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COMP3411/9814 Artificial Intelligence
创建日期：2024-05-02 10:13:25
标签： COMP3411
Artificial Intelligence

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COMP3411/9814 Artificial Intelligence

DRAFT
Assignment 2 – Prolog and Machine Learning
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 2*2 + (-4)*(-4) + 6*6 + 8*8). 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 evaluated, in
Prolog, if they appear after the is operator. So 0 is N mod 2 works, but N mod 2 is 0
doesn't work.
Question 1.2: Planning (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,
• SWC - Sam Wants Coffee,
• MW - Mail Waiting,
• RHM - Robot Has 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.
1. 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.
2. inter_construction operates on two clauses that gave different heads, i.e. C1 \= C2.
3. We then find the interaction of the bodies of the clauses and call it, B.
4. 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.
5. At this point the last argument C <- B = z1<-[b, d] because C is bound to z1 and
B is the intersection [b, d].
6. 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 (c) (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, you can install Weka in either your
home directory or somewhere central like /opt or /usr/local.
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
1. Poole &Mackworth, Artificial Intelligence: Foundations of Computational
Agents, Chapter 7, Supervised Machine Learning)
2. http://archive.ics.uci.edu/ml/datasets/Adult).
3. 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
292.
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.
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MOEC0021 STA 106 ENGG 1300A ENGG 1300 OLET1139 STAT 3503 STAT 206 FEM11149 ELEN 4720 COMP20003 SOLA1070 CS574 C11CS LINB29H3 MAST90083 ECON7200 STAT 610 LINB29 DSM-5 COMP3161 EEEN40010 MATH 4431 K353 STA 3386 IERG4300 COMS W4167 ENG5298 AMME 2302 CS3910 COMPSCI 320 STATS 369 GR5242 COSC 328 CSCI435 MGTS1301 INFS2200 COMP3900 CIS 375 Module AE03 COMP3121 CS260 HAM503 MGMT5050 INT3075 COSC 285 GEOL0029 GEGE2X01 STAT1201 MATH1061 MMF1941H STAT 5060 CS 520 COMP 4320 MSIN0104 000T FINS3648 ECMT2160 ECON4003 Phys 129L CSE 142 CS 3130 MGEB01 EECS 340 Finance (20443/20444) ECMM149 COMP5310 EEEN60180 CSC 111 AT2 CSCI 4430 STAB57 MA/CSC 427 MATH322 D100 CSCI544 CITS3004 CI6227 CSE 565 APCO 1P01 852L1 ELEC S411F COMP3207 STATS330 ECON7530 FIT5149 CSI4104 CMPT 371 COMP201 SEHS4696 FI4003 MATH6182 CG1 WS MATH401 MGT 205 QTM 110 COMP 0137 MACE12201 FIT5210 ECE 544 BISM7206 HUDM 6055 ECON3203 STATS 210 MSCI 570 MATH6173 COMPSCI4039 Accounting COM3503 AOE2024 ECON213 PSYC10004 Control Data 100/200A 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COMP2013 IB9X60 INVP001 EMATM0051 COMP220 BLAW1002 E20 TCP8001 ECON61001 EART40005 ECONM1022 MATH 365 MATH 367 ELEC6218 COMP 5812M EDUC 5061M ELEC ENG 1101 equation MATH6174 ST334 C4 EC9570 A33648 EBUS603 ECON47815 stata WFMS0001 PEM102 INF6060 ionically ELECTRICAL MATH 375 MMW 12 MKTG860 ECON60401 ALY6010 SOST20131 Introduction COM3524 MME2 GGR305 JNB 538 EECS101 COMM5030 IEOR E4601 2B CSC336 EN2315 LAW 432 CSC263 MATH2001 OLET1143 CSCB63 SIT772 COMM1170 STAT 441 MS930 INFT3050 GLBH0030 Stat 120B MAST10007 MGEC61 Machine GGR 252H1S ECON7030 Financial Marketing MGMT30019 ECON5120 SDSC 8009 CSC165H1S DSCI 551 3081W ACM41000 ECA 5304 MATH 1064 ECSE444 DATA 604 COMP7105 MIE1615 ECON4004 ENV200H1S EC481 MKT 306 EC1B1 MATH08051 ALY6140 CS260C CSE30 MSIN0226 CS4103 CSC3064 AM16 COMP11212 CSE4101 PSTAT 174 Math 380W CNT 5106C STAT 310 FIN2028 CS5014 STAT 425 COMP 4102A ELEC2140 MANF9544 QRM 9 QRM 8 ECON 457 COMPSCI 4091 MATH 523 DNSC-4211 PEAS 6000 MSIN0180 COMP2119 A2A HT 2022 IB93F0 MAS316/414 ACCT2522 MSIN0181 ATMS 201 COMP3425 MATH7861 SOLA2060 MA 325 7CCEMCTH ECON 432 INFT2051 SOLA 2060 ECOS2002 Stats141XP BIOA02H3 S2022 MA4601 K5 COMP5329 EG 284 IB3A70 WECO1020 COMP3027 Energy ACS61011 MAST20026 STAT 533 MATA31 ENVS257 BEM2031 EC 486 COMP0172 CS3214 EECE5644 IB2B20 COSC2536 COSC1147 COMP6214 5CCM242A COMP0130 AMME2000 MTMG50: EG238 ACF6006 QBUS2810 COSC2391 ECON7560 MATH377 STA305 CS 486 COMP1117B EL648 ELEC3909 ECON 241 COMP 4901W BUSI4428 ENEN20002 ENGN4524 ELEC3115 CSC 413 PSYC 100B ECON1002 MATH256 ANSC20005 MTHS2008 MATH40082 COSC 2406 FIT3174 STA 238 CMT202 Differential Equations MTHM017 GARCH 101 MATH2069 CSC336S ACSC12 ETF2121 CEME 2001 DB B474F ARHT1001 EECS 3311 3220 STATS 786 CS 484 COMP20008 MATH4091 ECE 455 CSCI 204 BUS B200F BA 875 BU5562 COSC 1147 X11 GEOG 101 IMM250F CSE 584A MATH38172 LAB 4 ELEC5882M FINS5523 EE 430 COMP 465 6CCS3PRE 7CCSMNSE FINANCE ACMB02H3 ENGG2112 ELEC5616 SCM B371 CHEM3120 CS22 ECMT2130 MAST30011 ADSPBF706 CENG0013 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