Assessment of the commercial applicability of artifical intelligence in electronic Businesses

Diploma Thesis, 2002

73 Pages, Grade: 1.3


Schloß Reichartshausen am Rhein
Diploma Thesis
In Order to Receive the Academic Degree
Assessment of the commercial applicability of
artificial intelligence in electronic businesses

To mum and dad

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Table of contents
List of tables...III
Table of figures...III
Table of abbreviations ...III
Introduction ...1
1.1 Problem
Statement ...1
Scope of the thesis...1
Course of the investigation ...2
Intelligent Electronic Business...2
The need for intelligent system technologies...3
Mediators in traditional and electronic markets...4
Consumer and buyer behaviour in electronic commerce...6
Artificial Intelligence in Electronic Commerce ...7
3.1 Artificial
Intelligence ...8
Theories of human intelligence... 8
The `Artificial' Intelligence ... 9
Intelligence in computers ... 10
Intelligent Agents: Embedding Artificial Intelligence in e-businesses...11
Software Agents... 12
The agents' intelligence ... 13
A taxonomy for intelligent agents... 14
Agent interactivity according to the Consumer Buyer Behaviour stages 15
Mediating agents in the electronic business scenarios ... 16
Technology and Business Application Analysis...19
Business Application Areas in Electronic Commerce ...19
Individualization through Recommendation... 20
Intelligent Partnering... 22
Online Bargaining ... 23
Ontology-based product content management... 25
Intelligent Customer Relationship Management... 27
Current state of Intelligent Agent Technology...29
The technologies employed by Intelligent Agents... 29
Agent communication and protocol standards... 31
Assessing the commercial applicability of Intelligent Agents...32
5.1 Cost-Benefit-Analysis
for Intelligent Agents ...32
Cost analysis: A quantitative and qualitative approach ... 32
Benefit analysis: The Return on Intelligence ... 34
A discussion of social, ethical and legal issues and dilemmas... 35
SWOT-Analysis for Intelligent Agents in electronic businesses...38
Strengths ... 38
Weaknesses ... 39
Opportunities... 40
Threats... 41
5.3 Real-world
of Intelligent Agents: Expert interviews ...42

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6.1 Summary ...45
6.2 Evaluation ...47
6.3 Outlook...48
Appendixes ...49
Appendix A: Interview Guideline...49
Appendix B: Interview summary (living systems AG) ...53
Appendix C: Interview summary (novomind AG) ...56
Appendix D: Interview summary (Ubis AG)...59

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List of tables
Table 1: Real-world agents and their application in transaction processes... 18
Table 2: SWOT Analysis: Strengths and their internal impact... 39
Table 3: SWOT Analysis: Weaknesses ... 40
Table 4: SWOT Analysis: Opportunities ... 41
Table 5: SWOT Analysis: Threats ... 41
Table of figures
Figure 1: Agent Interaction Scenario ... 15
Figure 2: Real-world agents and their position in the agent interaction scenarios ... 19
Table of abbreviations
Agent Communication Language
Automated Collaborative Filtering
AI Artificial
Computer Aided Selling
Consumer Buyer Behaviour
Customer Relationship Management
Electronic Commerce Code Management Association
Frequently Asked Questions
Foundation of Intelligent Physical Agent
IT Information
Knowledge Interchange Format
Knowledge Query Manipulation Language
Online Dynamic Bidding
Open Profiling Standard
Supplier Relationship Management
Strengths, Weaknesses, Opportunities & Threats
Universal Content Extended Classification
Universal Standard Products and Services Classification
Word Wide Web Consortium
World Wide Web

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1 Introduction
1.1 Problem Statement
Artificial intelligence has already been applied to many areas since its official birth in 1956, but
most of the applications ended up in great disappointments as the benefits they reaped were very
low (Andriole & Hall, 2000, p.17). Due to this reason the vast interest in applying this relatively
young technology to business calmed down in the late seventies when scientists recognized that
the current intelligent systems were not yet plug-and-play solutions, hence mature enough to
fully meet the business needs and requirements at that time.
However, the limited commercial applicability of artificial intelligence in the past has to be
rethought today as with the significant progress in artificial intelligence research and the growth
of electronic commerce conducted over the World Wide Web new opportunities for business
applications of artificial intelligence have emerged consequently. Nowadays horizontal and
vertical electronic commerce is significantly driven by intelligent applications. Their employ-
ment in electronic businesses "may well generate huge returns on investments, providing a
technology-based response to increasing competition, the volatility of business models, and the
pace of technology change" (Andriole & Hall, 2000, p.18). Despite the wide assumption that
artificial intelligence will have a major impact on Internet-related businesses today and espe-
cially in the next years to come, it is uncertain to what extent it performs and will perform that
1.2 Scope of the thesis
The purpose of this thesis is to analyse, assess and evaluate the potential of commercial applica-
tions of artificial intelligence in electronic businesses. Therefore the main research question of
this paper is whether artificial intelligence is reasonably applicable in Internet-related busi-
nesses, first in terms of effectiveness and second in terms of efficiency. In the assessment the
application of artificial intelligence in electronic businesses is represented by the employment of
intelligent agents.
In harmony with the major research question emphasized above, the paper provides a thorough
discussion about the economic impact of the most common and relevant application types of
intelligent agents on electronic commerce environments. In addition the driving underlying
technologies of intelligent agents are analysed with respect to artificial intelligence techniques
and methods, and current standardisation efforts.
The assessment itself constitutes of theoretical and practical instruments that measure the com-
mercial applicability of artificial intelligence in electronic businesses. First, the effectiveness of
employing intelligent agents will be measured with a cost-benefit analysis to prove whether it is
the right thing to do for an electronic business. Second, the efficiency of such an application
will be assessed with a detailed SWOT-Analysis in order to determine whether employed agents
do their job right. Finally, the results from a range of expert interviews with dominating devel-
opers in the field of intelligent systems technology will be integrated into the assessment. Expert
interviews as a research method seem to be appropriate for this assessment as they investigate

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the phenomenon within its real-life context. Furthermore they extend experience or add strength
to what is already known through previous research.
In a final summary and evaluation the answer to the given research question is provided. By the
end of the thesis, the reader should have gained a strong comprehension of the linkages between
electronic commerce and intelligent agents and should understand the resulting implications of
the technology's application on electronic businesses.
1.3 Course of the investigation
The thesis is divided into four core parts: First, the need and relevance for intelligent solutions
in electronic marketplaces is emphasized in section two. This includes the identification of to-
day's challenges for mediators in electronic commerce environments. The need for intelligent
solutions in Internet commerce can be satisfied by solutions that stem from artificial intelligence
research, such as intelligent agents. Therefore the third section provides thorough background
knowledge about the meaning of intelligence and the way it is reproduced in computer pro-
grams. Part of this section is also the introduction to intelligent agents, software programs that
embed various levels of artificial intelligence to solve problems in commercial environments on
the Internet. Their role as mediators in distinct business scenarios on the Web will be presented
by stressing important prototypical and real-world examples in these arenas. After having iden-
tified the need for intelligent systems and the potential solution of employing intelligent agents
in electronic businesses in the second and third section, the fourth and fifth section attempt to
answer the question whether these intelligent agents are really able to effectively and efficiently
cope with the identified problems and business needs. The fourth section deals with the analysis
of the five most common application areas of intelligent agents in electronic commerce envi-
ronments and the examination how they technologically cope with the challenges in these areas.
The fifth section attempts to prove whether it is profitable for electronic businesses to employ
intelligent agents or not. First, a cost-benefit analysis evaluates the effectiveness of commercial
applications of intelligent agents. Then a detailed SWOT analysis assesses the efficiency of
intelligent agent technology in electronic businesses. The assessment is complemented by a
series of interviews conducted with experts in the field of intelligent agents.
Finally, the thesis ends up with a precise summary. Moreover, the inferences drawn throughout
the entire paper will be aggregated and the main research question answered.
2 Intelligent Electronic Business
As Schulten defines, "E-commerce is about electronically doing business ­ that is, putting the
business process flows of companies on web-enabled technology" (Fensel, Ding et al., 2001, p.
12). However, web-enabled technology alone will not be able to cope with the emerging chal-
lenges in electronic commerce environments.
In this introductory section the need for and the relevance of intelligent solutions in commercial
electronic environments are discussed. The development of the market and business characteris-
tics in these environments will be highlighted pointing out the potential areas where and how
intelligent solutions might be applied.

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2.1 The need for intelligent system technologies
Our society has changed into an information age, in which information is considered as a useful
tool to solve many problems. During the last years the amount of information coming from an
increasing number of sources from all over the world has multiplied itself several times. But the
masses of information are rather unstructured what makes it more and more difficult for human
beings to get a clear picture of all information suppliers and to seek and take the needed infor-
mation of relevance into account. Herman states that the Internet has changed from a supplier-
driven to a demand-driven information market in which the supply of information has become
less important rather than the demand. He claims that on the one hand it might seem that the
gigantic pool of information meets the need for information demand sufficiently in the first
place, but on the other hand the amount of information is so vast and so unstructured that users
sometimes cannot even find relevant information at all. In this context Herman talks about the
"information overkill" (Hermans, 1996, pp. 4-5).
However, conventional search methods attempt to reduce the mentioned problem. The most
common solutions are search engines, huge databases with indexes that allow user to check
whether certain information can be found in the database. If matches are found, the databases
will provide users with the information where the requested information can be found on the
Internet. But these methods fail due to variety of reasons: First of all, the absence of a central
supervision for the Internet growth and development leads to a poor information transparency,
thus giving search engines a hard time. As an example, information that appeared in the past can
suddenly disappear (and vice versa) or move to a different unknown location on the Web. An-
other weakness is that information on the Internet is rather heterogeneous, which means that
information appears in different formats making it even harder to automatically search and re-
trieve relevant information (Hermans, 1996, p. 5).
Today a solution is required that is capable of more intelligent information search and retrieval,
maybe through the use of thesauri or similar concepts in order to minimize the retrieval of ir-
relevant and noisy information. Such solution should have some sort of knowledge base in
which information about the multiple information sources on the Internet is stored and updated
frequently, and some problem solving abilities to perform complex tasks more efficient and
faster. Users should only need to consider what information they seek; the questions about
where and how this particular information can be retrieved should not be of the users' concern.
As information search and retrieval can be quite a time-consuming job, a solution should be
capable of working in conjunction with other information systems and software applications to
gain the information requested by the user in a time- and cost efficient manner. Not only con-
text-sensitivity, which is required to distinguish between relevant and irrelevant information on
the Internet, but also the adoption to and the consideration of the user's individual needs and
wishes becomes highly significant when looking for relevant information. Furthermore, a solu-
tion should be that much intelligent that it is able to learn from the experiences gained from
performed tasks, and to react upon results (Hermans, 1996, p. 6-8).
These requirements cannot be performed by regular search engines or comparable alternatives
as they all lack the mentioned intelligent abilities necessary to cope with the new challenges of
today's information world. A solution is required quickly; otherwise the loss of control over the
Internet growth and development is the consequence resulting in the Internet's limited useful-
ness. At this stage Herman points out that scientists intend to draw up a new structure for the
Internet that makes it more conveniently and easy to use. A very promising idea is a three-layer

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concept involving users, suppliers and intermediaries (in contrast to the current dominance of
two-layer structures in the Internet). In this concept intermediaries gain major importance as
they are supposed to address and even resolve the mentioned weaknesses of the Internet with
the use of intelligent technologies (Hermans, 1996, pp. 8-9). At this stage, the role of these mid-
dle-layers in electronic markets has to be analyzed; that is subject of the following section.
2.2 Mediators in traditional and electronic markets
In order to understand the principles of intermediaries it is important to compare their different
roles in traditional and electronic markets.
Basically, a market is a place where buyer and seller cross path. In its original concept, a market
is described as a place where suppliers and demanders come together at a particular location and
a certain time in order to trade (Achleitner & Thommen, 1998, p. 148). According to the neo-
classical paradigm the price is the factor that is in charge of the economic allocation of the lim-
ited resources, thus establishing an equilibrium between supply and demand (Felderer & Hom-
burg, 1999, p. 51). In this paradigm the market is characterised by complete market transpar-
ency, the homogeneity of goods, the immediate reaction upon changes of market conditions, the
principle of maximising one's utility, and the lack of market entry barriers and user preferences
(Achleitner & Thommen, 1998, pp. 239-240). This is an idealistic assumption that is far away
from reality. In contrast to these perfect markets, imperfect markets are characterized by the
presence of transaction costs that incur in several stages of trading processes such as through the
search for suitable products (product brokering), the search for appropriate suppliers (merchant
brokering), the settlement of the terms and conditions of the trade (negotiation) and the execu-
tion of contractual agreements, which incur additional costs for monitoring and controlling ef-
forts (Jüttner, 1996).
In comparison to conventional markets in the real world, electronic markets come closer to the
ideal of perfect markets, because the employment of innovative Internet and communication
technologies allows market participants to reduce these transactions costs (Pauk, 1997). There-
fore Schmid defines electronic markets as markets that are supported by technological mecha-
nisms that facilitate the processes involved in all transaction phases (Schmid, 1993, pp. 465-
480). However, the reduction of transaction costs leads to significant advantages for both buyer
and seller in electronic markets (Malone, Yates & Benjamin, 1987, p. 488). The buyer has got
advantages such as the reduced coordination costs, the higher degree of buyer-seller interaction
due to more cost-efficient ways to communicate, the increase in product and service quality due
to a wider selection range of potential vendors, the increased customer focus of businesses due
to the increase in competition, the widened variety of products and services, and the lower
prices (due to the smaller profit margins as a consequence of increased competition). The ad-
vantages on the seller side are that businesses can use the Internet as an additional distribution
channel and establish direct relationships with customers without requiring intermediaries in
their traditional role any more. Furthermore, businesses in electronic marketplaces are able to
segment their customers more efficiently, to increase their product and service diversity, to sat-
isfy the information needs of their customers at a higher level, and to bypass price competition
by specialising in market niches. The communication costs for businesses are significantly re-
duced as well. Since market entry barriers for electronic markets are rather low, entering into
these markets is rather cheap and easy. As stated above, electronic markets are highly attractive

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due to the absence of dependencies on space and time. This allows market participants to react
faster upon market changes (Jüttner, 1996; Pauk, 1997; Schlueter, 1997).
In traditional markets the mentioned transaction phases can be supported or even entirely exe-
cuted by third parties. In that case these parties are called intermediaries. Their functions com-
prise the aggregation of supply or demand (as for example wholesalers who collect the demand
of many customers to achieve better price conditions), and the protection against opportunistic
market participants (as opportunistic market participants might want to benefit from the advan-
tages of intermediaries in the future as well, they renounce of their opportunistic behaviour) and
difficulties that can occur during business transactions (intermediaries are flexible and can pro-
vide alternative products, services or even suppliers on the fly). Further functions of intermedi-
aries include the facilitation of communication between buyer and seller (facilitation of product
and information brokering phases as intermediaries know more about the particular the local
market than the individual end-customer), and the matching of buyers and sellers (the team-
work among intermediaries and various buyers and sellers in marketplaces enables intermediar-
ies to facilitate merchant brokering). However, while traditional intermediaries attempt to re-
duce transaction costs on the one hand they increase the costs of the products and services for
end-customers on the other hand (because of the added value for end-customers). As a result,
many selling businesses have attempted to directly approach end-customers with their products
and services, thus bypassing intermediaries. However, profitable applications of these direct
channel concepts depend largely on the particular type of products/services and customers. Most
often the contact to and the knowledge about the end-customers is lacking, and renouncing of
intermediaries is not considered to be wise (Pauk, 1997).
The characteristics of electronic markets, as stated above, enable electronic businesses to estab-
lish these direct connections to end-customers easily. According to Schoder and Müller, due to
this reason it becomes more and more cost-efficient for businesses to internalize formerly out-
sourced services, perform vertical integration and thus eliminate intermediate stages in business
transactions. That enables them to offer their products directly and cheaper to the end-customers
(Sarkar, Butler & Steinfield, 1995; Schoder & Müller, 1999, pp. 2-4). In contrast to this so
called disintermediation hypothesis, which bases on the loss of importance of intermediaries in
electronic markets, the intermediation hypothesis claims the need for intermediaries in elec-
tronic marketplaces (Schoder & Müller, 1999, p. 4; Wigand & Benjamin, 1995). The hypothesis
puts more weight on the need for dealing with the "information overload" on the Internet, the
need for a stronger Taylorism and coordination which are provided by the services of intermedi-
aries (Schoder & Müller, 1999, pp. 3-4). Indeed, the Internet is characterised by the "unavail-
ability of structured and relevant information [...]" (Terpsidis, Moukas, Pergioudakis, Doukidis
& Maes, 1997, p. 2) about products, suppliers, etc., thus increasing the marketplace's complex-
ity and decreasing market transparency. While low transaction costs in electronic marketplaces
will make intermediaries' traditional role as aggregators of supply and demand less important,
they gain new roles in these marketplaces as for example the provider of more market transpar-
ency for customers (in product or information brokering stages). Communication among trading
partners and customers has become self-explanatory in electronic marketplaces, thus eliminating
a further traditional role of intermediaries. However, Pauk (1997) is of the opinion that because
Internet-based marketplaces transfer higher quantities of information among their actors, further
innovative roles for intermediaries arise. Even for merchant brokering there are new application
areas for intermediaries in providing additional relevant information about potential trading
partners (Pauk, 1997).

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Bailey and Bakos summarize that although traditional intermediaries may disappear in elec-
tronic markets there are still new roles emerging for them (Bailey & Bakos, 1997, p. 13). But
these are rather difficult to perform and require intelligent technologies "that can sense, under-
stand and act in the specific context in which e-business actors work" (Akkermans, 2001, p. 8).
As the demand for information gains more and more significance electronic business today has
to be viewed from a different perspective: The roles of intermediaries change in electronic mar-
ketplaces; so do business transactions. Generally, business transactions base on a series of sub-
sequent stages of buyer-seller interaction. These interaction stages, currently loaded with ineffi-
ciencies and transaction and opportunity costs are the places where intelligent solutions are
needed. Therefore the following section analyses the different stages of business transactions in
electronic marketplaces from a buyer's and seller's perspective.
2.3 Consumer and buyer behaviour in electronic commerce
One of the trends derived from the rise of the Internet is the migration from mass production to
mass customization, hence one-to-one marketing. The customers' demand for information,
products or services, and their active involvement in transaction processes becomes more and
more important for the success of an electronic business. The buyer's significance in transaction
processes has been analyzed since the sixties and brought up the marketing-based Consumer
Buyer Behaviour model (CBB). It describes the actions and decisions involved in the process of
buying goods or services. To suit the characteristics of electronic marketplaces it has been aug-
mented accordingly. However, the adapted model suffers from a couple of limitations such as
its sole focus on retail business (B2C), excluding concepts such as B2B or C2C that emerged
through the Internet. In addition, the model does not cover all possible buyer behaviours and has
difficulties in taking all issues related to electronic business into account. Nonetheless, Maes,
Guttman and Moukas believe that it is still powerful enough to support the understanding of the
basic stages of business transactions in electronic commerce (Maes, Guttman, & Moukas, 1998,
p. 22).
Concerning the traditional Consumer Buyer Behaviour model, there are a variety of theories and
models proposed in the past. However, the common ideas of these models have been extracted
and put into a framework that suits today's Internet commerce characteristics (Terpsidis, Mou-
kas, Pergioudakis, Doukidis & Maes, 1997, pp. 3-4; Maes, Guttman, & Moukas, 1998, p. 22;
Maes, Guttman & Moukas, 1999, pp. 81-91).
This framework consists of six stages and is described in the following paragraphs:
1. The first stage describes the need identification or problem recognition phase, where the
consumer gets aware of an unmet need. The awareness of needs can be stimulated by the
seller through product information.
2. The second stage comprises the information brokering or search process, where information
is retrieved by the consumer to determine which product to buy. An evaluation of the gath-
ered product alternatives is done based on consumer's criteria. An evoked set of products
results from the evaluation.

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3. The gained set is used in this stage in combination with merchant-specific information to
determine from which seller to buy from (merchant brokering). The merchant is selected by
consumer-specific criteria such as price, delivery time, warranty, reputation and many more.
These criteria basically base on the four Ps of marketing (product, place, price and promo-
4. The purchase decision is done in the following stage where the terms of the transaction are
negotiated with the potential seller. In many markets negotiation is an internal part of the
product and merchant brokering process.
5. The actual purchase and delivery is the consequence of the negotiation. However, even if
the purchase decision has already been made, the actual purchase can take place after some
time as well.
6. The final stage of the CBB model constitutes of customer and product service, including the
evaluation of the consumer's overall satisfaction or dissatisfaction of the buying process and
the rightfulness of his or her purchase decision.
Even though these stages may overlap and are only approximations of the real-world complex-
ity in buyers' behaviours the CBB model still has got enough strength to be applied in various
fields of business. (Terpsidis, Moukas, Pergioudakis, Doukidis & Maes, 1997, pp. 3-4; Maes,
Guttman, & Moukas, 1998, p. 22).
The first three introductory sections have pointed out, that mediators in electronic marketplaces
differ significantly from intermediaries in traditional markets. They have to face different chal-
lenges and must therefore perform distinct tasks. One of the most crucial challenges is this con-
text is the increasing quantity of information. Where conventional solutions fail to cope with
these challenges efficiently the need for more intelligent solutions emerges. In other words,
intermediaries require some sort of intelligence to ensure effective and efficient business trans-
actions and to shield actors in electronic marketplaces from the Web's complexity.
3 Artificial Intelligence in Electronic Commerce
As stated in the previous sections, the new roles of mediators in electronic commerce require
intelligent mechanisms to successfully cope with the challenges of today's electronic market-
places. Solutions for re-producing intelligence in electronic environments stem from artificial
intelligence research. This part of the thesis provides a compact introduction to the concept of
artificial intelligence emphasizing its meaning in context, the characteristics it has got and the
aims it follows. Furthermore, the most common application of artificial intelligence in elec-
tronic commerce, the intelligent agent, is introduced afterwards.

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3.1 Artificial Intelligence
Artificial Intelligence (AI) is a comprehensive and multi-disciplinary field. It has definitely its
roots in the area of computer science but has also close relationships to psychology, philosophy,
logic, linguistics, engineering and even neurophysiology (IBM Research, 2001).
There are a variety of definitions and explanation approaches for artificial intelligence but there
is still no common agreement on what term exactly comprises. The reason for that is that there
is also no real consensus about the meaning of `intelligence' itself (Moursund, 1999; Neisser et
al., 1996). However, the most famous one stems from Minsky (1968), who stated: "[...] artificial
intelligence is the science of making machines do things that would require intelligence if done
by men" (Decker & Hirshfield, 1998, p. 299, cited after Minsky, 1968, p. 23).
3.1.1 Theories of human intelligence
As already stated above, the source for the debates about artificial intelligence derives from the
attempt to define the term intelligence. There are many theories and approaches proposed by
famous researchers such as Gardner, Perkins, or Sternberg (Moursund, 1999; Neisser et al.,
Howard Gardner's theory of multiple intelligences was suggested in his book `Frames of Mind'.
He hypothesized the presence of the eight components of intelligence that are aligned with dif-
ferent academic disciplines: Interpersonal, Intrapersonal, naturalistic, musical, bodily-
kinaesthetic, linguistic, logical-mathematical, and spatial (Moursund, 1999; Neisser et al.,
Similar to Gardner's theory is the one of Robert Sternberg. In contrast to Gardner, he does not
relate the components of intelligence to academic disciplines; instead, he breaks down intelli-
gence into three different fundamental factors: practical intelligence, experiential intelligence
and componential intelligence. Practical intelligence deals with the ability to adapt to and shape
one's environment whereas the experiential intelligence addresses one's ability to deal with
novel situations, the ability to automate ways of dealing with novel situations so that they can be
performed more easily in the future and the ability to think in novel ways. The componential
intelligence comprises the ability to process information effectively. Sternberg believes that
human intelligence can be strengthened through study and practise (Moursund, 1999; Neisser et
al., 1996).
The theory of David Perkins is similar to Sternberg's theory although his research stresses the
support of Gardner's theory of multiple intelligences. He claims human intelligence to consist
of three major dimensions: Neural intelligence, experiential intelligence and reflective intelli-
gence. Neural intelligence deals with the efficiency and precision of one's neurological system,
experiential intelligence refers to one's accumulated knowledge and experiences (one's exper-
tise), and reflective intelligence is concerned with one's strategies for problem solving, learning
and approaching intellectually challenging tasks (Moursund, 1999).
Although these theories contain some plausible and traceable thoughts, neither one of them ever
gained universal acceptance nor could answer all open questions. It is just that we know too
little about our human brain. However, it is possible to extract some composite facts all theories
have in common. Then intelligence can be seen as the ability to learn, pose and solve problems.
The ability to learn is accomplished by combining experience, education and training. Posing

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problems includes recognizing problem situations and transforming them into more clearly and
precisely defined problems, whereas solving problems comprises accomplishing tasks and doing
complex projects. So, intelligence can be described as a combination of diverse abilities (Mour-
sund, 1999).
3.1.2 The `Artificial' Intelligence
The science of Artificial Intelligence (AI) can be described as the making of intelligent ma-
chines and computer programs that embed processes such as perceiving, learning, reasoning,
generalizing and discovering meaning in context (IBM Research, 2001). In other words it at-
tempts to "re-create in computer software the processes humans use to solve problems" (Andri-
ole & Hall, 2000, p.14). According to John McCarthy, professor at Stanford university and
known as the father of artificial intelligence, the strong relationship of the term "intelligence" to
our human intelligence makes it hard to define in general what kinds of computational proce-
dures can be called intelligent (McCarthy, 2001). However, artificial intelligence is not limited
to the attempt to reproduce or imitate human intelligence in other scientific fields. It also com-
prises the application of seemingly intelligent methods that must not have been observed in
human beings before (McCarthy, 2001). Therefore one can distinguish between two distinct
approaches to reincarnate human problem-solving processes in computer software, the bottom-
up and the top-down approach (McCarthy, 2001; Encyclopædia Britannica, no date).
In 1932, Edward Thorndike and in 1949 Donald Hebb suggested that learning capabilities de-
rive from strengthening certain patterns of neural activity by increasing the neuron firing be-
tween the associated connections. This was the origin of the connectionist or bottom-up ap-
proach. So, achieving artificial intelligence from bottom-up is done by building electronic repli-
cas of the human brain's complex network of neurons. The idea of rebuilding the human brain
was the birth of the neural network theory, which presented some impressive results by mimick-
ing the human thinking processes and recognizing letters. The connectionist approach has the
advantage that it is able to model human functions at a lower level such as image recognition,
motor control and learning capabilities. Humans learn through a bottom-up approach as well
since we all start with nothing when we are born. Just through our own intellect and learning we
learn walking, speaking a language and much more. However, this method is often hard to em-
ploy in the field of computing (Encyclopædia Britannica, no date).
In 1957, two advocates of the symbolic artificial intelligence, Allen Newell and Herbert Simon,
summed up the top-down approach. They claimed that processing structures of symbols is suffi-
cient to produce artificial intelligence in a computer, and moreover, that human intelligence is
the result of the same type of symbolic manipulations. In contrast to the bottom-up approach,
symbolic artificial intelligence attempts to mimic the brain's behaviour with computer programs
(independent of the biological structure of the brain). In this method all necessary knowledge is
already present for the program to use (given that it was pre-programmed in advance). There-
fore this method is quite powerful to perform relatively high level tasks such as language proc-
essing (Generation 5: The Artificial Intelligence Repository, no date; Encyclopædia Britannica,
no date).
As demonstrated above, both approaches have their advantages and disadvantages but each of
them fails where the other excels. As the bottom-up approach fails to cope with the complexity
of the real neuron interactions, it has not yet achieved the replication of the nervous system of

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even the simplest living things. Also top-down approaches work only in simplified environ-
ments and fail when confronted with the real world. However, one can see similarities to the
human brain in both approaches. Due to this reason both approaches are pursued today (Genera-
tion 5: The Artificial Intelligence Repository, no date; Encyclopædia Britannica, no date).
Andriole and Hall point out that artificial intelligence can be considered as one of the strongest
methods in problem solving, if accurately targeted. There are many problem types that suit to
techniques and tools of artificial intelligence, but for other problems AI-based techniques may
be the worst approach. The main differences between conventional and AI-based systems are:
While conventional systems process data within certain boundaries AI-based systems apply
their knowledge to new unspecified problems within particular domains. Moreover, where con-
ventional systems are rather passive, AI-based systems interact actively with the user. In addi-
tion such systems can draw inferences, implement rules of thumb or solve complex issues
whereas conventional systems cannot infer beyond certain pre-programmed limits. One of the
greatest things about artificial intelligence is that it can be applied to a variety of areas. Taken
the example from section 2.1 concerning the masses information on the Internet, AI-based
search routines can structure information as knowledge and apply it to various problem-solving
tasks. Moreover, Andriole and Hall emphasise that it is important to distinguish between the
tools and techniques of artificial intelligence and the areas it targets. Rules, semantic and infer-
ence networks, natural language processing or special-purpose programming languages count
for such tools. To some of these examples will be referred later in this thesis. Target areas are
the applications of AI-based tools and techniques to a particular domain. Such application areas
are for example expert systems, decision support systems, robotics, intelligent agents, data-
mining applications or intelligent content management applications. (Andriole & Hall, 2000,
However, today everybody is excited about the development and application potential of artifi-
cial intelligence but not long ago AI was regarded as a great disappointment. Especially in the
late seventies expectations concerning the short-term potential of artificial intelligence were
much too high, leading to immense frustrations when only little benefits could be reaped (An-
driole & Hall, 2000, p.17). According to Andriole and Hall, "the real pay-off for AI technolo-
gies lies in the extend to which they can link to other technologies and applications" (Andriole
& Hall, 2000, p.18).
3.1.3 Intelligence in computers
Research in artificial intelligence concentrates mainly on implementing the following compo-
nents of intelligence into computer programs: learning, reasoning and problem solving (Ency-
clopædia Britannica, no date).
- Learning:
Learning techniques are either based on trial-and-error principles, rote learning (memorizing
individual items), or generalization (applying past experiences to new analogues situations).
The first two learning techniques are easy to realize technically and are already imple-
mented in a wide range of applications whereas a much harder challenge is implementing
the ability to generalize (Encyclopædia Britannica, no date).
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Assessment of the commercial applicability of artifical intelligence in electronic Businesses
European Business School - International University Schloß Reichartshausen Oestrich-Winkel
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Thomas Kramer (Author), 2002, Assessment of the commercial applicability of artifical intelligence in electronic Businesses, Munich, GRIN Verlag,


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