Artificial Intelligence (AI) is a branch of Science which deals with helping
machines finds solutions to complex problems in a more human-like fashion.
Artificial intelligence (AI) is the study and design of machines or
computational methods that can perform tasks that normally require human
"Artificial intelligence is the ability of a human-made machine (automation) to
match or simulate human methods for the deductive and inductive acquisition and
application of knowledge and reason ".
"Artificial intelligence is the study of how to make computers do things at
which at the moment, people are better".
"Artificial intelligence is that branch of computer science dealing with
symbolic, non-algorithmic methods of problem solving".
"Artificial intelligence is part of computer science concerned with designing
intelligent computer systems that is systems that exhibit the characteristics we
associate with intelligence in human behavior".
Intelligence is the ability to acquire and apply the knowledge. Knowledge is the
information acquired through experience.
Experience is the knowledge gained through training. Summing the terms, we
get artificial intelligence as the “copy of something natural (i.e., human beings)
‘WHO’ is capable of acquiring and applying the information it has gained through
Intelligence can be simply defined as a set of properties of the mind.
These properties include the ability to plan, solve problems, and in general,
A simpler definition could be that intelligence is the ability to make the right
decision given a set of inputs and a variety of possible actions.
Intelligence is composed of:
Many tools are used in AI, including versions of search and mathematical
optimization, logic, methods based on probability and economics.
The AI field draws upon computer science, mathematics, psychology, linguistics,
philosophy, neuro-science, artificial psychology and many others.
Need for Artificial Intelligence:
•To create expert systems which exhibit intelligent behavior with the capability to
learn, demonstrate, explain and advice its users.
•Helping machines find solutions to complex problems like humans do and applying
them as algorithms in a computer-friendly manner.
Artificial intelligence is an emerging technology science that studies and develops
the theory, technology and application systems for simulating and extending human
intelligence, involving disciplines such as psychology, cognitive science, thinking
science, information science, systems science and bioscience.
The Artificial intelligence is in fact the simulation of the process of data interaction
of human thinking, hoping to understand the essence of human intelligence and then
produce a smart machine, this intelligent machine can be the same as human
thinking to respond and deal with the problem.
Artificial intelligence has provided great potential and space for the optimization of
electrical engineering, and it will bring about great improvement not only in
economic aspect, but also in safety and actual operation control.
Applications of AI:
It deals with the various kinds of knowledge representation schemes, different
techniques of intelligent search, various methods for resolving uncertainty of data
and knowledge, different schemes for automated machine learning and many others.
Among the application areas of AI, we have Expert systems, Game-playing, and
Theorem-proving, Natural language processing, Image recognition, Robotics and
The subject of AI has been enriched with a wide discipline of knowledge from
Philosophy, Psychology, Cognitive Science, Computer Science, Mathematics and
Thus in figure, they have been referred to as the parent disciplines of AI.
An at-a-glance look at figure also reveals the subject area of AI and its application
Fig: AI its parent disciplines and application areas.
· Gaming − AI plays important role for machine to think of large number of
possible positions based on deep knowledge in strategic games. For example:
chess, river crossing, N-queens problems and etc.
· Natural Language Processing − Interact with the computer that understands
natural language spoken by humans.
· Expert Systems − Machine or software provide explanation and advice to the
· Vision Systems − Systems understand, explain, and describe visual input on the
· Speech Recognition − There are some AI based speech recognition systems have
ability to hear and express as sentences and understand their meanings while a
person talks to it. For example: Siri and Google assistant.
· Handwriting Recognition − the handwriting recognition software reads the text
written on paper and recognize the shapes of the letters and convert it into editable
· Intelligent Robots − Robots are able to perform the instructions given by a
The motivation for every researcher in artificial intelligence (AI) is to match the
multi-faceted aspects of intelligent systems and perhaps at some point develop a
system that can match human intelligence or even exceed it.
The aim of the present work is to advance arguments to substantiate the view
that the modeling of cognitive and biological systems should utilize concepts
emanating from both the self-organizing and representation list paradigms.
Indeed, both paradigms offer only an incomplete account of the nature of life,
biological or cognitive, which is given a more realistic explanation when both
paradigms are understood as complementary.
Philosophical and conceptual arguments are given to substantiate this inclusive
position, and how it relates to a web of existing philosophical viewpoints.
The position is strengthened by the definition of mathematical and
computational formalisms which show its relevance in the creation of practical
computational applications for information technology.
I intend the present work to be organized in a semiotic way with semantic,
syntactic, and pragmatic areas made explicit.
This way, the problem is explored philosophically, formally, and
I do not expect the three areas to fully support one another.
The philosophical part lays out the problem in general terms and proposes
conceptual arguments that should stand on their own.
The formal parts, can also stand alone since they represent mathematical
constructs valid in their own right.
In any case, they are proposed as formal tools to deal with certain aspects
of the larger philosophical issues.
Finally, the computational parts give some pragmatic validation to certain
aspects of the formal tools, by creating computational models of the larger
conceptual issues as well as practical applications valid on their own.
These computational applications, useful for the fields of data-mining and
optimization algorithms, offer the desired pragmatic validation of the philosophical
In so doing, they show that there are important advantages to be gained
from more inclusive, complementary, theories of artificial intelligence and artificial
life that acknowledge both self-organization and representation.
The philosophical and conceptual part of the dissertation starts with a
discussion of the divisions between representationalism and self-organization.
Self-organization is presented within a framework of emergence and of
levels of description.
I introduce the notion of selected self-organization as the backbone of the
evolutionary constructivist position, and defend the existence of a symbolic
dimension as a pragmatic result to increase the effectiveness of selected self-
I further discuss how these ideas relate to the study of natural language and
Following these ideas, I next propose an evolving semiotic conceptual
framework for this inclusive form of self-organization with both representational and
constructed facets. With natural language in mind, I develop a mathematical
tool based on fuzzy set and evidence theories called evidence set, proposed as a more
accurate model of cognitive categorization processes.
Evidence sets extend interval valued fuzzy sets to a belief based framework,
creating a method of formally modeling the contextual constraints of cognitive
Evidence sets are representational artifacts, but are also constrained by
subjective belief structures, which are two key elements of evolutionary
In addition, evidence sets capture all forms of uncertainty recognized in
generalized information theory, uncapturable by other set structures.
Finally, an extended theory of approximate reasoning is proposed based on
set-theoretic operations defined for evidence sets.
With evolutionary systems and artificial life in mind, I discuss the idea of
contextual genetic algorithms.
These computational models of natural selection are based on the existence
of intermediate levels between genotype and phenotype.
In other words, genetic descriptions do not encode directly for phenotypic
traits, but for the boundary conditions of intermediate dynamical systems which
self-organize into a set of phenotypical traits.
The indirect encoding of solutions for a particular problem in genetic
algorithms is referred to as contextual since the intermediate dynamical systems
may depend on inputs other than just the genetic description, such as environmental
That is, expression of chromosomes to solutions does not depend solely on
genetic information, but also on the system’s context.
Indirect genetic encoding is not only a more biologically correct model of
genetic natural selection, but it also allows the evolution of different solutions from
the same descriptions, which is important for adaptation, and additionally yields
tremendous genetic information compression.
Furthermore, conceptually, the marriage of selection and self organization
is the crux of evolutionary constructivism in evolutionary systems theory.
In order to validate evidence sets and contextual genetic algorithms as
relevant models, I explore them computationally in a number of problem areas.
Evidence sets are utilized in the development of a search method which acts
on several relational databases.
This search is based on the reduction of uncertainty stemming from
conflicts between the information stored in the various databases which define
Contextual genetic algorithms are utilized in two distinct models.
The first a model of RNA editing which shows that environmental factors
can control genetic translation ontogenetically.
The second an indirect encoding scheme based on fuzzy logic designed to
attain important information compression of genetic descriptions, which is validated
in the evolution of neural networks and cellular automata.
Both of these models show how the specific materiality of evolutionary
systems, or embodiment, both constrains and enable emergent, evolutionary,
classification, which is the thrust of evolutionary constructivism.
Rule-based systems or production systems are computer systems that use rules
to provide recommendations or diagnoses, or to determine a course of action in a
particular situation or to solve a particular problem.
A rule-based system consists of a number of components:
1. a database of rules (also called a knowledge base)
2. a database of facts
3. an interpreter, or inference engine
In a rule-based system, the knowledge base consists of a set of rules that
represent the knowledge that the system has.
The database of facts represents inputs to the system that are used to derive
conclusions, or to cause actions.
The interpreter, or inference engine, is the part of the system that controls the
process of deriving conclusions. It uses the rules and facts, and combines them
together to draw conclusions.
Using deduction to reach a conclusion from a set of antecedents (background (or)
past history) is called forward chaining.
An alternative method, backward chaining, starts from a conclusion and tries to
show it by following a logical path backward from the conclusion to a set of
antecedents that are in the database of facts.
• Forward chaining employs the system starts from a set of facts, and a set of rules, and tries
to find a way of using those rules and facts to deduce a conclusion or come up with a
suitable course of action.
• This is known as data-driven reasoning because the reasoning starts from a set of data
and ends up at the goal, which is the conclusion.
• When applying forward chaining, the first step is to take the facts in the fact database and
see if any combination of these matches all the antecedents of one of the rules in the rule
• When all the antecedents of a rule are matched by facts in the database, then this rule is
• Usually, when a rule is triggered, it is then fired, which means its conclusion is added to
the facts database.
• If the conclusion of the rule that has fired is an action or a recommendation, then the
system may cause that action to take place or the recommendation to be made.
•For example, consider the following set of rules that is used to control an elevator in a
IF on first floor
AND button is pressed on first floor
THEN open door
IF on first floor
AND button is pressed on second floor
THEN go to second floor
IF on first floor
AND button is pressed on third floor
THEN go to third floor
IF on second floor
AND button is pressed on first floor
AND already going to third floor
THEN remember to go to first floor later
• This represents just a subset of the rules that would be needed, but we can use it
to illustrate how forward chaining works.
• Let us imagine that we start with the following facts in our database:
At first floor
Button pressed on third floor
Today is Tuesday
• Now the system examines the rules and finds that Facts 1 and 2 match the
antecedents of Rule 3. Hence, Rule 3 fires, and its conclusion “Go to third floor”
is added to the database of facts. Presumably, this results in the elevator heading
toward the third floor.
• Note that Fact 3 was ignored altogether because it did not match the antecedents
of any of the rules.
• Now let us imagine that the elevator is on its way to the third floor and has
reached the second floor, when the button is pressed on the first floor. The fact
Button pressed on first floor.
• Is now added to the database, which results in Rule 4 firing.
• Now let us imagine that later in the day the facts database contains the following
At first floor
Button pressed on second floor
Button pressed on third floor
In this case, two rules are triggered—Rules 2 and 3. In such cases where
there is more than one possible conclusion, conflict resolution needs to be applied
to decide which rule to fire.
• Forward chaining applies a set of rules and facts to deduce whatever conclusions
can be derived, which is useful when a set of facts are present, but you do not know
what conclusions you are trying to prove.
• Forward chaining can be inefficient because it may end up proving a number of
conclusions that are not currently interesting.
• In such cases, where a single specific conclusion is to be proved, backward
chaining is more appropriate.
• In backward chaining, we start from a conclusion, which is the hypothesis we wish
to prove, and we aim to show how that conclusion can be reached from the rules and
facts in the database.
• The conclusion we are aiming to prove is called a goal, and so reasoning in this
way is known as goal-driven reasoning.
• Backward chaining is often used in formulating plans.
• A plan is a sequence of actions that a program decides to take to solve a particular
• Backward chaining can make the process of formulating a plan more efficient than
•Backward chaining in this way starts with the goal state, which is the set of
conditions the agent wishes to achieve in carrying out its plan. It now examines this
state and sees what actions could lead to it.
• For example, if the goal state involves a block being on a table, then one possible
action would be to place that block on the table.
• This action might not be possible from the start state, and so further actions need
to be added before this action in order to reach it from the start state.
• In this way, a plan can be formulated starting from the goal and working back
toward the start state.
• The benefit in this method is particularly clear in situations where the first state
allows a very large number of possible actions.
• In this kind of situation, it can be very inefficient to attempt to formulate a plan
using forward chaining because it involves examining every possible action, without
paying any attention to which action might be the best one to lead to the goal state.
• Backward chaining ensures that each action that is taken is one that will definitely
lead to the goal, and in many cases this will make the planning process far more
An expert system (ES) is a software system that captures human expertise for
supporting decision-making; this is useful for dealing with problems involving
incomplete information or large amounts of complex knowledge.
Expert systems are particularly useful for on-line operations in the control field
because they incorporate symbolic and rule-based knowledge that relate
situation and actions, and they also have the ability to explain and justify a line
The ES basically consists of knowledge base, database, reasoning machine,
interpretation mechanism, knowledge acquisition and user interface, which is
shown in Figure.
An expert system is a set of programs that manipulate encoded knowledge to
solve problems in a specialized domain that normally requires human expertise.
An expert system’s knowledge is obtained from expert sources and coded in a
form suitable for the system to use in its inference or reasoning processes.
The expert knowledge must be obtained from specialists or other sources of
expertise, such as texts, journal, articles and databases.
This type of knowledge usually requires much training and experience in some
specialized field such as medicine, geology, system configuration, or
Once a sufficient body of expert knowledge has been auquired, it must be
encoded in some form, loaded into a knowledge base, then tested, and refined
continually throughout the life of the system.
Characteristics Features of Expert Systems:
Expert systems differ from conventional computer system in several important ways
1. Expert systems use knowledge rather than data to control the solution process.
Much of the knowledge used in heuristic in nature rather than algorithmic.
2. The knowledge is encoded and maintained as an entity separate from the control
program. As such, it is not complicated together with the control program itself. This
permits the incremental addition and modification of the knowledge base without
recompilation of the control programs. Furthermore, it is possible in some cases to
use different knowledge bases with the same control programs to produce different
types of expert systems. Such systems are known as expert system shells since they
may be loaded with different knowledge bases.
3. Expert systems are capable of explaining how a particular conclusion was
reached, and why requested information is needed during a consultation. This is
important as it gives the user a chance to assess and understand the systems
reasoning ability, thereby improving the user’s confidence in the system.
4. Expert systems use symbolic representations for knowledge and perform their
inference through symbolic computations that closely resemble manipulations of
5. Expert systems often reason with met knowledge, that is, they reason with
knowledge about themselves, and their own knowledge limits and capabilities.
Architecture of an Expert System:
Typical expert system architecture is shown in Figure.
The knowledge base contains the specific domain knowledge that is used by an
expert to derive conclusions from facts.
In the case of a rule-based expert system, this domain knowledge is expressed in
the form of a series of rules.
The explanation system provides information to the user about how the inference
engine arrived at its conclusions. This can often be essential, particularly if the
advice being given is of a critical nature, such as with a medical diagnosis
If the system has used faulty reasoning to arrive at its conclusions, then the user may be
able to see this by examining the data given by the explanation system.
The fact database contains the case-specific data that are to be used in a particular case to
derive a conclusion.
In the case of a medical expert system, this would contain information that had been
obtained about the patient’s condition.
The user of the expert system interfaces with it through a user interface, which provides
access to the inference engine, the explanation system, and the knowledge-base editor.
The inference engine is the part of the system that uses the rules and facts to derive
conclusions. The inference engine will use forward chaining, backward chaining, or a
combination of the two to make inferences from the data that are available to it.
The knowledge-base editor allows the user to edit the information that is contained in the
The knowledge-base editor is not usually made available to the end user of the system but
is used by the knowledge engineer or the expert to provide and update the knowledge that
is contained within the system.
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