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Artificial Intelligence Techniques – Some Attractive and Powerful Computational Tools

for Geodesy and Geomatics

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Artificial Intelligence Techniques – Some Attractive and Powerful Computational Tools for Geodesy and Geomatics

I.D. Doukas

Aristotle University of Thessaloniki

Department of Civil Engineering, Division of Geotechnical Engineering

Laboratory of Geodesy and Geomatics, GR-541 24, Univ. Box #465, Hellas

Abstract

There is a variety of definitions about Artificial intelligence (AI). In this paper, the fol-

lowing definition is selected advisedly: AI is the development of computer systems to solve

difficult problems, which can not be solved by an exhaustive examination of all possible

solutions since these may be too many. From this point of view, as this definition guides, a

brief review of some of AI-tools is attempted with two targets: To present some of the tools

(older-“classics” and younger-“exotics”) and to explore their penetrating potentiality in the

opportune fields of Geodesy and Geomatics.

1. Introduction-Some useful general terms

The terminology below can be found in many (slight or not) variations accord-

ing to different scientific views, books, Internet and other sources. In any case, the

selected terminology here is fully consistent with the related bibliography of this

paper. Furthermore, this bibliography is rich enough to allow the reader for deeper

explorations of the extremely wide scientific field of Artificial Intelligence.

The term intelligence is always defined as the ability to learn effectively, to

react adaptively, to make proper decisions, to communicate in language or images

in a sophisticated way, and to understand.

Artificial intelligence (AI), the science and engineering of making intelligent

machines, is a term coined by Prof. John McCarthy in 1956. AI is a general idiom

which includes, among others, evolutionary algorithms, genetic programming, arti-

ficial neural networks, cellular automata and fuzzy systems (Rajabi et al., 2009),

(Kasabov, 1998), (Kalogirou, 2007), (McCarthy, 2007). The main objectives of AI

are to develop methods and systems for solving problems, usually solved by the

intellectual activity of humans, for example, image recognition, language and

speech processing, planning, and prediction, thus enhancing computer information

systems; and to develop models which simulate living organisms and the human

86 I.D. Doukas

brain in particular, thus improving our understanding of how the human brain

works.

Knowledge (alternatively, the problem of dealing with knowledge), was for

many years a research field for psychology and sociology. The evolutions of AI

transformed the knowledge-problem to a problem of representation of knowledge

in computers.

Knowledge Engineering (KE) is a branch of Artificial Intelligence (AI). It is

an area that mainly concentrates on activities with knowledge (including knowl-

edge acquisition, representation, validation, inference and explanation). It is a dis-

cipline devoted to integrating human knowledge in computer systems, which

means to building knowledge-based systems (Ding, 2001).

Heuristic (a word with Greek roots) means discovery. Heuristic methods are

based on experience, rational ideas, and rules of thumb. Heuristics are based more

on common sense than on mathematics. Heuristics are useful, for example, when

the optimal solution needs an exhaustive search that is not realistic in terms of

time. In principle, a heuristic does not guarantee the best solution, but a heuristic

solution can provide a tremendous shortcut in cost and time (Kasabov, 1998).

Soft Computing (SC) (Zadeh 1994), (Kecman, 2001), (a term sometimes met

as Softcomputing), is a concept introduced by Iranian professor Asgar Lotfi Zadeh

in the early 1990s. SC is an evolving collection of methodologies for the represen-

tation of the ambiguity in human thinking. SC refers to a collection of computa-

tional techniques in computer science, artificial intelligence, machine learning and

some engineering disciplines, which attempt to study, model, and analyze very

complex phenomena: those for which more conventional methods have not yielded

low cost, analytic, and complete solutions. Earlier computational approaches could

model and precisely analyze only relatively simple systems. More complex sys-

tems arising in biology, medicine, engineering, earth sciences, ecology, the hu-

manities, management sciences, and similar fields often remained intractable to

conventional mathematical and analytical methods. The core methodologies of SC

are Fuzzy Logic (FL), Neuro-Computing (NC) (or neural modeling, brain theory,

(Artificial) Neural Networks (ANN)), Probabilistic Reasoning (PR), Evolutionary

Computation (EC) (especially the Genetic Algorithms (GA) and the Evolution

Strategies (ES)), chaotic systems, belief networks, parts of learning theory. SC tar-

gets at exploiting the tolerance for imprecision and uncertainty, approximate rea-

soning, and partial truth in order to achieve tractability, robustness, and low-cost

solutions.

Overall, there is a rather permanent confusion about the terms AI, SC, KE and

their components-branches. A few (among others) reasons are: their differences in

age of appearance on the scientific stage, the involvement of other disciplines (such

as computer science, mathematics etc.) and the fact that there is not yet an elucida-



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tion and official classification of the sectors/branches of AI. For example, FL,

ANN, etc., both appear to belong to the SC and to the AI. In any case, for conven-

ience these overlaps will be resolved here, by considering for the rest of the paper

all the relevant tools as tools of AI/SC/KE.

Generally speaking, in order AI to achieve modeling human intelligence, there

are two exemplars:

(1) The symbolic: It is based on symbol manipulation. Symbolic AI rule-based

systems can be used when the problem knowledge is in the form of well de-

fined, rigid rules; no adaptation is possible, or at least it is difficult to imple-

ment (Kasabov, 1998). A symbolic system consists of two sets:

a) A set of elements (or symbols) which can be used to construct more com-

plicated elements or structures. The symbols have semantic meanings. They

represent concepts or objects.

b) A set of processes and rules, which, when applied to symbols and struc-

tures, produce new structures.

(2) The subsymbolic. It is based on Neurocomputing (or Neuro Computing (NC)).

NC is the study of brain function in terms of the information processing prop-

erties of the structures that make up the nervous system. It is an interdiscipli-

nary science that links the diverse fields of neuroscience, cognitive science and

psychology with electrical engineering, computer science, mathematics and

physics.

Hard Computing (HC) (i.e. conventional («traditional») computing), requires

a precisely stated analytical model and often a lot of computation time. HC mainly

is based on formal logical systems, such as sentential logic and predicate logic, or

rely heavily on computer-aided numerical analysis (the finite element analysis is a

representative example). HC has its foundations on binary logic, crisp systems,

numerical analysis and crisp software. There is no need to mention here that many

analytical models are valid for ideal cases and real-world problems exist in a non-

ideal environment.

Comparing the contrasts between SC and HC, SC resemble biological processes

more closely than HC. Also, the SC techniques often complement each other. The

premises of HC are: precision, certainty and rigor. The premises of SC are: The

real world problems are pervasively imprecise and uncertain. Precision and cer-

tainty carry a cost. For a particular given problem, HC strives for exactness and full

truth, while SC exploits the given tolerance of imprecision, partial truth, and uncer-

tainty. Finally, inductive reasoning plays a larger role in SC than in HC (Du and

Swamy, 2006), (Kirankumar and Jayaram, 2008), (Syed and Cannon, 2004).

88 I.D. Doukas

2. Areas and tools of AI/SC/KE

Most important tools of AI/SC/KE are:

2.1 Artificial �eural �etworks (A��) (Du and Swamy, 2006), (Rajabi et

al., 2009), (Rao, 1995), (Taylor and Smith, 2006):

An Artificial Neural Network (ANN) is an information processing paradigm

that is inspired by the way biological nervous systems (such as the brain) process

information. A brain consists of a large number of cells, referred to as "neurons". A

neuron receives impulses from other neurons through a number of "dendrites". De-

pending on the impulses received, a neuron may send a signal to other neurons,

through its single "axon", which connects to dendrites of other neurons. Like the

brain, ANNs consist of elements, each of which receives a number of inputs, and

generates a single output, where the output is a relatively simple function of the

inputs. Research on ANNs dates back to the 1940s; their discipline is well devel-

oped with wide applications in almost all areas of science and engineering. The key

element of this paradigm is the novel structure of the information processing sys-

tem. Main purpose of this model is simulation of an ideal form of basic processors

in brain and self continuity, signal processing and self-organization abilities. From

a more mathematical point of view, an ANN is composed of a large number of

highly interconnected processing elements (neurons) working in unison to solve

specific problems. Information is passed between these units, along interconnec-

tions. An incoming connection has two values associated with it, an input value

and a weight. The output of the unit is a function of the summed values of the in-

puts, multiplied by the weights. Learning in biological systems involves adjust-

ments to the synaptic connections that exist between the neurons, also an attribute

of ANNs.

When ANNs are used to predict numeric values, they typically have just one

output. This is because single-output nets are more reliable than multiple-output

nets, and almost any prediction problem can be addressed using single-output nets.

On the other hand, ANNs have multiple outputs when used for classifica-

tion/category prediction.

Statistical methods can be used when statistically representable data are avail-

able and the underlying type of goal function is known. ANNs are applicable when

problem knowledge includes data without having any knowledge as to what the

type of the goal function might be; they can be used to learn heuristic rules after

training with data. ANNs also can be used to implement existing fuzzy or symbolic

rules, providing a flexible approximate reasoning mechanism (Kasabov, 1998).

ANNs provide an alternative to more traditional statistical methods. ANNs are used

for function approximation (like Linear Regression) and ANNs are used for classi-

fication (like Discriminant Analysis and Logistic Regression), as well.



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Main advantages of ANNs:

• Strong learning and generalization capabilities. After learning the unknown

relations from given data set(s), an ANN can then predict, by generalization,

outputs for new samples that were not included in the learning sample set.

• Dealing with a given problem (linear or not), they absorb information (knowl-

edge) in a direct manner through their training-phase. They can handle very

large and/or complex systems, with high-dimensional situations.

• They are capable for parallel and distributed processing, associative memory,

vector quantization and optimization problems, wide fields of applications far

beyond the usual problem of function approximation.

• They are capable to deal with (either numerical or analogue) data, if there are no

alternative means to use for these data (p.e. due data form, due the high-

dimensionality of the data etc.).

• An ANN is a «black-box» that directly learns the internal relations of an un-

known system. Consequently, the ANN method is model free. This «black-box»

character allows the user not to need high-level mathematical knowledge and

experience.

• The acquired information-knowledge is finally stored, in a compact form, inside

the trained network. Furthermore, located there the knowledge, it offers to the

user easy access and management.

• They can be tolerant to transient bad data, adapt to accommodate true system

change, and based upon the complexity of the architecture, usually have redun-

dancy for internal knowledge because of the weights and internal connections.

Although there could be noisy data (a most usual situation), the ANN solutions

can be robust. This characteristic is one of the most important credentials of

ANNs.

• Being in the generalization mode over a set of not-used in the training mode

data, they can deliver high accuracy.

• An ANN is a model of computations that can be implemented in various types

of computer hardware.

Main disadvantages of ANNs:

• The information contained into the training set of data, has to (ideally) be spread

evenly covering all of the system’s range.

• The design on ANNs lacks strong supportive theory.

• An acceptable solution is not always guaranteed. Additionally, if the outcome is

an acceptable solution, its rationalization has not plenty of opportunities.

• The processing could obtain as outcome «memory», not «intelligence». It is

about the overtraining-problem, and although the ANN seems trained well, ac-

tually is just working on remembering solutions and specially training data,

with paucity for real data.

90 I.D. Doukas

2.2 Fuzzy Systems (FS) (Du and Swamy, 2006), (Rajabi et al., 2009),

(Syed and Cannon, 2004):

Fuzzy theory was first introduced in 1965 by Iranian professor Asgar Lotfi

Zadeh, (at Berkley University, USA) to implement in uncertainty condition. This

theory is able to provide several of phenomena, variables and the ground to deduc-

tion, control and decision making in uncertainty condition. Fuzzy logic (FL) pro-

vides a means for treating uncertainty and computing with words. This is especially

useful to mimic human recognition, which skillfully copes with uncertainty. Fuzzy

systems (FS) are conventionally created from explicit knowledge expressed in the

form of fuzzy rules, which are designed based on experts’ experience. A FS can

explain its action by fuzzy rules. FS can also be used for function approximation.

The synergy of FL and ANNs generates neurofuzzy systems, which inherit the

learning capability of ANNs and the knowledge-representation capability of FS.

Fuzzy logic (FL) has two different meanings.

� In a narrow sense, FL is a logical system, which is an extension of multivalued

logic.

� In a wider sense, FL is almost synonymous with the theory of fuzzy sets, a the-

ory which relates to classes of objects with "unsharp" boundaries in which

membership is a matter of degree. Even in its more narrow definition, FL differs

both in concept and substance from traditional multivalued logical systems.

Classical sets follow Boolean logic (i.e. either an element belongs to a set or

not) whereas fuzzy sets use the concept of degree of membership. The membership

functions define the degree to which an input belongs to a fuzzy set. These mem-

bership functions are chosen empirically and optimized using a sample input/output

data. There are three points of view to define fuzzy membership: semantic import

model, similarity relation model, and experimental analysis.

In a general view, FS are applicable when the problem knowledge includes heu-

ristic rules, but they are vague, ill-defined, approximate, possibly contradictory.

2.3 Evolutionary Computation, Evolutionary Algorithms, Evolutionary

Strategies (Du and Swamy, 2006), (Goldberg, 1989), (Mai, 2010):

Evolutionary Computation is a computational method for obtaining the best

possible solutions in a huge solution space based on Darwin’s survival-of-the-

fittest principle. Evolutionary algorithms are a class of robust adaptation and global

optimization techniques for many hard problems.

The Genetic Algorithm (GA) is the best known and most studied among evo-

lutionary algorithms, while Evolutionary Strategy is more efficient for numerical

optimization. Genetic algorithms require neither data sets nor heuristic rules, but a

simple selection criterion to start with; they are very efficient when only a little is



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known to start with (Kasabov, 1998). EC has been applied for the optimization of

the structure or parameters of ANNs, FS and neurofuzzy systems. The hybridiza-

tion between ANN, FL, and EC provides a powerful combination for solving engi-

neering problems.

The first GAs were developed in the early 1970s by John Holland (at University

of Michigan, USA). GAs are inspired by the mechanism of natural selection where

stronger individuals are likely the winners in a competing environment. The GAs

are defined as:

“... search algorithms based on the mechanics of natural selection and natural

genetics. They combine survival of the fittest among string structures with a struc-

tured yet randomized information exchange to form a search algorithm with some

of the innovative flair of human search. In every generation, a new set of artificial

creatures (strings) is created using bits and pieces of the fittest of the old; an occa-

sional new part is tried for good measure. While randomized, GAs are no simple

random walk. They efficiently exploit historical information to speculate on new

search points with expected improved performance”.

A GA has three major components.

• The first component is related with the creation of an initial population of m

randomly selected individuals. The problem is encoded into binary strings (rows

of “1s” and “0s”) to represent the chromosomes, and then the computer gener-

ates many of these “bit” strings to form a whole population of them. The initial

population shapes the first generation.

• The second component inputs m individuals and gives as output an evaluation

for each of them based on an objective function known as fitness function (this

fitness function replaced the role of death in the biological world). This evalua-

tion describes how close to our demands each one of these m individuals is.

• Finally the third component is responsible for the formulation of the next gen-

eration. A new generation is formed based on the fittest individuals of the pre-

vious one. This procedure of evaluation of generation N and production of gen-

eration N+1 (based on N) is iterated until a performance criterion is met. The

creation of offspring based on the fittest individuals of the previous generation

is known as breeding. The breeding procedure includes three basic genetic op-

erations: Reproduction, Crossover and Mutation.

Evolutionary Strategies (ES) as being introduced in the early 1970s (Mai,

2010) have seen many improvements within the last decades. Nowadays, this ap-

proach can be regarded as an alternative to standard optimization techniques in

many scientific areas, especially in cases where gradient methods like the classical

least-squares algorithm fail.

Compared to other optimization techniques, ES algorithms are relatively easy to

realize, because the main idea behind it is very simple. They are universal, unde-

manding, close to reality, robust, and can be considered as a compromise between

92 I.D. Doukas

volume and path orientated search strategies. Once implemented, the same algo-

rithm can be applied to a wide range of problems without any big changes. In many

cases it’s even sufficient just to set up the new performance index that’s specific to

the actual problem; one rarely needs any additional a priori insight into the mathe-

matical/physical nature of the optimization task.

The only necessary condition for the ES to be applicable to a specific problem,

is the inherent existence of strong causality, not to be confused with weak causal-

ity. But on the other hand, there is no guarantee to actually find the global opti-

mum. In addition, the convergence speed of an ES algorithm might be less com-

pared to some alternative methods that are tuned to a specific problem.

Although there are several differences between GA and ES, their between barri-

ers are nowadays being hazy, since both techniques are improved by borrowing the

ideas from each other.

2.4 Harmony Search (HS) (Geem, 2010a), (Geem, 2010b):

In optimization problems, if the variables are discrete, they just do not have

derivatives (a good example is the ready-made cross sectional area of structural

members). If such a case, the HS (Harmony Search) algorithm offers an outlet,

since it is based on a novel stochastic derivative. HS algorithm is a phenomenon-

mimicking algorithm, which is inspired by the improvisation process of (especially

Jazz) musicians. In the HS algorithm, each "musician" (i.e. a decision variable)

"plays" (i.e. generates) a "note" (i.e. a value) for finding a "best harmony" (i.e. a

global optimum) all together. The traditional optimization algorithms, in order to

detect the right direction of the optimal solution, they give information dealing

with gradient. On the contrary, the above mentioned stochastic derivative that char-

acterizes the HS algorithm gives a probability to be selected for each value of a

decision variable.

2.5 Swarm Intelligence (SI) (Garg et al., 2009), (Umarani and Selvi,

2010), (Bonabeau and Meyer, 2001), (Kirankumar and Jayaram,

2008):

The collective behavior that emerges from a group of social insects has been

dubbed “Swarm Intelligence.” (SI). Social insects work without supervision. In

fact, their teamwork is largely self-organized, and coordination arises from the dif-

ferent interactions among individuals in the colony. Although these interactions

might be primitive (one ant merely following the trail left by another, for instance),

taken together they result in efficient solutions to difficult problems (such as find-

ing the shortest route among myriad possible paths in Internet traffic).

SI is a design framework based on social insect behavior. Social insects such as

ants, bees, and wasps are unique in the way these simple individuals cooperate to

accomplish complex, difficult tasks. This cooperation is distributed among the en-



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tire population, without any centralized control. Each individual simply follows a

small set of rules influenced by locally available information. This emergent be-

havior results in great achievements that no single member could complete by

themselves. Additional properties Swarm intelligent systems possess include: ro-

bustness against individual misbehavior or loss, the flexibility to change quickly in

a dynamic environment, and an inherent parallelism or distributed action.

The main advantages of SI are: Flexibility, robustness and self-organization (i.e.

the group needs relatively little supervision or top-down control.

2.6 Cellular Automata (CA) (Rajabi, 2009), (Wolfram MathWorld, 2010),

(Wolfram, 2000):

A Cellular Automata (CA) system, is a separated dynamic system which is

formed by a group of cells in a single/multi dimensional network. Position of each

cell in this positional network depends on previous position and position of

neighbor cells. Situation of cells get updated by a set of local probable or absolute

rules. Clearly situation of a cell depends only to its situation in previous time round

and position of near neighbors in previous time. All cells of a automaton get up-

dated simultaneously and in parallel manner. As the result condition of all automa-

ton advance in separate time stages. System general condition by completion of

situation of all cells will be determined as the result of several interactions. Place of

occurrence of interaction between a cell and its neighbors is a defined property of

CA. CA are the simplest models of spatially distributed processes.

Since CA models are clearly spatial, they can be used for urban planning simu-

lation and other utilization such as simulating land usage change, freeways traffic

and fire advancement as well. Furthermore, the CA method has been universalized

through mixing agent-like behavior or non-local search. Agent technologies, is

developing agents in database real world or purposeful search on the internet. In an

agent-based model, agents symbolizing human or other subjects are in a simulated

world of real world.

It is possible to define agent-base systems as a group of agents which have in-

ternal interaction in a common space and can change themselves and their envi-

ronment as well.

CA were invented in the 1940's by the mathematicians John von Neuman and

Stanislaw Ulam. Despite the simplicity of the rules governing the changes of state

as the automaton moves from one generation to the next, the evolution of such a

system is complex indeed.

2.7 Hybrid Systems (Kasabov, 1998), (Kalogirou, 2007), (Taylor and

Smith, 2006):

The combination of two or more AI-techniques results into an Hybrid-system.

The neuro-fuzzy control is a representative example of this category of systems.

94 I.D. Doukas

Figure 1: Mapping the domain space into the solution space-Selection of solution-paths.



95

On the one side, the merits of a FS are found in the representation of linguistic and

structural knowledge by fuzzy sets.

To perform fuzzy reasoning and/or logic in a qualitative manner is another

strong point of FS.

Figure 2: Data and/or expertise (theories) availability enlighten on the selection of the

method.

On the other side, the strongest points of ANNs are the representation of

nonlinear mappings and their - through training – construction. Speaking about FS,

their behavior is comprehensible and "digestible", thanks to their logical structure

and their stepwise inference procedures. Speaking about ANNs, simply the afore-

mentioned «black-box» recapitulates their behavior.

The possibility of combining these two components into a new system, named

neuro-fuzzy control, is a rather recent consideration of the scientists. Such a com-

96 I.D. Doukas

Figure 3: An indicative qualification of some intelligent systems



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bination gives as a resultant a new system armed with «weapons» from both the

fuzzy and the neural ones.

The main goal of the design of an intelligent system is to represent as ade-

quately as possible the existing problem domain knowledge in order to better ap-

proximate the goal function, in most cases not known a priori. In order a solution to

be achieved, there is a set of different methods to select from.

Depending on the type of the problem and the available knowledge about the

problem, different methods could be recommended for use (Figure 1). If it is a mat-

ter of data population and the available knowledge (expertise), then Figure 2 illus-

trates a topology of «solution-spaces». Finally, a qualitative comparison of some

intelligent systems is illustrated in Figure 3 (Taylor and Smith, 2006).

3. A sample of AI/SC/KE applications in Geodesy and Geomatics

Geodesy and Geomatics do offer the “fertile land” for many AI/SC/KE applica-

tions. There is an increasing diffusion of such methods and IAG has already estab-

lished IAG-WG 4.2.3 (dealing with application of AI in Engineering Geodesy)

(IAG-WG 4.2.3, 2010). By comparing problems of Geodesy / Geomatics / Engi-

neering Geodesy with problems of AI/SC/KE, there are noteworthy similarities

(Kutterer, 2010). Both disciplines use methods based on mathematical stochastics,

they also use modeling (for variables, parameters). Finally, the issue of learning in

simple “geodetic words” is equivalent to the procedure of model selection and pa-

rameter identification.

The relative bibliography is in fact huge and increases rapidly. The space here

allows only for only some indicative bibliography regarding just broad fields of

Geodesy/Geomatics. For example, in the GIS field, Kirankumar and Jayaram,

(2008) offer an excellent review. The conclusion is that the modeling of the envi-

ronment and the site selection, the analysis of spatial data and the integration of SC

components, the decision support are some of many essential topics where

AI/SC/KE has a powerful influence (Kirankumar and Jayaram, 2008), (Rajabi et

al., 2009), (Bartoněk, D., 2003). Going to the GNSS area, there are plenty of appli-

cations dealing with GPS and navigation, covering a really wide range of cases,

from atmospheric issues to geoid and space geodesy (Xu et al. 2002), (Doukas and

Ioannidis, 1997 ), (Coulot et al. 2009), (Crowell, 1992), (Syed and Cannon, 2004),

(Liu et al. 2007), (Zaletnyik et al. 2004).

Another excellent review, dealing with AI/SC/KE techniques as applied in En-

gineering Geodesy, is given by Kutterer (2010), while the same does Adeli (2001)

for the science of Civil Enginnering. There is plenty of common fields between

Engineering Geodesy, Civil Engineering, Geodesy and Geomatics, where geodetic

methods (combined or not with AI/SC/KE tools) play key roles (for example

ground deformation, landslides, geodetic control nets etc.) (Haberler-Weber 2005),

(Einhorn, 2007), (Carbobe et al. 2008).

98 I.D. Doukas

4. Conclusions

AI/SC/KE form an emerging field that consists of complementary elements of

fuzzy logic, neural computing, evolutionary computation, machine learning and

probabilistic reasoning. Due to their strong learning, cognitive ability and good

tolerance of uncertainty and imprecision, SC techniques have found wide applica-

tions. Generally speaking SC techniques resemble human reasoning more closely

than traditional techniques which are largely based on conventional logical sys-

tems, such as sentential logic and predicate logic, or rely heavily on the mathemati-

cal capabilities of a computer.

Navigation, deformation analysis, GIS/GPS/Geomatics, deformation network

adjustments, optimization of complex measurement procedures, decision support

systems are already some of the geodetic fields with derived and certified benefits

arise from the AI/SC/KE penetration and diffusion.

In coming years, AI/SC/KE is expected to play an increasingly important role in

the conception and design of systems whose MIQ (Machine IQ) is much higher

than that of systems designed by conventional methods.

The younger “meta-modern” AI/SC/KE tools (i.e. Harmony Search and Swarm

Intelligence), although the didn’t show their impact on geodetic fields so far, they

have ‘an exotic timbre’ of originality, that makes them attractively promising and

their role in the geodetic community is expected to be strongly expanded in the

near future.

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