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An agent-driven semantical identifier using radial basis neural
networks and reinforcement learning
C. Napoli, G. Pappalardo, and E. Tramontana

arXiv:1409.8484v1 [] 30 Sep 2014

PUBLISHED ON: Proceedings of the XV Workshop ”Dagli Oggetti agli Agenti”
BIBITEX:
@inproceedings{Napoli2014Anagent
year={2014},
issn={1613-0073},
url={http://ceur-ws.org/Vol-1260/},
booktitle={Proceedings of the XV Workshop ”Dagli Oggetti agli Agenti”},
title={An agent-driven semantical identifier using radial basis neural networks and reinforcement learning},
publisher={CEUR-WS},
volume={1260},
author={Napoli, Christian and Pappalardo, Giuseppe and Tramontana, Emiliano},
}

Published version copyright c 2014
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An agent-driven semantical identifier using radial
basis neural networks and reinforcement learning
Christian Napoli, Giuseppe Pappalardo, and Emiliano Tramontana
Department of Mathematics and Informatics
University of Catania, Viale A. Doria 6, 95125 Catania, Italy
{napoli, pappalardo, tramontana}@dmi.unict.it
Abstract—Due to the huge availability of documents in digital
form, and the deception possibility raise bound to the essence of
digital documents and the way they are spread, the authorship
attribution problem has constantly increased its relevance. Nowadays, authorship attribution, for both information retrieval and
analysis, has gained great importance in the context of security,
trust and copyright preservation.
This work proposes an innovative multi-agent driven machine
learning technique that has been developed for authorship attribution. By means of a preprocessing for word-grouping and timeperiod related analysis of the common lexicon, we determine a
bias reference level for the recurrence frequency of the words
within analysed texts, and then train a Radial Basis Neural
Networks (RBPNN)-based classifier to identify the correct author.
The main advantage of the proposed approach lies in the generality of the semantic analysis, which can be applied to different
contexts and lexical domains, without requiring any modification.
Moreover, the proposed system is able to incorporate an external
input, meant to tune the classifier, and then self-adjust by means
of continuous learning reinforcement.

I.

I NTRODUCTION

Nowadays, the automatic attribution of a text to an author,
assisting both information retrieval and analysis, has become
an important issue, e.g. in the context of security, trust and
copyright preservation. This results from the availability of
documents in digital form, and the raising deception possibilities bound to the essence of the digital reproducible contents,
as well as the need for new mechanical methods that can
organise the constantly increasing amount of digital texts.
During the last decade only, the field of text classification
and attribution has undergone new developement due to the
novel availability of computational intelligence techniques,
such as natural language processing, advanced data mining
and information retrieval systems, machine learning and artificial intelligence techniques, agent oriented programming,
etc. Among such techniques, Computer Intelligence (CI) and
Evolutionary Computation (EC) methods have been largely
used for optimisation and positioning problems [1], [2]. In [3],
agent driven clustering has been used as an advanced solution
for some optimal management problems, whereas in [4] such
problems are solved for mechatronical module controls. Agent
driven artificial intelligence is often used in combination with
advanced data analysis techniques in order to create intelligent
control systems [5], [6] by means of multi resolution analysis [7]. CI and parallel analysis systems have been proposed
in order to support developers, as in [8], [9], [10], [11],

where such a classification and analysis was applied to assist
refactoring in large software systems [12], [13], [14], [15].
Moreover, CI and techniques like neural networks (NNs)
have been used in [16], [17] in order to model electrical
networks and the related controls starting by classification
strategies, as well as for other complex physical systems
by using several kinds of hybrid NN-based approaches [18],
[19], [20], [21]. All the said works use different forms of
agent-based modeling and clustering for recognition purposes,
and these methods efficiently perform very challenging tasks,
where other common computational methods failed or had
low efficiency or, simply, resulted as inapplicable due to
complicated model underlying the case study. In general,
agent-driven machine learning has been proven as a promising
field of research for the purpose of text classification, since
it allows building classification rules by means of automatic
learning, taking as a basis a set of known texts and trying to
generalise for unknown ones.
While machine learning and NNs are a very promising
field, the effectiveness of such approaches often lies on the
correct and precise preprocessing of data, i.e. the definition
of semantic categories, affinities and rules used to generate a
set of numbers characterising a text sample, to be successively
given as input to a classifier. Typical text classification, e.g.
by using NNs, takes advantage of topics recognition, however
results are seldom appropriate when it comes to classify people
belonging to the same social group or who are involved
in a similar business (e.g. the classification of: texts from
different scientists in the same field of research, the politicians
belonging to the same party, texts authored by different people
using the same technical jargon).
In our approach we devise a solution for extracting from the
analysed texts some characteristics that can express the style of
a specific author. Obtaining this kind of information abstraction
is crucial in order to create a precise and correct classification
system. On the other hand, while data abound in the context
of text analysis, a robust classifier should rely on input sets
that are compact enough to be apt to the training process.
Therefore, some data have to reflect averaged evaluations that
concern some anthropological aspects such as the historical
period, or the ethnicity, etc. This work satisfies the above
conditions of extracting compact data from texts since we
use a preprocessing tool for word-grouping and time-period
related analysis of the common lexicon. Such a tool computes
a bias reference system for the recurrence frequency of the
word used in the analysed texts. The main advantage of this
choice lies in the generality of the implemented semantical

REFERENCE
DATABASE

TEXT
DATABASE

TRAINING
SET

KNOWN
PREPROCESSING

BIASING
NEW
DATA

UNKNOWN

RBPNN
with
reinforcement
learning

LOCAL
EXTERNAL
TEXT
DATABASE

WORDNET
LEXICON

Fig. 1. A general schema of the data flow through the agents of the developed
system.

identifier, which can be then applied to different contexts and
lexical domains without requiring any modification. Moreover,
in order to have continuous updates or complete renewals of
the reference data, a statically trained NN would not suffice
to the purpose of the work. For these reasons, the developed
system is able to self-correct by means of continuous learning
reinforcement. The proposed architecture also diminishes the
human intervention over time thanks to its self-adaption properties. Our solution comprises three main collaborating agents:
the first for preprocessing, i.e. to extract meaningful data from
texts; the second for classification by means of a proper Radial
Basis NN (RBNN); and finally, one for adapting by means of
a feedforward NN.
The rest of this paper is as follows. Section II gives
the details of the implemented preprocessing agent based on
lexicon analysis. Section III describes the proposed classifier
agent based on RBNNs, our introduced modifications and
the structure of the reinforcement learning agent. Section IV
reports on the performed experiments and the related results.
Finally, Section V gives a background of the existing related
works, while Section VI draws our conclusions.

Algorithm 1: Find the group a word belongs to and count
occurrences
Start,
Import a speech into T ext,
Load dictionary into W ords,
Load group database into Groups,
thisW ord = Text.get();
while thisW ord do
thisGroup = Words.search(thisW ord);
if !thisGroup then
Load a different Lexicon;
if Lexicon.exist(thisW ord) then
W ords.update();
Groups.update();
end
else
break;
end
end
while Text.search(thisW ord) do
Groups.count(thisGroup);
end
thisW ord = Text.get();
end
Export W ords and Groups,
Stop.

The fundamental steps of the said analysis (see also Algorithm 1) are the followings:
1)
2)
3)
4)
5)
6)

II.

E XTRACTING SEMANTICS FROM LEXICON

Figure 1 shows the agents for our developed system: a
preprocessing agent extracts characteristics from given text
parts (see text database in the Figure), according to a known
set of words organised into groups (see reference database);
a RBPNN agent takes as input the extracted characteristics,
properly organised, and performs the identification on new
data, after appropriate training. An additional agent, dubbed
adaptive critic, shown in Figure 6, dynamically adapts the
behaviour of the RBPNN agent when new data are available.
Firstly, preprocessing agent analyses a text given as input
by counting the words that belong to a priori known groups of
mutually related words. Such groups contain words that pertain
to a given concern, and have been built ad-hoc and according
to the semantic relations between words, hence e.g. assisted
by the WordNet lexicon1 .
1 http://wordnet.princeton.edu

7)
8)
9)

import a single text file containing the speech;
import word groups from a predefined database, the
set containing all words from each group is called
dictionary;
compare each word on the text with words on the
dictionary;
if the word exists on the dictionary then the relevant
group is returned;
if the word has not been found then search the
available lexicon;
if the word exists on the lexicon then the related
group is identified;
if the word is unkown, then a new lexicon is loaded
and if the word is found then dictionary and groups
are updated;
search all the occurrences of the word in the text;
when an occurrence has been found, then remove it
from the text and increase the group counter.

Figure 2 shows the UML class diagram for the software
system performing the above analysis. Class Text holds a text
to be analysed; class Words represents the known dictionary,
i.e. all the known words, which are organised into groups given
by class Groups; class Lexicon holds several dictionaries.
III.

T HE RBPNN CLASSIFIER AGENT

For the work proposed here, we use a variation on Radial
Basis Neural Networks (RBNN). RBNNs have a topology similar to common FeedForward Neural Networks (FFNN) with
BackPropagation Training Algorithms (BPTA): the primary

Lexicon
Text
get()
exist()

get()

Words
search()
update()
Groups
Filter
service()

search()
count()
update()

FFNN

RBNN

PNN

Our RBPNN

Fig. 2. UML class diagram for handling groups and counting words belonging
to a group.

difference only lies in the activation function that, instead of
being a sigmoid function or a similar activation function, is a
statistical distribution or a statistically significant mathematical
function. The selection of transfer functions is indeed decisive
for the speed of convergence in approximation and classification problems [22]. The kinds of activation functions used
for Probabilistic Neural Networks (PNNs) have to meet some
important properties to preserve the generalisation abilities of
the ANNs. In addition, these functions have to preserve the
decision boundaries of the probabilistic neural networks. The
selected RBPNN architecture is shown in Figure 4 and takes
advantage from both the PNN topology and the Radial Basis
Neural Networks (RBNN) used in [23].
Each neuron performs a weighted sum of its inputs and
passes it through a transfer function f to produce an output.
This occurs for each neural layer in a FFNN. The network
can be perceived as a model connecting inputs and outputs,
with the weights and thresholds being free parameters of the
model, which are modified by the training algorithm. Such
networks can model functions of almost arbitrary complexity
with the number of layers and the number of units in each
layer determining the function complexity. A FFNN is capable
to generalise the model, and to separate the input space in
various classes (e.g. in a 2D variable space it is equivalent to
the separation of the different semi-planes). In any case, such
a FFNN can only create a general model of the entire variable
space, while can not insert single set of inputs into categories.
On the other hand, a RBNN is capable of clustering the inputs
by fitting each class by means of a radial basis function [24],
while the model is not general for the entire variable space, it
is capable to act on the single variables (e.g. in a 2D variable
space it locates closed subspaces, without any inference on the
remaining space outside such subspaces).
Another interesting topology is provided by PNNs, which
are mainly FFNNs also functioning as Bayesian networks
with Fisher Kernels [25]. By replacing the sigmoid activation
function often used in neural networks with an exponential
function, a PNN can compute nonlinear decision boundaries
approaching the Bayes optimal classification [26]. Moreover,
a PNN generates accurate predicted target probability scores
with a probabilistic meaning (e.g. in the 2D space it is
equivalent to attribute a probabilistic score to some chosen
points, which in Figure 3 are represented as the size of the
points).
Finally, in the presented approach we decided to combine
the advantages of both RBNN and PNN using the so called

Fig. 3. A comparison between results of several types of NNs, Our RBPNN
includes the maximum probability selector module

RBPNN. The RBPNN architecture, while preserving the capabilities of a PNN, due to its topology, then being capable
of statistical inference, is also capable of clustering since the
standard activation functions of a PNN are substituted by radial
basis functions still verifying the Fisher kernel conditions
required for a PNN (e.g. such an architecture in the 2D variable
space can both locate subspace of points and give to them a
probabilistic score). Figure 3 shows a representation of the
behaviour for each network topology presented above.
A. The RBPNN structure and topology
In a RBPNN both the input and the first hidden layer
exactly match the PNN architecture: the input neurones are
used as distribution units that supply the same input values
to all the neurones in the first hidden layer that, for historical
reasons, are called pattern units. In a PNN, each pattern unit
performs the dot product of the input pattern vector v by a
weight vector W(0) , and then performs a nonlinear operation
on the result. This nonlinear operation gives output x(1) that
is then provided to the following summation layer. While a
common sigmoid function is used for a standard FFNN with
BPTA, in a PNN the activation function is an exponential, such
that, for the j-esime neurone the output is


||W(0) · v||
(1)
(1)
xj ∝ exp
2σ 2
where σ represents the statistical distribution spread.
The given activation function can be modified or substituted
while the condition of Parzen (window function) is still satisfied for the estimator N̂ . In order to satisfy such a condition
some rules must be verified for the chosen window function in
order to obtain the expected estimate, which can be expressed
as a Parzen window estimate p(x) by means of the kernel K
of f in the d-dimensional space S d


n
P
x−xi
1
pn (x) = n1
K
d
hn
h
i=1

R
Sd

K(x)dx = 1

n

(2)

P1

Σ

υ

.
.
.

.
.
.

.
.
.

.
.
.
.
.
.
.
.
.

Σ
.
.
.

.
.
.

P*

Pn

Σ

Fig. 4. A representation of a Radial Basis Probabilistic Neural Network with
maximum probability selector module

where hn ∈ N is called window width or bandwidth parameter
and corresponds to the width of the kernel. In general hn ∈ N
depends on the number of available sample data n for the
estimator pn (x). Since the estimator pn (x) converges in mean
square to the expected value p(x) if
lim hpn (x)i = p(x)

n→∞

(3)
lim var (pn (x))

n→∞

=

0

where hpn (x)i represents the mean estimator values and
var (pn (x)) the variance of the estimated output with respect
to the expected values, the Parzen condition states that such
convergence holds within the following conditions:
sup K(x) < ∞
x

lim xK(x)

=

0

lim hnd

=

0

|x|→∞

Fig. 5.
Setup values for the proposed RBPNN: NF is the number of
considered lexical groups, NS the number of analysed texts, and NG is the
number of people that can possibly be recognised as authors.

units work as in the neurones of a linear perceptron network.
The training for the output layer is performed as in a RBNN,
however since the number of summation units is very small
and in general remarkably less than in a RBNN, the training
is simplified and the speed greatly increased [27].
The output of the RBPNN (as shown in Figure 4) is given
to the maximum probability selector module, which effectively
acts as a one-neuron output layer. This selector receives as
input the probability score generated by the RBPNN and
attributes to one author only the analysed text, by selecting
the most probable author, i.e. the one having the maximum
input probability score. Note that the links to this selector are
weighted (with weights adjusted during the training), hence the
actual input is the product between the weight and the output
of the summation layer of the RBPNN.

(4)
n→∞

lim nhnd

n→∞

= ∞

In this case, while preserving the PNN topology, to obtain
the RBPNN capabilities, the activation function is substituted
with a radial basis function (RBF); an RBF still verifies all
the conditions stated before. It then follows the equivalence
between the W(0) vector of weights and the centroids vector of
a radial basis neural network, which, in this case, are computed
as the statistical centroids of all the input sets given to the
network.
We name f the chosen radial basis function, then the new
output of the first hidden layer for the j-esime neurone is


||v − W(0) ||
(1)
(5)
xj , f
β
where β is a parameter that is intended to control the distribution shape, quite similar to the σ used in (1).
The second hidden layer in a RBPNN is identical to a PNN,
it just computes weighted sums of the received values from the
preceding neurones. This second hidden layer is called indeed
summation layer: the output of the k-esime summation unit is
X
(2)
(1)
xk =
Wjk xj
(6)
j

where Wjk represents the weight matrix. Such weight matrix
consists of a weight value for each connection from the j-esime
pattern units to the k-esime summation unit. These summation

B. Layer size for a RBPNN
The devised topology enables us to distribute to different
layers of the network different parts of the classification task.
While the pattern layer is just a nonlinear processing layer, the
summation layer selectively sums the output of the first hidden
layer. The output layer fullfills the nonlinear mapping such as
classification, approximation and prediction. In fact, the first
hidden layer of the RBPNN has the responsibility to perform
the fundamental task expected from a neural network [28]. In
order to have a proper classification of the input dataset, i.e. of
analysed texts to be attributed to authors, the size of the input
layer should match the exact number NF of different lexical
groups given to the RBPNN, whereas the size of the pattern
units should match the number of samples, i.e. analysed texts,
NS . The number of the summation units in the second hidden
layer is equal to the number of output units, these should match
the number of people NG we are interested in for the correct
recognition of the speakers (Figure 5).
C. Reinforcement learning
In order to continuously update the reference database for
our system, a statically trained NN would not suffice for the
purpose of the work. Since the aim of the presented system is
having an expanding database of text samples for classification
and recognition purpose, the agent driven identification should
dynamically follow the changes in such a database. When
a new entry is made then the related feature set and biases
change, it implies that also the RBPNN should be properly
managed in order to ensure a continuous adaptive control for
reinforcement learning. Moreover, for the considered domain

it is desirable that a human supervisor supply suggestions, expecially when the system starts working. The human activities
are related to the supply of new entries into the text sample
database, and to the removal of misclassifications made by the
RBPNN.
We used a supervised control configuration (see Figure 6),
where the external control is provided by the actions and
choices of a human operator. While the RBPNN is trained with
a classical backpropagation learning algorithm, it is also embedded into an actor-critic reinforcement learning architecture,
which back propagates learning by evaluating the correctness
of the RBPNN-made choices with respect to the real word.
Let ξ be the error function, i.e. ξ = 0 for the results
supported by human verification, or the vectorial deviance for
the results not supported by a positive human response. This
assessment is made by an agent named Critic. We consider
the filtering step for the RBPNN output, to be both: Critic,
i.e. a human supervisor acknowledging or rejecting RBPNN
classifications; or Adaptive critic, i.e. an agent embedding a
NN that in the long run simulates the control activity made by
the human Critic, hence decreasing human control over time.
Adaptive critic needs to learn, and this learning is obtained by
a modified backpropagation algorithm using just ξ as error
function. Hence, Adaptive critic has been implemented by
a simple feedforward NN trained, by means of a traditional
gradient descent algorithm so that the weight modification
∆wij is
∆wij = −µ

∂ξ
∂ξ ∂ f˜i ∂ ũi
= −µ
∂wij
∂ f˜i ∂ ũi ∂wij

(7)

The f˜i is the activation of i-esime neuron, ũi is the i-esime
input to the neurone weighted as
X
ũi =
wij f˜j (ξi )
(8)
j

The result of the adaptive control determines whether to
continue the training of the RBPNN with new data, and
whether the last training results should be saved or discarded.
At runtime this process results in a continuous adaptive learning, hence avoiding the classical problem of NN polarisation
and overfitting. Figure 6 shows the developed learning system
reinforcement. According to the literature [29], [30], [31], [32],
straight lines represent the data flow, i.e. training data fed
to the RBPNN, then new data inserted by a supervisor, and
the output of the RBPNN sent to the Critic modules also
by means of a delay operator z −1 . Functional modifications
operated within the system are represented as slanting arrows,
i.e. the choices made by a human supervisor (Critic) modify
the Adaptive critic, which adjust the weight of its NN; the
combined output of Critic and Adaptive critic determines
whether the RBPNN should undergo more training epochs and
so modify its weights.

z -1
Critic
Features
DATA
RBPNN

Fig. 6. The adopted supervised learning model reinforcement. Slanting arrows
represent internal commands supplied in order to control or change the status
of the modules, straight arrows represent the data flow along the model, z −1
represents a time delay module which provides 1-step delayed outputs.

characteristics (see Section II), then such results have been
given to the classification agent. The total number of text
samples was 344, and we used 258 of them for training the
classification agent and 86 for validation. The text samples,
both for training and validation, were from different persons
that have given a speech (from A. Einstein to G. Lewis), as
shown in Figure 7.
Given the flexible structure of the implemented learning
model, the word groups are not fixed and can be modified,
added or removed over time by an external tuning activity. By
using the count of words in a group, instead of a word-by-word
counts, the multi-agent system realises a statistically driven
classifier that identifies the main semantic concerns regarding
the text samples, and then, attributes such concerns to the most
probable person.
The relevant information useful in order to recognise the
author of the speech is usually largely spread over a certain
number of word groups that could be indication of the cultural
extraction, heritage, field of study, professional category etc.
This implies that we can not exclude any word group, a priori,
while the RBPNN could learn to automatically enhance the
relevant information in order to classify the speeches.
Figure 7-left shows an example of the classifier performances for results generated by the RBPNN (before the filter
implemened by the probabilistic selector). Since the RBPNN
results have a probability between 0 and 1, then the shown
performance is 0 when a text was correctly attributed (or
not attributed) to a specific person. Figure 7-right shows the
performances of the system when including the probabilistic
selector. For this case, a boolean selection is involved, then
correct identifications are represented as 0, false positive
identifications as −1 (black marks), and missed identifications
as +1 (white marks).
For validation purposes, Figure 7-(left and right) shows
results according to e:
e = y − ỹ

IV.

E XPERIMENTAL SETUP

The proposed RBPNN architecture has been tested using
several text samples collected from public speeches of different
people both from the present and the past era. Each text sample
has been given to the preprocessing agent that extract some

Adaptive
critic

(9)

where e identifies the performance, ỹ the classification result,
and y the expected result. Lower e (negative values) identify
an excess of confidence in the attribution of a text to a person,
while greater e (positive values) identify a lack of confidence
in that sense.

Fig. 7. The obtained performance for our classification system before (left) and after (right) the maximum probability selector choice. The mean grey color
represents the correct classifications, while white color represents missed classification and black color false classifications.

The system was able to correctly attribute the text to the
proper author with only a 20% of missing assignments.
V.

R ELATED W ORKS

Several generative models can be used to characterise
datasets that determine properties and allow grouping data
into classes. Generative models are based on stochastic block
structures [33], or on ‘Infinite Hidden Relational Models’ [34],
and ‘Mixed Membership Stochastic Blockmodel’ [35]. The
main issue of class-based models is the type of relational
structure that such solutions are capable to describe. Since
the definition of a class is attribute-dependent, generally the
reported models risk to replicate the existing classes for each
new attribute added. E.g. such models would be unable to
efficiently organise similarities between the classes ‘cats’ and
‘dogs’ as child classes of the more general class ‘mammals’.
Such attribute-dependent classes would have to be replicated as
the classification generates two different classes of ‘mammals’:
the class ‘mammals as cats’ and the class ‘mammals as dogs’.
Consequently, in order to distinguish between the different
races of cats and dogs, it would be necessary to further
multiply the ‘mammals’ class for each one of the identified
race. Therefore, such models quickly lead to an explosion of
classes. In addition, we would either have to add another class
to handle each specific use or a mixed membership model, as
for crossbred species.
Another paradigm concerns the ’Non-Parametric Latent
Feature Relational Model’ [36], i.e. a Bayesian nonparametric
model in which each entity has boolean valued latent features
that influence the model’s relations. Such relations depend on
well-known covariant sets, which are neither explicit or known
in our case study at the moment of the initial analysis.
In [37], the authors propose a sequential forward feature
selection method to find the subset of features that are relevant
to a classification task. This approach uses novel estimation of
the conditional mutual information between candidate feature
and classes, given a subset of already selected features used as
a classifier independent criterion for evaluating feature subsets.

In [38], data from the charge-discharge simulation of
lithium-ions battery energy storage are used for classification
purposes with recurrent NNs and PNNs by means of a theoretical framework based on signal theory.
While showing the effectiveness of the neural network
based approaches, in our case study classification results are
given by means of a probability, hence the use of a RBPNN,
and an on-line training achieved by reinforcement learning.
VI.

C ONCLUSION

This work has presented a multi-agent system, in which an
agent analyses fragments of texts and another agent consisting
of a RBPNN classifier, performs probabilistic clustering. The
system has successfully managed to identify the most probable
author among a given list for the examined text samples. The
provided identification can be used in order to complement
and integrate a comprehensive verification system, or other
kinds of software systems trying to automatically identify
the author of a written text. The RBPNN classifier agent
is continuously trained by means of reinforcement learning
techniques in order to follow a potential correction provided by
an human supervisor, or an agent that learns about supervision.
The developed system was also able to cope with new data
that are continuously fed into the database, for the adaptation
abilities of its collaborating agents and their reasoning based
on NNs.
ACKNOWLEDGMENT
This work has been supported by project PRIME funded
within POR FESR Sicilia 2007-2013 framework and project
PRISMA PON04a2 A/F funded by the Italian Ministry of
University and Research within PON 2007-2013 framework.
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