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TitleArchitectural Design of Multi-Agent Systems: Technologies and Techniques
ISBN 139781599041087
Author
LanguageEnglish
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Total Pages442
Document Text Contents
Page 2

Architectural Design of
Multi-Agent Systems:
Technologies and Techniques

Hong Lin
University of Houston – Downtown, USA

Hershey • New York
INFORMAT ION SC IENCE REFERENCE

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MAITS

can avoid this limitation of the HMM. Moreover,
the HMM is trained using maximum likelihood
estimation (MLE). The MLE method assumes the
data sets have a statistical distribution. However,
the statistical distribution of biometric or network
data is unknown. To avoid these limitations of
the current HMM, we use the temporal Markov
modeling (TMM) method presented a later sec-
tion. The TMM has an observed Markov chain
where cluster indices are states to represent the
dependence of each observation on its predeces-
sor. The hidden Markov chain in the HMM is
still employed in the TMM (Tran, 2004b; Tran
& Pham, 2005).

We train the TMM using the quasi-likeli-
hood estimation (QLE) method. The reason is
that computer network data and biometric data
do not have a statistical distribution, therefore
the current MLE method is not appropriate. The
QLE method requires only assumptions on the
mean and variance functions rather than the full
likelihood.

The method used to determine the similarity
score S is usually formulated as a problem of
statistical hypothesis testing. For a given input
biometric X and a claimed identity, the problem
formulation is to test the null hypothesis H0: X is
from the claimed identity, against the alternative
hypothesis H: X is not from the claimed identity. If
the probabilities of both the hypotheses are known
exactly, according to Neyman-Pearson’s Lemma,
the optimum test to decide between these two
hypotheses is a likelihood ratio test. Consider a
false rejection of a claimed user caused in the cur-
rent likelihood ratio-based scores because of the
use of the background model set. The likelihood
values of the background models are assumed to
be equally weighted. However, this assumption is
often not true as the similarity measures between
each background model and the claimed model
might be different. A likelihood transformation is
proposed to overcome this problem. An alterna-
tive approach is the use of prior probabilities as
mixture weights for background models.

On the other hand, a false acceptance of an
untrue user can arise because of the relativity of
the ratio-based values. For example, the two ratios
of (0.06 / 0.03) and (0.00006 / 0.00003) have the
same value. The first ratio can lead to a correct
decision whereas the second one is not likely to
because both likelihood values are very low.

Estimation of Prior Probabilities

The prior probabilities can be estimated directly
from the training data. Let X be the training data
set used to train the model set = { 1, 2, …, M}.
The prior probabilities P( i ) satisfies:

1
( | ) 1

M

i
i

P (1)

Maximising P(X | ) over P( i ) using the La-
grangian method, the updated prior probabilities

( | )iP is calculated from P( i ) as follows:

1

( | , ) ( | )
( | )

( | , ) ( | )

i i
i M

k k
k

P X P
P

P X P

or:

1

1

( | , ) ( | )1
( | )

( | , ) ( | )

T
t i i

i M
t

t k k
k

P x P
P

T P x P

(2)

The second estimation method in (2) is called
frame-level prior estimation to distinguish it from
the first estimation.


Temporal Models

The use of codewords in a codebook as states of
a Markov chain was developed by Dai (1995) for

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isolated word recognition. The proposed research
extends this idea to a general framework, and
hence it can apply to HMMs, GMMs, and their
fuzzy versions.

Let O = o1, o2, …, oT denote a stochastic pro-
cess in discrete time. The probability that the
t-th variable ot takes the value wt depends on the
values taken by all the previous variables. Using
the Markov assumption, the probability that the
t-th variable ot takes the value wt depends on the
immediately preceding value ot – 1 as follows:

1 1 2 2 1 1( | , ,..., )t t t t t tP o w o w o w o w- - - -= = = = =

1 1( | )t t t tP o w o w- -= =
(3)

The stochastic processes based on this assump-
tion are termed Markov processes. Markov chains
are Markov processes for which state variables
are restricted to have a finite number of values,
and the probability )|( 11 -- == tttt wowoP is as-
sumed to be invariant in time. The sequence W
= w1, w2, …, wT represents a sequence of states.
In order to apply Markov chains theory to tem-
poral models, the feature vectors are considered
outputs of Markov chains. Let X = x1, x2, …, xT
be a sequence of feature vectors that represents
a spoken or written word, a feature vector xt
can be mapped to either a member of the set of
codewords V ={v1, v2, …, vK} obtained by vector
quantisation (VQ) modelling or a member of the
set of Gaussian components G ={g1, g2, …, gK}
by GMM. The state sequence W may be either a
codeword sequence w1 = vi, w2 = vj, …, wT = vm or
a Gaussian sequence w1 = gi, w2 = gj, …, wT = gm,
where 1 < i, j, m < K. Therefore, each codeword in
V or each Gaussian in G is a specific state of the
Markov chain. The state-transition probabilities
of the Markov chain are used to represent the
dependence between acoustic features.

It should be noted that each observation in the
sequence O is assumed to be statistically depen-
dent on its predecessor in the proposed temporal
model. In the HMM, observations in the sequence

O are assumed to be independent. Therefore the
temporal model has avoided the limitation of the
HMM. There are 2 types of temporal models:
Temporal Gaussian Mixture Model and Temporal
Hidden Markov Model. The later can be further
classified into discrete temporal hidden Markov
model (DTHMM) and continuous temporal hid-
den Markov model (CTHMM) (Tran, 2004b).

Methods

Let lc be the claimed speaker model and l be a
model representing all other possible speakers,
that is, impostors. Let P(X |lc) and P(X |l) be the
likelihood functions of the claimed speaker and
impostors, respectively. For a given input utter-
ance X and a claimed identity, a basic hypothesis
test is between the null hypothesis H0: X is from
lc and the alternative hypothesis H1: X is from l.
The optimum test to decide between these two
hypotheses is a likelihood ratio test given by:

0

0

accept H( | )
( )

reject H( | )
cP XS X

P X
>

= 
≤

(4)

S(X) is regarded as the claimed speaker’s
similarity score. While the model for H0 can be
estimated using the training data from the claimed
speaker, the model for impostors is less defined.
The first approach is to use a subset of impostors’
models that is representative of the population
close (similar) to the claimed speaker. This subset
has been called cohort set or background speaker
set. Depending on the approximation of P(X | l) in
(4) by the likelihood functions of the background
model set P(X | li), i = 1, …, B, we obtain different
normalisation methods (Burileanu, Moraru, Bo-
jan, Puchiu, & Stan, 2002). The second approach
is to pool speech from several speakers to train a
single universal background model (Reynolds et
al., 2000). The third approach is the hybrid cohort-

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Index

P
patient agent (PA) 320
peer-to-peer (P2P)

client 117
network 116–125

performance
analysis 136
prediction toolkit (PACE) 184

personal
agent 57
data 122
scheduling management 57

pharmacy agent (PhA) 320
phishing 195
polling 66
port scanning 194–195
pre-mortem-based computer forensics agent 192
process planning agent (PPA) 300
producer-consumer problem 32
productivity 1
programming language 3, 27, 107

Q
quality 16
quasi-likelihood estimation (QLE) 197, 203

R
rationality 27
recommendation cycle 254
recommender system 254, 260
registry server agent (RSA) 299
reinforcement learning (RL) 184, 239–252

problem (RLP) 264
reliability 16
repetition 262
rescheduling 302
reusability 16, 108
RiskMan (risk management) 364–384
risk management 364–384
robot control paradigm 344
robust intelligent control 342–363
robustness 346
runtime engine 368

S
scalability 16
scalable fault tolerant agent grooming environment

(SAGE) 144–174
scanning 194

scheduling 48, 122, 290–291
agent 298, 300
problem 50

scripted agent 369
security 16, 108, 118, 192–212
seed selection 292
self-healing 342
self-organization 276, 316, 318
semantic language (SL) 13
sense

-act (SA) 344
-plan-act (SPA) 344

108
Shakey 344
shared memory 30
similarity knowledge 261
SimMaster 369, 376
simple network management protocol (SNMP) 127
Slammer virus 193
software

agent 180
customization 100

spam 195
sportier critique 257
SpyWare 195
standard set 62
stationary agent 320
storage device card 320
story engine 372
structured thinking 4
supplier management agent (SMA) 298–299
supply chain management (SCM) 288–312, 313–

341
survivability 198
swarm intelligence approach 274–287
swarms 316
system log monitoring agent 192

T
T-Cham 27
task

-scheduling algorithm 128
allocation 274–287
decomposition 130
manipulation 128
reduction 215–218

temporal
logic 37
properties 37

time slot 49
tourism 274

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Index

trainee
agent 369
interface (TI) 376

transmission control protocol/internet protocol
(TCP/IP) 117

tuple space 31

U
uniform resource locator (URL) 123
unit critique 256
unstructured thinking 4
user

feedback 253
knowledge 260

V
vector quantization 197
Venn Diagram 31
veracity 27

30
virtual

agent cluster 152
organization (VO) 176
reality training 364, 366

virus 193
visual management agent 149
vocabulary knowledge 260
voice authentication 201

W
122

working agent 192
worms 193
WS-Resource 177

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