ARTIFICIAL INTELLIGENCE SEARCH
ASSIGNMENT
This is the assignment for the sub-module Artificial Intelligence Search of
the module Software Methodologies. The hand-out date is Monday 14th
October 2019 and it is to be completed and handed in via DUO by 2
p.m. on Friday 17th January 2020.
***** It is really important that you read this document *****
***** thoroughly before you start. I’m sorry to appear *****
***** so dramatic with my bold-italic-red script but it *****
***** really is important that you follow the guidelines. *****
Overview
You are to implement two different algorithms (call them Algorithm
A and Algorithm B) in Python using techniques studied during the lectures,
but possibly also other algorithms you have devised or discovered for
yourself, to solve the Travelling Salesman Problem (TSP). Your implementations
should seek to obtain the best TSP tours that you can, given 10
collections of cities and their distances that I will supply to you. You will
need to hand in the following items:
? between 2 and 4 correct Python programs comprising of: at least
a basic implementation of each of your chosen algorithms (these are
the files necessarily named AlgAbasic.py and AlgBbasic.py); and
possibly an enhanced implementation of each of your chosen algorithms
(these are the files necessarily named AlgAenhanced.py and
AlgBenhanced.py)
? for each of the two algorithms that you implement, 10 tours, one
for each city-set that I give you, detailing the best tours you have
found with any implementation of that algorithm (moreover, each
tour needs to be in a specifically named and formatted text file as
dictated in Section 4; so this amounts to 20 tour files)
? a one-page proforma (a pdf document) briefly describing the algorithms
you implemented and any enhancements you have made.
The content is as follows: the mark scheme is in Section 1; the FAQs in
Section 2; the algorithm tariffs in Section 3; how your tour files should be
formatted in Section 4; and finally some hints and tips as regards the core
algorithms in Section 5.
1
1 Mark scheme
The mark scheme is complicated so please read it carefully as it will
determine which algorithms you ultimately choose to implement and help
you plan your time. A list of FAQs follows in Section 2.
Marks will be awarded for:
(a) the sophistication of the algorithms that you implement
(b) the correctness of your basic implementations (AlgAbasic.py and
AlgBbasic.py)
(c) any enhancements you have made so as to obtain enhanced implementations
of your basic implementations (AlgAenhanced.py and
AlgBenhanced.py)
(d) the quality of the tours that you obtain.
I will measure ‘sophistication’ and ‘correctness’ as follows.
? ‘Sophistication’: Each algorithm has a tariff associated with it (see
Section 3) where this tariff gives the maximum number of marks that
you can secure with your basic implementation of the ‘vanilla’ version
of that particular algorithm (by ‘basic’ I mean the standard version
covered in lectures). The tariff is such that the more technically
complex the algorithm, the more marks you can secure.
? ‘Correctness’: I will run your basic implementations (AlgAbasic.py
and AlgBbasic.py) on 2 secret sets of cities that I will not divulge
to you beforehand. One city-set will have around 40 cities and the
other city-set will have around 100 cities.
Your basic implementations (AlgAbasic.py and AlgBbasic.py) need to be
correct.
? By ‘correct’ I mean that when I run your basic implementations on
my secret city-sets, a legal tour is produced for both city-sets.
However, . . . your ‘sophistication’ mark will be the maximum tariff from
your ‘correct’ basic implementations (I’ll say why in Section 2). Your ‘correctness’
mark will only be earned if both of your basic implementations
are correct (I’ll say why in Section 2).
? So, if you have only one ‘correct’ basic implementation then your
‘sophistication’ mark will be the tariff corresponding to the ‘correct’
basic implementation but you will not be awarded a ‘correctness’
mark. (Of course, if neither of your basic implementations is ‘correct’
then you will not be awarded either a ‘sophistication’ mark or a
‘correctness’ mark).
2
In addition, you can pick up extra ‘enhancement’ marks by enhancing
and experimenting with your basic implementations to obtain two enhanced
implementations (AlgAenhanced.py and AlgBenhanced.py).
? For example, you might try different versions of crossover for a genetic
algorithm and this experimentation might secure extra bonus marks
(at my discretion).
It is up to you as to whether you wish to try and enhance your basic implementations
(though once you have your basic implementations, it is not particularly
time-consuming to do a little bit of experimentation). Ideally, you
should hand in 2 basic implementations (AlgAbasic.py and AlgBbasic.py)
of two distinct TSP algorithms as well as 2 enhanced implementations
(AlgAenhanced.py and AlgBenhanced.py) of the same algorithms.
? Note that you are not allowed to implement 4 different TSP algorithms!
You will be awarded an ‘enhancement’ mark for each ‘correct’ enhanced implementation
(with ‘correct’ defined as above) but these marks will depend
upon how innovative I feel your enhancements are.
You will also receive a ‘basic quality’ mark corresponding to how good
the tours are that you have found. For each of the 10 given city-sets, you
should return: the best tour you have produced by any implementation of
your first algorithm (Algorithm A), enhanced or otherwise; and the best
tour you have produced by any implementation of your second algorithm
(Algorithm B), enhanced or otherwise. However, . . . the ‘basic quality’
mark will be determined only by the best tour you have found for each
of the 10 city-sets (regardless whether this is via an implementation of
Algorithm A or of Algorithm B; I’ll say why in Section 2).
You could also receive an ‘enhanced quality’ mark. For each algorithm,
I will run your basic implementation and your enhanced implementation
on my secret city-sets and depending upon how well your enhanced implementation
does in comparison with your basic implementation, I will award
extra marks (at my discretion); so, the ‘enhanced quality’ mark is derived
from both of your enhanced implementations.
? When I compare a basic implementation with an enhanced implementation,
please ensure that the parameters are identically set, e.g., if
your Algorithm A is a genetic algorithm then you should ensure that
the number of iterations, mutation probability, size of pupulation,
and so on, are identically set in AlgAbasic.py and AlgAenhanced.py
(failure to do so could mean you lose ‘enhancement quality’ marks).
The possible marks for each category follow:
? ‘sophistication’: the maximum obtainable is 10 but this will depend
upon the tariff of the algorithms implemented
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? ‘correctness’: if both basic implementations are ‘correct’ then a mark
of 4 is awarded, otherwise a mark of 0
? ‘enhancement’: there is a maximum of 3 marks available for each
enhanced implementation; so, the overall ‘enhancement’ mark is at
most 6
? ‘quality’: the maximum ‘basic quality’ mark available is 8 and the
maximum ‘enhanced quality’ mark available is 2; so, the overall ‘quality’
mark is at most 10.
Consequently, the maximum mark achievable is 30.
2 FAQs
? Student: Why do you insist on Python?
? Iain: Every student has completed Computational Thinking and so
can program in Python; this yields a ‘level playing field’ when it
comes to implementation. Also, restricting to Python-only leads to
fairer marking.
? Student: Why do you take the maximum tariff from two ‘correct’
implementations as the ‘sophistication’ mark ?
? Iain: On occasion, a more sophisticated algorithm may give worse
results than a more elementary algorithm. I want to reward both the
quality of the tours that you find and also your accomplishments in
coding up a more difficult algorithm. With things set up as above,
I encourage you to code up a more sophisticated algorithm without
harming your chances of getting good tours.
? So I guess that’s why you take the best overall tour found when you
give the ‘basic quality’ mark? So that we don’t harm the quality of
the tours we find if we choose to implement a more sophisticated algorithm?
? Iain: Correct!
? Student: And I suppose you ask us to implement two algorithms and
to experiment so that you can assess both our understanding of the
algorithms in the sub-module, through our capacity to code them up,
along with our ingenuity and deeper understanding in obtaining good
tours?
? Iain: Correct again! If all I asked you to do was to produce basic implementations
of two algorithms and produce some half-decent tours
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then there wouldn’t be much of a challenge (nor fun) in that. So, I
would like you to experiment. I understand that some of you will have
more time and inclination for this than others so I ensure that if all
you do is produce basic implementations and some half-decent tours
then this will suffice to pass the coursework, which I think is fair.
But I’m also giving you the opportunity to show me what you can do
and be rewarded for it. In the past, many students have enjoyed the
challenge of producing good tours.
? Student: Why do you only give us a ‘correctness’ mark if both basic
implementations give tours on your secret city-sets?
? Iain: I think it is reasonable that your two basic implementations
should both be correct. Don’t you agree? My definition of ‘correctness’
is not exactly challenging!
? Student: Why do you not just ask us to return the best tour we have
found by whatever algorithm for each of the 10 city-sets rather than
one tour for each algorithm?
? Iain: I get to see the profile of tours you have produced for each
algorithm. This allows me to have confidence that the tours you have
supplied to me are indeed produced by the implementations stated,
as I know (roughly) how different algorithms should perform. Also, I
will run your basic and enhanced implementations on the 10 city-sets
to provide me with a sanity check that your codes are what you say
they are.
? Student: What if we have worked hard to fine-tune our implementations
to perform really well on the 10 city-sets but when you run them
on your secret city-sets, they don’t do so well?
? Iain: There is a small chance that this might happen but if your
enhanced implementation improves things across many of the 10 citysets,
it is likely that it will improve things on my secret city-sets too.
Also, the ‘enhanced quality’ mark is relatively small.
? Student: Can you give me some illustrations of the different mark
awards depending upon what sort of a student I am?
? Iain: OK; here are some illustrations.
– Student A: Maybe you are hard-pushed and will not have the
time nor inclination for experimenting but correctly implement a
reasonably sophisticated algorithm at tariff 8 and a less complicated
algorithm at tariff 6; so, your ‘sophistication’ mark will be
5
8 and your ‘correctness’ mark will be 4. The lack of experimentation
means: that you get an ‘enhancement’ mark of 0; and that
you only obtained moderately good tours overall. Perhaps you
get a ‘basic quality’ mark of 5 and so an overall ‘quality’ mark
of 5. You would score 17/30 = 57% (a very solid lower-second
mark).
– Student B: Maybe you are struggling and cannot correctly implement
two algorithms but get a basic tariff-6 TSP algorithm
working, though the tours produced are not very good, resulting
in a ‘quality’ mark of 4. You would receive a ‘sophistication’
mark of 6, a ‘correctness’ mark of 0 and an ‘enhancement’ mark
of 0. Your total mark would be 10/30 = 34% (a fail mark).
– Student C: maybe you are like Student A but you are inclined
to experiment. Your enhanced impementations are correct and
moderately innovative; so you obtain an ‘enhancement’ mark of
3. The tours of the secret city-sets produced by your enhanced
implementations produce a modest improvement over those produced
by your basic implementations and you obtain an ‘enhanced
quality’ mark of 1. So, you would score 21/30 = 70% (a
first-class mark).
The moral of the story? Show ambition with your implementations
and experiment.
? Student: Is the assignment the same as last year ?
? Iain: No, it has changed. First, the city-sets are different to last
year; but more importantly you are being asked to do less work. Last
year, students felt that the amount of work required did not match
the credit available. This year, students no longer need to supply a
4-page report and I have also supplied to you all of the Python code
(skeleton code.py) required for reading and writing data files (you
should use this code as directed; it is well commented). I implemented
a basic greedy algorithm and a genetic algorithm in an afternoon . . .
and I can assure you that your programming skills are way better
than mine!
3 Algorithm tariff
Brute-force search: As you are doubtless aware, a brute-force search for
the TSP will only work for very, very small sets of cities; of course, when
it works, the tour obtained is optimal. (Note that it is not combinatorially
straightforward to generate all possible tours for checking.) Tariff: 6/10
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Basic greedy algorithm: The most obvious greedy algorithm is that obtained
by starting at some city and iteratively moving to the nearest city
to the last city visited. This is trivial to implement. Tariff: 5/10
Best-first search without heuristic data: Depending on how you choose your
evaluation function, you can implement a breadth-first search, a depth-first
search and various other algorithms. However, you should bear in mind that
a breadth-first search is demanding on memory and a depth-first search runs
the risk of not terminating (though this will depend upon how you define
your search problem). Of course, you can choose to refine a best-first search
by forcing termination under given circumstances. Tariff: 7/10
Iterative deepening: One way of getting round the memory requirements
of a breadth-first search is to undertake repeated depth-first searches with
bounds on the depth. Tariff: 8/10
Best-first search with heuristic data: You’ll have to build your own heuristic
functions as none are supplied. Tariff: 9/10
A* search: You’ll have to build your own heuristic functions as none are
supplied. Also, note that an A* search with a ‘good’ heuristic is optimal
and also that the TSP is NP-hard; so, if you don’t have a heuristic that
gives a good estimation then it is unlikely that you’ll get good tours. Of
course, you can choose to terminate an A* search under given user-defined
circumstances. Tariff: 10/10
Hill-climbing search: This is very easy to implement once you decide upon
the representation of the TSP as a search problem. Tariff: 6/10
Simulated annealing: This is moderately easy to implement once you decide
upon the representation of TSP as a search problem. Tariff: 7/10
Genetic algorithm: There are many ways in which you can represent the
TSP using a genetic algorithm. Tariff: 8/10
Some of the above algorithms lend themselves to experimentation more
than others so you might bear this is mind when making your choices.
In the past, many students have implemented algorithms they have
found themselves from books and the general literature, such as Ant Colony
Optimisation, Recursive Best-First Search and Beam Search. There are lots
of nature-inspired algorithms to choose from with new ones regularly being
devised. If you wish to implement an algorithm not covered in the course
(and I welcome this) then please e-mail me beforehand and I will supply
you with the tariff.
One final thing: I will be executing your implementations automatically
and it is possible that you choose parameters so that your implementations
(on my secret city-sets) take a long time to execute. When you hand your
implementations in, you should ensure that your implementations will take
no more than a minute or so on my secret city-sets. Typical parameters
that you will need to adjust are, for example, the number of iterations in
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a genetic algorithm, the temperature function in simulated annealing, and
so on. If an implementation of yours takes too long then it will be
killed and you will run the risk of losing a significant number of
marks.
4 The format of your submission
All submissions should be named using your username as follows. I illustrate
using the dummy username abcd12 but you should substitute your
own. If you don’t follow the rules below then you run the risk of
getting no marks!
All files should be in a folder called abcd12. Within this folder there
should be:
? 4 Python programs (.py) entitled AlgAbasic.py, AlgBbasic.py,
AlgAenhanced.py and AlgBenhanced.py which are the basic versions
of your two chosen algorithms, Algorithm A and Algorithm B, along
with the enhanced versions
? 10 tour-files (.txt) entitled AlgA012.txt, AlgA017.txt, AlgA021.txt,
AlgA026.txt, AlgA042.txt, AlgA048.txt, AlgA058.txt,
AlgA175.txt, AlgA180.txt and AlgA535.txt, with each tour-file
containing the best tour you have found using your first chosen algorithm,
Algorithm A, on the corresponding city-set
? 10 tour-files (.txt) entitled AlgB012.txt, AlgB017.txt, AlgB021.txt,
AlgB026.txt, AlgB042.txt, AlgB048.txt, AlgB058.txt,
AlgB175.txt, AlgB180.txt and AlgB535.txt, with each tour-file
containing the best tour you have found using your first chosen algorithm,
Algorithm B, on the corresponding city-set
? one proforma (.pdf) entitled AISearchProforma.pdf (I give you the
template in Word but please save it and return it in the form of a
pdf).
You need to ensure that all of your Python implementations can be
executed in command-line mode by supplying the tour-file, e.g.,
via the command
python AlgAbasic.py AlgA012.txt
If I cannot execute your implementations as above then I will not be able
to run your codes and you will lose marks. Using skeleton code.py will
enable such a command-line execution (and if the input file is omitted then
there is a default input file embedded in the code; take a look).
Also, when I run one of your implementations, in your folder abcd12,
via a command-line instruction such as
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? python AlgAbasic.py AISearchfile012.txt
the actual input file AISearchfile012.txt is assumed to reside in a folder
named city-files that is in the same folder as abcd12.
5 The data-files
The actual instances of the Travelling Salesman problem (more precisely,
the symmetric Travelling Salesman problem, where the distance from city
x to city y, denoted (x, y), is always the same as the distance from city y
to city x, that is, (y, x)) will be given in the following form (the cities are
always named 1, 2, . . . , n).
NAME = <string = name-of-the-data-file>,
SIZE = <integer n = the number of cities in the instance>,
<list-of-integers d1, d2, d3, . . . , dm> where the list consists of
the distances between cities (1, 2),(1, 3), . . . ,(1, n),
then the distances between cities (2, 3),(2, 4), . . . ,(2, n),
. . .
and finally the distance between the cities (n ? 1, n).
Commas ‘,’ are used as delimitters in data-files; carriage returns, end-of-line
markers, spaces, etc., should be ignored.
So, for example, the instance with 5 cities where: the city 1 is at the origin;
the city 2 is 3 miles north; the city 3 is 4 miles east; the city 4 is 3 miles
south; and the city 5 is 4 miles west, and all distances are the Euclidean
distances between cities, is encoded as the city-file AISearchsample.txt:
NAME = AISearchsample,
SIZE = 5,
3, 4, 3, 4,
5, 6, 5,
5, 8,
5
With reference to the remark above, re: delimitters, the above city-file
could well be presented with no carriage returns or spaces, etc., as simply
NAME=AISearchsample,SIZE=5,3,4,3,4,5,6,5,5,8,5
Note that in general a given instance need not be based on the Euclidean
distances of a collection of cities on the plane. You should assume that a
given distance between two cities is a non-negative integer (and so it could
well be the case that the distance between two distinct cities is 0).
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As stated earlier, I supply you with the (well commented) skeleton
of a Python program called skeleton code.py. The code reads a
given input file (in the format above) and supplies the number
of cities (num cities in skeleton code.py) and a two-dimensional
symetric array (distance matrix in skeleton code.py) containing
the distances between cities. You should use skeleton code.py.
The data-files that you will have to execute your code on will be called
AISearchfile012.txt, AISearchfile017.txt, AISearchfile021.txt,
AISearchfile026.txt, AISearchfile042.txt, AISearchfile048.txt,
AISearchfile058.txt, AISearchfile175.txt, AISearchfile180.txt
and AISearchfile535.txt. The numeric digits denote the number of cities
in the particular instance.
When you have computed a legitimate tour of some set of cities,
there is code in skeleton code.py that checks that this tour is legitimate
and writes the tour to an output-file in the correct format.
You should supply your tours to me in the file obtained by using
this code (after renaming it).
Some remarks and hints
As mentioned earlier, the following methods are available to you (as studied
in the course):
? brute-force search
? basic greedy algorithm (‘nearest-neighbour’)
? best-first search without heuristic data
? greedy best-first search
? A?
search
? hill-climbing search
? simulated annealing
? genetic algorithm.
There is a little bit of work to do as regards the implementation of each
method and here is a little bit of help.
10
Brute-force search
Here is a hint as to how all tours in an n-city Travelling Salesman instance
can be generated. Each tour is stored in a 1-dimensional list T of size n,
where the elements are all distinct and come from {1, 2, . . . , n}. The tour
stored in T is T[1], T[2], . . . , T[n], T[1]. In fact, we can similarly store a tour
of the m ≤ n cities {1, 2, . . . , m} in T[1], T[2], . . . , T[m], with T[m + 1] =
T[m + 2] = . . . = T[n] = 0.
Our procedure gen(T,m) takes as input a tour of m cities, x1, x2, . . . ,
xm, x1, say, held in the list T (as above), and proceeds as follows.
? If m = n then we compare the length of the tour T (of n cities) with
the length of the shortest tour found so far and if T is shorter then
we remember T and its length.
? If m < n then gen(T,m) generates all tours of the cities {1, 2, . . . , m,
m+ 1} by inserting the value m+ 1 in location 1, then location 2, . . .,
then location m, then location m + 1 of T (so that the cities coming
after m+ 1 are ‘shifted’ along the array). Interleaved with generating
each tour, which we refer to as T
0
, we recursively call the procedure
gen(T
0,m + 1).
In more detail, gen(T,m) is as follows.
if m == n then
calculate the length of the tour T and if it is shorter
than the best tour found so far, remember T and its
length as the best tour found so far
else
for i = 1 to m + 1 do
T
0 = T with the city m + 1 inserted into location i
call gen(T
0,m + 1)
T
0 = T with the city m + 1 removed from location i
fi
Thus, the following pseudo-code generates and tests all possible tours, given
the distance-file of n cities.
T = [1, 0, 0, . . . , 0] %initialize tour T as [1]
m = 1 %initialize number of cities of tour T as 1
call gen(T,m)
output the shortest tour found
Although I don’t recommend that you implement a brute-force search,
brute-force searches are good for checking optimal values in small cases;
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also generating all combinatorial possibilities comes up regularly and it is
useful to know how to do this. If you do implement a brute-force search
then you can always choose to kill an execution and take the best tour you
have found up until that point as a guide.
Best-first, greedy best-first and A?
search
The Travelling Salesman Problem needs to be realised as a search problem.
One way of doing this is to have the set of all lists of distinct cities as the
states together with the lists of n distinct cities augmented with the start
city (and so a state is a list of between 0 and n cities, or a list of n + 1
cities where the first n are distinct and the last city equals the first). There
is one action with a state t
0 being a successor of a state t if the list t
0
is
the partial tour t extended with one new city (not appearing in t), or if t
has length n and t
0
is t augmented with the first city of t. The step-cost
associated with any transition from state t to state t
0
is the cost of moving
from the final city in the list t to the final city of the list t
0
. The initial
state is the list t0 consisting of just the start city and a goal state is a list
of n + 1 cities. An optimal solution is thus a path from the initial state to
a goal state of minimal cost.
There are a number of heuristic functions available for the Travelling Salesman
Problem. One of these is the heuristic h where, given a state t =
x1, x2, . . . , xr, h(t) is defined to be the minimal step-cost of moving to
a state of the form t
0 = x1, x2, . . . , xr, xr+1 (that is, always move to the
nearest legal city from where you are; if there is no city to move to then
h(t) = 0).
Another heuristic is as follows. Given some state t, your heuristic function
h(t) is the sum of the distance of the closest city c that has not been visited
to the last city of the partial tour t plus the distance of any other unvisited
city (different from c) to the start city (if there aren’t enough unvisited
cities to apply this rule then the heuristic value is 0). This heuristic reflects
that you want your next city to be visited to be close to the current city
but that you don’t want to be left with a city that is a long way from the
start city.
Yet another heuristic h is as follows. Given a state t = x1, x2, . . . , xm, h(t)
is defined to be the minimal step-cost of moving to a state t
0
, where t
0
is t
with some new city inserted somewhere within t, e.g., if t = 1, 5, 4, 7 then
t
0 might be 1, 5, 6, 4, 7. If this heuristic is to be used then the transition
function (above) needs to be amended to allow such transitions.
Bear in mind that A?
search gives optimal solutions (under mild circumstances)
and so unless you have a brilliant heuristic function (which is
unlikely) then this method will only work on small instances. I have no
idea how the above heuristics will pan out on the instances given!
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Hill-climbing search and simulated annealing
Here, the states might be the set of all possible tours of n cities, and one
state t
0 might be a successor of another state t if a swap of the positions
of two (or more!) of the cities in the tour t results in the tour t
0
. The
heuristic cost function of a state might be the length of the tour. There
are numerous other definitions of a successor function.
Genetic algorithms
In order to formulate the Travelling Salesman Problem for solution by a
genetic algorithm, we need to define our population. One way of doing this
is to define the population as a set of tours of n cities, represented as strings
of length n. The fitness of a member of the population might be just the
length of the tour. We now need to come up with a notion of mutation
and crossover. One way of defining a mutation is just to randomly swap
the positions of two cities within a tour (though there are many others).
Defining a notion of crossover is more difficult. However, given two tours
t = x1, x2, . . . , xn and t0 = x01, x02, . . . , x0n
, we could define a new tour as
follows.
? Randomly choose some i ∈ {1, 2, . . . , n ? 1} and form the strings
(note that these might not be tours as some cities might be missing
and some repeated).
? Scan through s and make a list of the cities not appearing in s and
a list of the locations containing repeated cities (these lists have the
same length). Replace every repetition with a missing city (according
to some user-defined strategy). Do the same for s0.? Hence, we obtain two tours s and s0, and we take the crossover as the
shortest one.
For those really interested, there is a paper:
? P. Larranaga, C.M.H. Kuijpers, R.H. Murga, I. Inza and S. Dizdarevic,
Genetic Algorithms for the Travelling Salesman Problem: A Review
of Representations and Operators, Artificial Intelligence Review
13 (1999) 129–170
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that discusses genetic algorithms for the TSP.
Please note: the above hints are just suggestions and you might care to
come up with your own ideas. Also, it will be up to you to (experimentally)
vary parameters (e.g., the different probabilities in a genetic algorithm or
a simulated annealing algorithm) to improve your solutions.
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