# A Novel Parser Design Algorithm Based on Artificial Ants

**ABSTRACT** This article presents a unique design for a parser using the ant colony optimization algorithm. The paper implements the intuitive thought process of human mind through the activities of artificial ants. Traditional methods of designing parser involve calculation of different sets like FIRST, FOLLOW, GOTO, CLOSURE and parsing or precedence relation tables. Calculation of these tables and sets are both memory and time consuming. Moreover, the grammar concerned has to be converted into a context-free, non-redundant and unambiguous one. The scheme presented here uses a bottom-up approach and the parsing program can directly use ambiguous or redundant grammars. We allocate a node corresponding to each production rule present in the given grammar. Each node is connected to all other nodes (representing other production rules), thereby establishing a completely connected graph susceptible to the movement of artificial ants. Ants are endowed with some memory that they use to carry the sentential form derived from the given input string to the parser. Each ant tries to modify this sentential form by the production rule present in the node and upgrades its position until the sentential form reduces to the start symbol S. Successful ants deposit pheromone on the links that they have traversed through in inverse proportion of the number of hops required to complete a successful tour. Eventually, the optimum path is discovered by the links carrying maximum amount of pheromone concentration. The design is simple, versatile, robust and effective and obviates the calculation of the above mentioned sets and precedence relation tables. Further advantages of our scheme lie in i) ascertaining whether a given string belongs to the language represented by the grammar, and ii) finding out the shortest possible path from the given string to the start symbol S in case multiple routes exist.

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**ABSTRACT:**Neural networks are frequently used as adaptive classifiers. This research represents an attempt to measure the “neural complexity” of any regular set of binary strings, that is, to quantify the size of a recurrent continuous-valued neural network that is needed for correctly classifying the given regular set. Our estimate provides a predictor that is superior to the size of the minimal automaton that was used as an upper bound so far. Moreover, it is easily computable, using techniques from the theory of rational power series in non-commuting variables.Neurocomputing. 01/1997; - SourceAvailable from: psu.edu[Show abstract] [Hide abstract]

**ABSTRACT:**We describe an artificial ant colony capable of solving the travelling salesman problem (TSP). Ants of the artificial colony are able to generate successively shorter feasible tours by using information accumulated in the form of a pheromone trail deposited on the edges of the TSP graph. Computer simulations demonstrate that the artificial ant colony is capable of generating good solutions to both symmetric and asymmetric instances of the TSP. The method is an example, like simulated annealing, neural networks and evolutionary computation, of the successful use of a natural metaphor to design an optimization algorithm.Biosystems 02/1997; 43(2):73-81. · 1.58 Impact Factor - IEEE Computational Intelligence Magazine 01/2006; 1(4):28-39. · 4.63 Impact Factor

Page 1

A Novel Parser Design Algorithm Based on

Artificial Ants

Deepyaman Maiti1, Ayan Acharya2, Amit Konar3

Department of Electronics and Telecommunication

Engineering

Jadavpur University

Kolkata: 700032, India

1deepyamanmaiti@gmail.com, 2masterayan@gmail.com,

3konaramit@yahoo.co.in

Janarthanan Ramadoss4

Department of Information Technology

Jaya Engineering College

Chennai: 600017, India

4srmjana_73@yahoo.com

Abstract— This article presents a unique design for a parser

using the Ant Colony Optimization algorithm. The paper

implements the intuitive thought process of human mind through

the activities of artificial ants. Traditional methods of designing

parser involve calculation of different sets like FIRST,

FOLLOW, GOTO, CLOSURE and parsing or precedence

relation tables. Calculation of these tables and sets are both

memory and time consuming. Moreover, the grammar concerned

has to be converted into a context-free, non-redundant and

unambiguous one. The scheme presented here uses a bottom-up

approach and the parsing program can directly use ambiguous or

redundant grammars. We allocate a node corresponding to each

production rule present in the given grammar. Each node is

connected to all other nodes (representing other production

rules), thereby establishing a completely connected graph

susceptible to the movement of artificial ants. Ants are endowed

with some memory that they use to carry the sentential form

derived from the given input string to the parser. Each ant tries

to modify this sentential form by the production rule present in

the node and upgrades its position until the sentential form

reduces to the start symbol S. Successful ants deposit pheromone

on the links that they have traversed through in inverse

proportion of the number of hops required to complete a

successful tour. Eventually, the optimum path is discovered by

the links carrying maximum

concentration. The design is simple, versatile, robust and effective

and obviates the calculation of the above mentioned sets and

precedence relation tables. Further advantages of our scheme lie

in i) ascertaining whether a given string belongs to the language

represented by the grammar, and ii) finding out the shortest

possible path from the given string to the start symbol S in case

multiple routes exist.

amount of pheromone

Keywords— Ant Colony Optimization; Parser Design;

Intuitive thought process; context-free grammar; ambiguous

grammar; redundancy.

I.

INTRODUCTION

Formally, a context-free grammar is a four-tuple (T,N,S,P),

where T is a set of terminal symbols, describing the allowed

words, N is a set of non-terminals describing sequences of

words and forming constructs. A unique non-terminal S is the

start symbol. P, the set of production rules, describes the

relationship between the non-terminal and terminal symbols,

defining the syntax of the language. A series of regular

expressions can be used to describe the set of allowable words,

and acts as the basis for the description of a scanner, also

called a lexical analyzer.

Parsing is the process whereby a given program is matched

against the grammar rules to determine (at least) whether or

not it is syntactically correct. As part of this process the

various parts of the program are identified with the

corresponding constructs in the grammar, so that program

elements such as declarations, statements and expressions can

then be identified. So, a parser for a grammar G is a program

that takes as input a string ω and produces as output either a

parse tree for ω, if ω is a sentence of G, or an error message

indicating that ω is not a sentence of G.

As well as forming the front-end of a compiler, a parser is

also the foundation for many software engineering tools, such

as pretty-printing, automatic generation of documentation,

coding tools such as class browsers, metrication tools and

tools that check coding style. Automatic re-engineering and

maintenance tools, as well as tools to support refactoring and

reverse-engineering also typically require a parser as a front-

end. The amenability of a language’s syntax for parser

generation is crucial in the development of such tools.

This article deals with a novel parser design algorithm

based on Ant Colony Optimization (ACO) algorithm. The

paper has been structured into 6 sections. In section II, we

present a brief introduction to previous works on parsers.

Section III provides a comprehensive detail of the ACO

metaheuristic. We present our scheme in section IV. Section V

highlights the advantages of our scheme. Finally, the

conclusions are listed in section 6.

II.

PREVIOUS WORKS ON PARSERS

Two most common forms of parsers are operator

precedence and recursive descent. Two newer methods,

which are more general than these and more firmly grounded

Page 2

in grammar theory, are: LL parsing, which really is a table-

based variant of recursive descent, and LR parsing [1], [2].

The automatic generation of parsing programs from a context-

free grammar is a well-established process, and various

algorithms such as LL (ANTLR and JavaCC) and LALR

(most notably yacc [3]) can be used). Application of software

metrices to the measurement of context-free grammar is

studied in [4]. The construction of a very wide-coverage

probabilistic parsing system for natural language, based on LR

parsing techniques is attempted in [5].

In [6], a design for a reconfigurable frame parser to

translate radio protocol descriptions to asynchronous

microprocessor cores is described. [7] presents the design and

implementation of a parser/solver

programming problems (SDPs).

for semi-definite

[8] describes the development of a parser for the C#

programming language. [9], [10] study the pattern matching

capabilities of neural networks for an automated, natural

language partial parser.

III.

ANT COLONY OPTIMIZATION METAHEURISTICS

This section presents an overview of ACO algorithm which

is the basis of our design. ACO ([11], [12], [16]) is a paradigm

for designing metaheuristic algorithms for combinatorial

optimization (CO) problems like Travelling Salesperson

Problem ([13]), Graph Coloring Problem ([14]), Quadratic

Assignment Problem ([15]) etc. The algorithms are inspired by

the trail laying and following behavior of natural ants. While

roaming from food sources to destination or vice versa, some

of the ant species mark their paths by a chemical called

pheromone. Other foraging ants can detect pheromone and

choose, in probability, paths marked by stronger pheromone

concentration. Thus pheromone trail helps the ants find the

way followed by their team members towards food source or

nest.

The real challenge of solving any CO problem by ACO is to

map the problem to a representation that can be used by

artificial ants to perform solution. In fact, any minimization

problem can be represented as a triple (S, f, Ω) where S is the

set of candidate solutions, f(s,t) is the objective function over

the elements s∈S and Ω(t) represents the problem constraints.

The goal in such problems is to find a globally optimum

solution s* such that f(s*,t)≤ f(s,t) for all s∈S. To solve such

problems using the ACO metaheuristic, the problem is

mapped to an environment that can be represented by

connected graph GC=(C, L) ([15]), where C={c1, c1,…., cN} is

the finite set of components and L is the set of links that

connects fully the components in C. The states of the problem

are defined in terms of sequences x=<ci,cj,….,ch,…> of finite

length over the elements of C. X is the set of all possible

states. The set of candidate solutions S is a subset of X. S?

specifies the set of feasible candidate solution which is again a

subset of S. the set of optimal solution

is associated with each candidate solution s∈S. Artificial ants

build solutions by performing random walks on this

construction graph to search for optimal solutions s*∈ S*.

Connection lij∈L has associated pheromone trail τij and a

heuristic value ηij. Heuristic value represents a priori

information about the problem instance and pheromone trail

conveys information to subsequent ants about the experiences

gained by their predecessors.

*

SS

⊆?. A cost g(s,t)

Each ant k has some memory Mk which is utilized for

building feasible solution and retracing the path travelled

backward. Ant starts from a starting state xs

feasible solution until termination conditions are not met.

While in state xr= < xr-1, i >, if termination condition is not

satisfied, ant moves to a node j in its neighborhood Nk(xr), i.e.

to state < xr, j >. Choice of this node j is guided by a

probability based selection approach given by the following

equation:

k and build

k

N ( )

∈

k

k

β

0

k

i

0

0

β

αα

ijijikik

k: k

β

ij

β

ij

β

αα

ijik

β

αα

ijik

ik

ik

(τ ).(η )/

⎧

⎪

⎪

⎪

⎨

⎪

⎪

⎪

⎩

(τ).(η) ifq<q

P (j)1, if(τ ).(η )=max{(τ).(η):k N ( )}with q>q

r

x

∈

(1)

0, if(τ ).(η ) max{(τ).(η):k N ( )}with q>q

r

x

∈

rx

⎫

⎪

⎪

⎪

⎬

⎪

⎪

⎪

⎭

=

≠

∑

with Pi

ant k. Nk(xr) being the neighborhood of ant k when it is at node

i or in other words in state xr). 0<q0<1 is a pseudo random

factor deliberately introduced for path exploration. q, a

random number generated every time ant updates its position,

also lies between 0 and 1. α, β are the weights for pheromone

concentration and visibility. After building a solution

successfully, ant can retrace its path and deposit pheromone on

the links that it has traversed through.

k(j) is the probability of selecting node j after node i for

Therefore, ACO algorithm can be thought of as interplay of

three procedures as depicted in the following pseudo code.

Procedure ACO metaheuristic

Schedule Activities

•

•

•

Construct Solution

Update pheromone

Daemon Actions (optional)

end Schedule Activities

end Procedure

IV. OUR ALGORITHM

This section describes our design in detail. Let

ωabbcde

=

be a string which may or may not belong to

L(G), the set of strings identified by the grammar G. The

given production rules are:

→

, 2.A

1.SaAcBeAb

→

, 3. Ae

→

Page 3

4.Ab

→

, 5. BBdc

→

, 6. Bd

→

The parser will check if ω can be reduced to S (the start

symbol) by using the production rules. The quickest process to

verify that is to use the production rules in the sequence shown

below:

=

→

(Rule 6),

→

(Rule 2),

ωabbcde

→

→

abbcBeaAbcBe

(Rule 4),

aAcBeS

(Rule 1)

So, the parser program changes sub-strings of ω which

matches the RHS of a production rule by the LHS of that

production rule to get a new string. In this way it continues

until a new string is obtained that matches/ is the start symbol

S. If ultimately S is obtained as a new string,ω

is not.

L(G)

∈

, else it

Now, suppose, we are given to check whether the string

ωabbcde

=

belongs to L(G) or not under the given

production rules (1) - (6), i.e. we have to check whether ωcan

be reduced to S or not using the given production rules. While

implementing ACO algorithm to solve this problem, we first

map the entire problem into an environment represented by a

connected graph. This is, as already mentioned in section 3,

the first step of solving any optimization problem by a discrete

optimization algorithm like ACO.

Corresponding to each production rule, which we can access

to reduce ω to S, we create a node. Every node stores a

production rule after splitting it into two halves. One is the

LHS of the rule and the other is its RHS. Suppose, rule (1):

S aAcBe

→

is needed to be stored in a node. Then, first we

split the rule into two halves and store the LHS part S and the

RHS part aAcBe separately. Therefore, each node contains

exactly two stacks for storing the two halves of the production

rule that it represents. A single node is connected to every

other node, which implies that there is a provision of

traversing from one node to any other node.

Artificial ants are endowed with some attributes to move

through this connected graph. Each ant is provided with the

starting string ω . This string is stored in a stack in ant’s

memory. In the inception, ants are placed randomly on nodes

and each ant tries to use the stacks already stored in the node

in which it is placed. The procedure can be illustrated using

the problem string "abbcde" and the rule B

ant placed in the node representing the rule B

the expression "abbcde"and tries to find whether any sub-

string of "abbcde" matches with the string corresponding to

the RHS expression “d”. If there is a match, the ant replaces

the sub-string of ωabbcde

=

with the other string present in

the node (LHS of the production rule). Here an ant finds that

the string “d” matches with a sub-string stored in its memory.

Therefore it modifies the string stored in its memory

to"abbcBe".

d

→

(rule 6). An

d

starts with

→

Ant’s transition from one node to another is guided by a

probability based selection approach given in the following

equation:

∑

0

k

i

k

i

0

k

i

0

ijik

k

k: k Ni

ij

ijik

ik

(τ ) /(τ) if q<q

P (j)1, if τ =max{(τ

⎨

⎪

⎪

⎪

⎩

):kN } with q >q(2)

0,if τmax{(τ):kN }with q >q

∈

⎧

⎪

⎪

⎪

⎫

⎪

⎪

⎪

⎬

⎪

⎪

⎪

⎭

=∈

≠∈

and tries to match the modified string that it contains now in

its memory with the strings stored in that node. If ant finds the

strings useful so that either of them matches with any sub-

string of the expression stored in ant’s memory, ant modifies

the expression with the other string stored in that node as

before and moves to the next node. If an ant does not find the

information in the current node useful, it stops moving in the

graph (i.e. it becomes inactive). Each time an ant updates its

position (i.e. moves from one node to the other), it checks the

expression that it is modifying and the expression that it has to

arrive at (which is S). If these strings match, it indicates that

the ant has discovered steps through which S can be arrived at

starting from ωand the ant stops moving. There is, however,

no restriction in visiting a particular node more than once

because in process of deriving one expression from the other,

we might require to use a rule more than once.

The difference between (1) and (2) is that here we do not

consider any heuristic information. In the beginning, the

search space is covered

concentration. So, ants do not have any idea as how to move

through the graph. It, therefore, selects the next node randomly

with uniform pheromone

Figure 1. Pictorial representation of our scheme

B

d

B

Bdc

A

b

A

e

A

Ab

S

aAcBe

ab

bc

Be

aA

bc

Be

aA

cB

e

S

w=

abb

cde

Ant’s

Path

Page 4

But an ant counts the number of hops it requires to move

from the starting node to the ending node and deposits

pheromone on the links in inverse proportion of the number of

hops. It implies that ant, which arrives at S from the starting

expression using minimum number of rules, deposits

maximum amount of pheromone on the links that it has

traversed through. As the algorithm progresses, only a few

links, which are conducive in guiding the ants towards an

optimal solution, receive increasing amount of pheromone

which eventually leads to the exploration of the shortest

possible steps to check the validity of the given string. Figure

(1) shows a visual representation of the entire scheme.

V.

ADVANTAGES OF PROPOSED SCHEME

This section highlights the advantages of our scheme over all

the existing algorithms of designing a parser. The benefits are

summarized below:

1. We do not need to use up resources for calculating

FIRST, FOLLOW, GOTO or CLOSURE sets or parsing or

precedence relation tables which are required by more

advanced types of parsers. Nor do we require to get rid of

back-tracking. Since there are numerous ants, the

appropriate production rule to be used is found easily. So

we do not need to left-factor to eliminate back-tracking,

since the elimination of back-tracking is not needed at all.

We can directly work with ambiguous grammars.

2. There is no need to eliminate left-recursion either, since

one ant will invariably find the correct path; and when one

does, the others will soon follow suit. So we do not

require working with a context-free or a non-redundant

grammar. This is a huge simplification to the parsing

problem.

3. Our scheme is based on the intuitive method of human

thinking, and thus conceptually simpler, and easier to

visualize.

While it is true that the scheme 'could' have been implemented

by exploring paths from ω to S randomly, the use of a

stochastic optimization strategy, namely ACO is justified due

to the following two conditions:

i) if ω is NOT a legal string of L(G), it is difficult to

ascertain the fact without using a systematic search policy.

ii) using an optimization algorithm gives the shortest route

from ω to S; this is essential to increase the speed of

operation of the parser, especially when there are multiple

routes, which is the general case, and the usual case for

complex applications.

VI. CONCLUSIONS AND SCOPE OF FUTURE WORK

The many advantages of the proposed parsing scheme point

towards the fact that this approach will be suitable for parsing

complex expressions, such as those encountered in natural

language analysis applications. We use the very basic bottom-

up approach, so the scheme is conceptually simple. The use of

the ACO metaheuristic ensures that we can use ambiguous and

redundant grammars. In the future, we plan to use the ACO

algorithm to design more advanced parser types.

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