   Call: (10) plan(state(_4046, corner, floor(middle)), state(_4030, _4032, in_basket), _4040) ? creep
   Call: (11) action(_4058, state(_4046, corner, floor(middle)), _4082) ? creep
   Exit: (11) action(pickup, state(middle, corner, floor(middle)), state(middle, corner, held)) ? creep
   Call: (11) plan(state(middle, corner, held), state(_4030, _4032, in_basket), _4060) ? creep
   Call: (12) action(_4072, state(middle, corner, held), _4096) ? creep
   Exit: (12) action(go(middle, _4080), state(middle, corner, held), state(_4080, corner, held)) ? creep
   Call: (12) plan(state(_4080, corner, held), state(_4030, _4032, in_basket), _4074) ? creep
   Call: (13) action(_4092, state(_4080, corner, held), _4116) ? creep
   Exit: (13) action(drop, state(corner, corner, held), state(corner, corner, in_basket)) ? creep
   Call: (13) plan(state(corner, corner, in_basket), state(_4030, _4032, in_basket), _4094) ? creep
   Exit: (13) plan(state(corner, corner, in_basket), state(corner, corner, in_basket), []) ? creep
   Exit: (12) plan(state(corner, corner, held), state(corner, corner, in_basket), [drop]) ? creep
   Exit: (11) plan(state(middle, corner, held), state(corner, corner, in_basket), [go(middle, corner), drop]) ? creep
   Exit: (10) plan(state(middle, corner, floor(middle)), state(corner, corner, in_basket), [pickup, go(middle, corner), drop]) ? creep
   Exit: (9) plan(state(door, corner, floor(middle)), state(corner, corner, in_basket), [go(door, middle), pickup, go(middle, corner), drop]) ? creep
   Exit: (8) test1([go(door, middle), pickup, go(middle, corner), drop]) ? creep
X = [go(door, middle), pickup, go(middle, corner), drop] .

1.1.3 test2失败，也就是说，深度优先失败的原因

最后会一直重复push操作。

test2 调用栈如下

[trace]  ?- test2(X).
   Call: (8) test2(_1084) ? creep
   Call: (9) plan(state(door, corner, floor(middle)), state(door, corner1, in_basket), _1084) ? creep
   Call: (10) action(_1304, state(door, corner, floor(middle)), _1328) ? creep
   Exit: (10) action(go(door, _1312), state(door, corner, floor(middle)), state(_1312, corner, floor(middle))) ? creep
   Call: (10) plan(state(_1312, corner, floor(middle)), state(door, corner1, in_basket), _1306) ? creep
   Call: (11) action(_1324, state(_1312, corner, floor(middle)), _1348) ? creep
   Exit: (11) action(pickup, state(middle, corner, floor(middle)), state(middle, corner, held)) ? creep
   Call: (11) plan(state(middle, corner, held), state(door, corner1, in_basket), _1326) ? creep
   Call: (12) action(_1338, state(middle, corner, held), _1362) ? creep
   Exit: (12) action(go(middle, _1346), state(middle, corner, held), state(_1346, corner, held)) ? creep
   Call: (12) plan(state(_1346, corner, held), state(door, corner1, in_basket), _1340) ? creep
   Call: (13) action(_1358, state(_1346, corner, held), _1382) ? creep
   Exit: (13) action(drop, state(corner, corner, held), state(corner, corner, in_basket)) ? creep
   Call: (13) plan(state(corner, corner, in_basket), state(door, corner1, in_basket), _1360) ? creep
   Call: (14) action(_1372, state(corner, corner, in_basket), _1396) ? creep
   Exit: (14) action(push(corner, _1380), state(corner, corner, in_basket), state(_1380, _1380, in_basket)) ? creep
   Call: (14) plan(state(_1380, _1380, in_basket), state(door, corner1, in_basket), _1374) ? creep
   Call: (15) action(_1392, state(_1380, _1380, in_basket), _1416) ? creep
   Exit: (15) action(push(_1380, _1400), state(_1380, _1380, in_basket), state(_1400, _1400, in_basket)) ? creep
   Call: (15) plan(state(_1400, _1400, in_basket), state(door, corner1, in_basket), _1394) ? creep
   Call: (16) action(_1412, state(_1400, _1400, in_basket), _1436) ? creep
   Exit: (16) action(push(_1400, _1420), state(_1400, _1400, in_basket), state(_1420, _1420, in_basket)) ? creep
   Call: (16) plan(state(_1420, _1420, in_basket), state(door, corner1, in_basket), _1414) ? creep
   Call: (17) action(_1432, state(_1420, _1420, in_basket), _1456) ? creep
   Exit: (17) action(push(_1420, _1440), state(_1420, _1420, in_basket), state(_1440, _1440, in_basket)) ? creep
   Call: (17) plan(state(_1440, _1440, in_basket), state(door, corner1, in_basket), _1434) ? creep
   Call: (18) action(_1452, state(_1440, _1440, in_basket), _1476) ? creep
   Exit: (18) action(push(_1440, _1460), state(_1440, _1440, in_basket), state(_1460, _1460, in_basket)) ? creep
   Call: (18) plan(state(_1460, _1460, in_basket), state(door, corner1, in_basket), _1454) ? creep
   Call: (19) action(_1472, state(_1460, _1460, in_basket), _1496) ? creep
   Exit: (19) action(push(_1460, _1480), state(_1460, _1460, in_basket), state(_1480, _1480, in_basket)) ? creep
   Call: (19) plan(state(_1480, _1480, in_basket), state(door, corner1, in_basket), _1474) ? creep
   Call: (20) action(_1492, state(_1480, _1480, in_basket), _1516) ? creep
   Exit: (20) action(push(_1480, _1500), state(_1480, _1480, in_basket), state(_1500, _1500, in_basket)) ? creep
   Call: (20) plan(state(_1500, _1500, in_basket), state(door, corner1, in_basket), _1494) ? creep
   Call: (21) action(_1512, state(_1500, _1500, in_basket), _1536) ? creep
   Exit: (21) action(push(_1500, _1520), state(_1500, _1500, in_basket), state(_1520, _1520, in_basket)) ? creep
   Call: (21) plan(state(_1520, _1520, in_basket), state(door, corner1, in_basket), _1514) ? creep
   Call: (22) action(_1532, state(_1520, _1520, in_basket), _1556) ? creep
   Exit: (22) action(push(_1520, _1540), state(_1520, _1520, in_basket), state(_1540, _1540, in_basket)) ? creep
   Call: (22) plan(state(_1540, _1540, in_basket), state(door, corner1, in_basket), _1534) ? creep
   Call: (23) action(_1552, state(_1540, _1540, in_basket), _1576) ? creep
   Exit: (23) action(push(_1540, _1560), state(_1540, _1540, in_basket), state(_1560, _1560, in_basket)) ? creep
   Call: (23) plan(state(_1560, _1560, in_basket), state(door, corner1, in_basket), _1554) ? creep
   Call: (24) action(_1572, state(_1560, _1560, in_basket), _1596) ? creep
   Exit: (24) action(push(_1560, _1580), state(_1560, _1560, in_basket), state(_1580, _1580, in_basket)) ? creep
   Call: (24) plan(state(_1580, _1580, in_basket), state(door, corner1, in_basket), _1574) ? creep
   Call: (25) action(_1592, state(_1580, _1580, in_basket), _1616) ? creep
   Exit: (25) action(push(_1580, _1600), state(_1580, _1580, in_basket), state(_1600, _1600, in_basket)) ? creep
   Call: (25) plan(state(_1600, _1600, in_basket), state(door, corner1, in_basket), _1594) ? creep
   Call: (26) action(_1612, state(_1600, _1600, in_basket), _1636) ? creep
   Exit: (26) action(push(_1600, _1620), state(_1600, _1600, in_basket), state(_1620, _1620, in_basket)) ? creep
   Call: (26) plan(state(_1620, _1620, in_basket), state(door, corner1, in_basket), _1614) ? creep
   Call: (27) action(_1632, state(_1620, _1620, in_basket), _1656) ? creep
   Exit: (27) action(push(_1620, _1640), state(_1620, _1620, in_basket), state(_1640, _1640, in_basket)) ? creep
   Call: (27) plan(state(_1640, _1640, in_basket), state(door, corner1, in_basket), _1634) ? creep
   Call: (28) action(_1652, state(_1640, _1640, in_basket), _1676) ? creep
   Exit: (28) action(push(_1640, _1660), state(_1640, _1640, in_basket), state(_1660, _1660, in_basket)) ? creep
   Call: (28) plan(state(_1660, _1660, in_basket), state(door, corner1, in_basket), _1654) ? creep
   Call: (29) action(_1672, state(_1660, _1660, in_basket), _1696) ? creep
   Exit: (29) action(push(_1660, _1680), state(_1660, _1660, in_basket), state(_1680, _1680, in_basket)) ? creep
   Call: (29) plan(state(_1680, _1680, in_basket), state(door, corner1, in_basket), _1674) ? creep
   Call: (30) action(_1692, state(_1680, _1680, in_basket), _1716) ? creep
   Exit: (30) action(push(_1680, _1700), state(_1680, _1680, in_basket), state(_1700, _1700, in_basket)) ? creep
   Call: (30) plan(state(_1700, _1700, in_basket), state(door, corner1, in_basket), _1694) ? creep
   Call: (31) action(_1712, state(_1700, _1700, in_basket), _1736) ? creep
   Exit: (31) action(push(_1700, _1720), state(_1700, _1700, in_basket), state(_1720, _1720, in_basket)) ? creep
   Call: (31) plan(state(_1720, _1720, in_basket), state(door, corner1, in_basket), _1714) ? creep
   Call: (32) action(_1732, state(_1720, _1720, in_basket), _1756) ? creep
   Exit: (32) action(push(_1720, _1740), state(_1720, _1720, in_basket), state(_1740, _1740, in_basket)) ? creep
   Call: (32) plan(state(_1740, _1740, in_basket), state(door, corner1, in_basket), _1734) ? creep
   Call: (33) action(_1752, state(_1740, _1740, in_basket), _1776) ? creep
   Exit: (33) action(push(_1740, _1760), state(_1740, _1740, in_basket), state(_1760, _1760, in_basket)) ? creep
   Call: (33) plan(state(_1760, _1760, in_basket), state(door, corner1, in_basket), _1754) ? creep

1.1.4 test3 id search iggs

Plan: []
Plan: [_3910]
Plan: [_3910,_3922]
Plan: [_3910,_3922,_3934]
Plan: [_3910,_3922,_3934,_3946]
Plan: [_3910,_3922,_3934,_3946,_3958]
Plan: [_3910,_3922,_3934,_3946,_3958,_3970]
X = [go(door, middle), pickup, go(middle, corner), drop, push(corner, corner1), go(corner1, door)] .

这个使用append的操作，是如何实现iterative deepening planner呢？

% Iterative deepening planner
% Backtracking to "append" generates lists of increasing length
% Forces "plan" to ceate fixed length plans

id_plan(Start, Goal, Plan) :-
    append(Plan, _, _),
    % write('Plan: '), write(Plan), nl,
    plan(Start, Goal, Plan).

1.2 题目详情

这题就不说了，其实主要就是给了一个代码，修改这个代码适合题目。
这个原本的代码，只需要修改action那里的代码，planner那里完全不用改就可以成功了。

1.3 代码解析

1.3代码比较多。
:-op :op是定义运算符的操作，具体用法如下：

:- op(Precedence, Type, Name).

Precedence是介于 0 和 1200 之间的数字。在prolog内部，=的优先级是 700，+的优先级是 500，*优先级是400。如果我们自定义的运算符是600,那运算的时候，600大于500和400，*和+就会后运算。
类型是指定运算符的类型和关联性。Type的外观有以下几种可能性：


中缀： xfx, xfy, yfx

前缀：fx, fy

后缀：xf, yf

Name就简单了，单纯说明我们新定义的运算符的名字是什么。

inter_construction

inter_construction(C1 <- B1, C2 <- B2, C1 <- Z1B, C2 <- Z2B, C <- B) :-
C1 \= C2,
intersection(B1, B2, B),
gensym(z, C),
subtract(B1, B, B11),
subtract(B2, B, B12),
append(B11, [C], Z1B),
append(B12, [C], Z2B).

The first line the program assumes that the first two arguments are given as
propositional clauses. The remaining three arguments are the output clauses, as in the
example above.

这句话的意思是说，我们建的这个函数，总共有5个参数，10个变量，其实真正用的时候，我们可以任意用原子值填充一些变量，那剩下的变量就会根据规则去推理。不过，我们实际用的时候，会填充前两个参数中的4个变量，推理剩下的3个参数中的6个变量。
实验,参数可以是多个变量：

%
test(X+Y, Z) :-
    Z is X+Y.
test(1+2,Z).
Z=3

intersection()

?- intersection([1,2],[1],X).
X = [1].

gensym
subtract(list1,list2, target)

subtract()把list1中和list2含有的元素都删除掉。

gensym(z,C)
就是生成符号的意思。generate symbol，内置命令，z是base,第一次用gensym(z,C)就是用z1绑定C，[C]=z1
append(B12, [C], Z2B),将B12和[C]的绑定，也就是z1结合在一起。
subset(X, Y),
where X is a subset of Y.

Intra-construction.

ass2 详情

COMP3411/9814 Artificial Intelligence
Term 1, 2022
DRAFT
Assignment 1 – Prolog and Machine Learning
Due: Week 10 - 10pm Friday 22 April
Marks: 20% of final assessment for COMP3411/9814 Artificial Intelligence
This assignment has two parts: Prolog programming and using a machine learning
toolkit. The first part will be submitted as a Prolog source file and the second part as a
PDF file.
Part 1 - Prolog
At the top of your ,ile, place a comment containing your full name, student
number and assignment name. You may add additional information like the date
the program was completed, etc. if you wish.
At the start of each Prolog predicate, write a comment describing the operation of
the predicate.
A signi,icant part of completing this assignment will be testing the code you write
to make sure that it works correctly. To do this, you will need to design test cases
that exercise every part of the code.
It is important to use exactly the names given below for your predicates,
otherwise the automated testing procedure will not be able to 9ind your
predicates and you will lose marks. Even the capitalisation of your predicate
names must be as given below.
This assignment will be marked on functionality in the first instance. However, you
should always adhere to good programming practices in regard to structure, style and
comments, as described in the Prolog Dictionary. Submissions which score very low in
the automarking will be examined by a human marker, and may be awarded a few
marks, provided the code is readable.
Your code must work under the version of SWI Prolog used on the Linux machines in
the UNSW School of Computer Science and Engineering. If you develop your code on
any other platform, it is your responsibility to re-test and, if necessary, correct your code
when you transfer it to a CSE Linux machine prior to submission.
Your code will be run on a few simple tests when you submit. So, it is a good idea to
submit early and often so that potential problems with your code can be detected early.
You will be notified at submission time if your code produces any compiler warnings.
Please ensure that your final submission does not produce any such warnings
(otherwise, marks will be deducted).
Question 1.1: List Processing (4 marks)
Write a predicate sumsq_even(Numbers, Sum) that sums the squares of only the
even numbers in a list of integers.
Example:
?- sumsq_even([1,3,5,2,-4,6,8,-7], Sum).
Sum = 120
The example computes 22 + (-4)(-4) + 66 + 88). The sum for an empty list should
be 0.
To decide if a number is even or odd, you can use the built-in Prolog operator
N mod M, which computes the remainder after dividing the whole number N by the
whole number M. Thus a number N is even if the goal 0 is N mod 2 succeeds.
Remember that arithmetic expressions like X + 1 and N mod M are only eval（5 marks)
Modify the simple planner from the week 5 lecture so that it can solve the planning
problem in the textbook, Poole and Mackworth Section 6.1. Here, there is a robot can
can move around an office building, pickup mail and deliver coffee.
Like the rubbish pickup problem, actions can be representation by a triple:
action(Action, State, NewState)
where the state of the world is represented by a quintuple:
state(RLoc, RHC, SWC, MW, RHM)
• RLoc - Robot Location,
• RHC - Robot Has Coffee,
• SMC - Sam Wants Coffee,
• MW - Mail Waiting,
• RCM - Robot Carrying Mail
You are required to replace the action specifications by a new set that specify the state
transitions for each of the actions:
• mc - move clockwise
• mcc - move counterclockwise
• puc - pickup coffee
• dc - deliver coffee
• pum - pickup mail
• dm - deliver mail.
Do not alter the planner code, only the action clauses should be replaced.
You should only need to write one clause for each action, however, you might need
some addition clauses to help with the mc and mcc actions. Think about how to
represent the room layout.
Your program should be able to satisfy queries such as:
?- id_plan(
state(lab, false, true, false, false),
state(_, _, false, _, ),
Plan).
Plan = [mc, mc, puc, mc, dc]
which asks the robot to get Sam coffee. Note that the values of the state variables, RHC,
SWC, MW, RHM are boolean and we’ve used the constants true and false, to represent
the boolean values. The initial state in this example has the robot in the lab, the robot
does not have any coffee, Sam wants coffee, there is no mail waiting and the robot does
not have any mail. The goal state is where the the Sam no longer wants coffee. Note that
the anonymous variable, ‘’, indicates that we don’t care what the other values are.
To test your action specifications, think about how you would ask the robot to pickup
mail. What about if Sam want coffee and there was mail waiting?
Do not leave any testing code on your submitted file.
Question 1.3: Inductive Logic Programming
Duce is a program devised by Muggleton [3] to perform learning on a set of
propositional clauses. The system includes 6 operators that transform pairs of clauses
into a new set of clauses. Your job is to write Prolog programs to implement 2 of the 6
operators. One is given as an example to get you started.
Inter-construction. This transformation takes two rules, with different heads, such as
x <- [b, c, d, e]
y <- [a, b, d, f]
and replaces them with rules
x <- [c, e, z]
y <- [a, f, z]
z <- [b, d]
i.e. we find the intersection of the bodies of the first two clauses, invent a new predicate
symbol, z, and create the clause z ← [b, d]. Create two new clauses which are the
original clauses rewritten to replace [b, d] by z.
To make processing the rules easier, we represent the body of the clause by a list and we
define the operator <- to mean “implied by” or “if’.
A Prolog program to implement inter-construction is given as an example to give you
hints about how to write the two operators.
:- op(300, xfx, <-).
inter_construction(C1 <- B1, C2 <- B2, C1 <- Z1B, C2 <- Z2B, C <- B) :-
C1 = C2,
intersection(B1, B2, B),
gensym(z, C),
subtract(B1, B, B11),
subtract(B2, B, B12),
append(B11, [C], Z1B),
append(B12, [C], Z2B).
First, we define <- as an operator that will allow us to use the notation x <- y. The
program uses Prolog built-in predicates to perform set operations on lists and to
generate new symbols.

The first line the program assumes that the first two arguments are given as
propositional clauses. The remaining three arguments are the output clauses, as in the
example above.
inter_construction operates on two clauses that gave different heads, i.e. C1 = C2.
We then find the interaction of the bodies of the clauses and call it, B.
gensym is a builtin predicate that creates a new name from a base name. So
gensym(z, C) binds C to z1. Every subsequent call to gensym, with base z, will
create names, z2, z3, …. Calling reset_gensym will restart the numbering sequence.
At this point the last argument C <- B = z1<-[b, d] because C is bound to z1 and
B is the intersection [b, d].
The bodies Z1B and Z2B, are obtained by subtracting the intersection, B, from B1
and B2 and appending the single element [C]. So we can run the program:
?- inter_construction(x <- [b, c, d, e], y <- [a, b, d, f], X, Y, Z).
X = x<-[c, e, z1],
Y = y<-[a, f, z1],
Z = z1<-[b, d].
obtaining the desired result.
You will write programs for the other five operators, defined below. All of these
programs can be created from the builtin predicates used in the inter-construction
code. So all you have to do is work out how to combine them. The only other builtin
predicate you may need is to test if one list is a subset of another with subset(X, Y),
where X is a subset of Y.
You can assume that the inputs will always be valid clauses and the lists will always be
non-empty, i.e. with at least one element.
Question 1.3 (a) (2 marks):
Intra-construction. This transformation takes two rules, with the same head, such as
x <- [b, c, d, e]
x <- [a, b, d, f]
and replaces the with rules
x <- [b, d, z]
z <- [c, e]
z <- [a, f]
That is, we merge the two, x, clauses, keeping the intersection and adding a new
predicate, z, that distributes the differences to two new clauses.
?- intra_construction(x <- [b, c, d, e], x <- [a, b, d, f], X, Y, Z).
X = x<-[b, d, z1],
Y = z1<-[c, e],
Z = z1<-[a, f].
Question 1.3 (b) (2 marks):
Absorption. This transformation takes two rules, with the different heads, such as
x <- [a, b, c, d, e]
y <- [a, b, c]
and replaces the with rules
x <- [y, d, e]
y <- [a, b, c]
Note that the second clause is unchanged. This operator checks to see if the body of one
clause is a subset of the other. If it is, the common elements can be removed from the
larger clause and replaced by the head of the smaller one.
?- absorption(x <- [a, b, c, d, e], y <- [a, b, c], X, Y).
X = x<-[y, d, e],
Y = y<-[a, b, c].
Question 1.3 © (2 marks):
Truncation. This is the simplest transformation. It takes two rules that have the same
head and simply drops the differences to leave just one rule. For example
x <- [a, b, c, d]
x <- [a, c, j, k]
are replaced by
x <- [a, c]
That is, the body of the new clause is just the intersection of the bodies of the input
clauses.
?- truncation(x <- [a, b, c, d], x <- [a, c, j, k], X).
X = x<-[a, c].
The complete Duce algorithm performs a search, looking for combination of these
operators that will lead to the greater compression over the database of examples. Here
compression is measured by the reduction in the number of symbols in the database.
You don’t have to worry about the search algorithm, just implement the operators, as
above
Part 2 - Machine Learning
Question 2.1 (5 marks): Machine Learning
For this question, you will the Weka machine learning toolkit to apply a variety of
algorithms to a given dataset so that you can compare the results.
Go to the Weka website and download the program and see the accompanying
documentation for how to use it. There is much more on the Weka website and it also
has online help.
Use the data set Soybean Data Set that is provided as an example with Weka. The
example data are in a directory called data, that is in Weka’s installation directory. On
the Mac, this is in /Applications/weka-3.8.6.app/Contents/app/data. On Windows it is
in C:\Program Files\Weka-3-8-6\data. On Linux, Weka creates a directory ~/wekafiles.
You will try four learning algorithms:
• J48 - decision tree learner
• NaiveBayes - a naïve Bayes classifier
• JRip - a rule learner
• MultilayerPerceptron - a multilayer neural net algorithm
Create a table based on the output:
The last column is your subjective assessment of the readability of the output model.
References
Poole &Mackworth, Artificial Intelligence: Foundations of Computational
Agents, Chapter 7, Supervised Machine Learning)
http://archive.ics.uci.edu/ml/datasets/Adult).
Muggleton, S. (1987). Duce, An oracle based approach to constructive induction.
Proceedings of the International Joint Conference on Artificial Intelligence, 287–

Algorithm
Accuracy
(correctly classified
instances)
Time to build
model
Readablity
NaiveBayes
Multilayer Perceptron
JRip
J48
Submitting your assignment
This assignment must be submitted electronically.
Put your zID and your name at the top of every page of your submission!
give cs3411 assign2 part1.pl part2.pdf
• part1.pl should contain all the your Prolog code.
• part2.pdf should contain the table from part 2.
Late submissions will incur a penalty of 5% per day, applied to the maximum mark.
Group submissions are not allowed. By all means, discuss the assignment with your
fellow students. But you must write (or type) your answers individually. Do NOT copy
anyone else’s assignment, or send your assignment to any other student.

Using the Weka Machine Learning toolkit
Weka is a Machine Learning toolkit written in Java. It was developed and is maintained by a team
from Waikato University in New Zealand but it has packages contributed from many different
sources.
Weka is reasonably easy to use as it has a GUI so you don’t need to do any programming. It runs on
Linux, macOS and Windows. You can download it from:
https://waikato.github.io/weka-wiki/downloading_weka/ The download includes a Java JVM, so you shouldn’t need to install Java, if you haven’t got it
already.
Once you have it installed, when you start Weka, you will see window like this:
1.
Click on the explorer button and you will get a new window:
This shows you the data panel.
2.
Click “Open file …” to select the data file. This is in “ARFF” format. An example is appended
at the end of this document. You can find example datasets in the data folder that is the
installation folder, if you are using a Mac or Windows. If you use Linux, Weka will create a
directory in your home directory.
3.
By ticking the check boxes, you can see the distribution of objects with the ticked attribute
values.
4.
Next, click on the “classify” button (at the top of the window) to bring up the classification
panel:
5.
Clicking the “Choose” button will give you a list of different classifier learning algorithms.
Choose J48 under the Trees subcategory. This is Weka’s implementation of Quinlan’s C4.5
algorithm. You will find NaiveBayes under Bayes and MultilayerPerceptron under functions.
JRip is under rules.
6.
The default setting in for testing is 10-fold cross-validation. You can also click on “Percentage
split” to get a default 66% training data 34% test data.
7.
Clicking “Start” will run the learning algorithm on the data set and produce the output on the
right. The panel contain error statistics and timing information.
Example ARFF File
The file needs an @relation line to tell it the name of the data table.
Each attribute is defined in an @attribute line, which also give the type of the attribute. A
real valued attribute is ‘real’; discrete values attributes are given a list of the valid values.
A question mark means missing value.
The example below is from the UCI repository. It is not the complete data file, as it is too
big to list here. If you are interested, more example data sets are here:
https://waikato.github.io/weka-wiki/datasets/
@relation labor
@attribute ‘duration’ real
@attribute ‘wage-increase-first-year’ real
@attribute ‘wage-increase-second-year’ real
@attribute ‘wage-increase-third-year’ real
@attribute ‘cost-of-living-adjustment’ {‘none’,‘tcf’,‘tc’}
@attribute ‘working-hours’ real
@attribute ‘pension’ {‘none’,‘ret_allw’,‘empl_contr’}
@attribute ‘standby-pay’ real
@attribute ‘shift-differential’ real
@attribute ‘education-allowance’ {‘yes’,‘no’}
@attribute ‘statutory-holidays’ real
@attribute ‘vacation’ {‘below_average’,‘average’,‘generous’}
@attribute ‘longterm-disability-assistance’ {‘yes’,‘no’}
@attribute ‘contribution-to-dental-plan’ {‘none’,‘half’,‘full’}
@attribute ‘bereavement-assistance’ {‘yes’,‘no’}
@attribute ‘contribution-to-health-plan’ {‘none’,‘half’,‘full’}
@attribute ‘class’ {‘bad’,‘good’}
@data
1,5,?,?,?,40,?,?,2,?,11,‘average’,?,?,‘yes’,?,‘good’
2,4.5,5.8,?,?,35,‘ret_allw’,?,?,‘yes’,11,‘below_average’,?,‘full’,?,‘full’,‘good’
?,?,?,?,?,38,‘empl_contr’,?,5,?,11,‘generous’,‘yes’,‘half’,‘yes’,‘half’,‘good’
3,3.7,4,5,‘tc’,?,?,?,?,‘yes’,?,?,?,?,‘yes’,?,‘good’
3,4.5,4.5,5,?,40,?,?,?,?,12,‘average’,?,‘half’,‘yes’,‘half’,‘good’
2,2,2.5,?,?,35,?,?,6,‘yes’,12,‘average’,?,?,?,?,‘good’
3,4,5,5,‘tc’,?,‘empl_contr’,?,?,?,12,‘generous’,‘yes’,‘none’,‘yes’,‘half’,‘good’
3,6.9,4.8,2.3,?,40,?,?,3,?,12,‘below_average’,?,?,?,?,‘good’
2,3,7,?,?,38,?,12,25,‘yes’,11,‘below_average’,‘yes’,‘half’,‘yes’,?,‘good’
1,5.7,?,?,‘none’,40,‘empl_contr’,?,4,?,11,‘generous’,‘yes’,‘full’,?,?,‘good’
3,3.5,4,4.6,‘none’,36,?,?,3,?,13,‘generous’,?,?,‘yes’,‘full’,‘good’
2,6.4,6.4,?,?,38,?,?,4,?,15,?,?,‘full’,?,?,‘good’
2,3.5,4,?,‘none’,40,?,?,2,‘no’,10,‘below_average’,‘no’,‘half’,?,‘half’,‘bad’
3,3.5,4,5.1,‘tcf’,37,?,?,4,?,13,‘generous’,?,‘full’,‘yes’,‘full’,‘good’
1,3,?,?,‘none’,36,?,?,10,‘no’,11,‘generous’,?,?,?,?,‘good’
2,4.5,4,?,‘none’,37,‘empl_contr’,?,?,?,11,‘average’,?,‘full’,‘yes’,?,‘good’
1,2.8,?,?,?,35,?,?,2,?,12,‘below_average’,?,?,?,?,‘good’
1,2.1,?,?,‘tc’,40,‘ret_allw’,2,3,‘no’,9,‘below_average’,‘yes’,‘half’,?,‘none’,‘bad’

标签：comp3411,assignment2,辅导,state,Call,basket,corner,action,creep
From： https://blog.51cto.com/u_15721769/5745186

文章目录

1 内容分析

1.1

1.2 模拟机器人