AI Final Study
Terms
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- what is knowledge?
-
Knowledge is a theoretical or practical
understanding of a subject or a domain.
Knowledge is also the sum of what is currently
known, and apparently knowledge is power. Those
who possess knowledge are called experts. - Define the term "rule" in AI.
-
The term rule in AI, which is the most commonly
used type of knowledge representation, can be
defined as an IF-THEN structure that relates given
information or facts in the IF part to some action in
the THEN part. A rule provides some description
of how to solve a problem. Rules are relatively
easy to create and understand. - The IF part of a rule is called the ....
- antecedent (premise or condition)
- The THEN part of a rule is called the
-
consequent (conclusion or
action) - Formal names for AND and OR operations?
- AND (conjunction), OR (disjunction)
-
Rules can represent relations, recommendations,
directives, strategies and heuristics: -
Relation
IF the ‘fuel tank’ is empty
THEN the car is dead
Recommendation
IF the season is autumn
AND the sky is cloudy
AND the forecast is drizzle
THEN the advice is ‘take an umbrella’
Directive
IF the car is dead
AND the ‘fuel tank’ is empty
THEN the action is ‘refuel the car’
Strategy
IF the car is dead
THEN the action is ‘check the fuel tank’;
step1 is complete
IF step1 is complete
AND the ‘fuel tank’ is full
THEN the action is ‘check the battery’;
step2 is complete
Heuristic
IF the spill is liquid
AND the ‘spill pH’ < 6
AND the ‘spill smell’ is vinegar
THEN the ‘spill material’ is ‘acetic acid’ - Name the five members in the development of an expert system.
-
the domain expert, the
knowledge engineer, the programmer, the
project manager and the end-user. - How would you go about selecting a problem as being a problem that should be solved by an expert System?
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The need for the solution justifies the cost and
effort for building an ES
Human expertise is not available in all
situations where it is needed
The problem may be solved using symbolic
reasoning
The problem domain is well structured and
does not require commonsense reasoning
The problem may not be solved using
traditional computing methods
Cooperative and articulate experts exist
The problem is of proper size and scope - What is the knowledge base in an expert system?
-
The knowledge base contains the domain
knowledge useful for problem solving. In a rulebased
expert system, the knowledge is represented
as a set of rules. Each rule specifies a relation,
recommendation, directive, strategy or heuristic
and has the IF (condition) THEN (action) structure.
When the condition part of a rule is satisfied, the
rule is said to fire and the action part is executed. - What is the database in an expert system?
-
The database includes a set of facts used to match
against the IF (condition) parts of rules stored in the
knowledge base. - What is the inference engine in an expert system?
-
The inference engine carries out the reasoning
whereby the expert system reaches a solution. It
links the rules given in the knowledge base with the
facts provided in the database. - What is the explanation facilities in an expert system?
-
The explanation facilities enable the user to ask
the expert system how a particular conclusion is
reached and why a specific fact is needed. An
expert system must be able to explain its reasoning
and justify its advice, analysis or conclusion. - What is Forward Chaining?
-
Forward chaining is the data-driven reasoning.
The reasoning starts from the known data and
proceeds forward with that data. Each time only
the topmost rule is executed. When fired, the rule
adds a new fact in the database. Any rule can be
executed only once. The match-fire cycle stops
when no further rules can be fired. - What is Backward Chaining?
-
Backward chaining is the goal-driven reasoning.
In backward chaining, an expert system has the goal
(a hypothetical solution) and the inference engine
attempts to find the evidence to prove it. First, the
knowledge base is searched to find rules that might
have the desired solution. Such rules must have the
goal in their THEN (action) parts. If such a rule is
found and its IF (condition) part matches data in the
database, then the rule is fired and the goal is
proved. However, this is rarely the case. - Give 4 advantages of Rule based expert systems.
-
Natural knowledge representation. An expert
usually explains the problem-solving procedure
with expressions like “In such-and-such situation, I
do so-and-soâ€. These expressions can be
represented quite naturally as IF-THEN production
rules.
Uniform structure. Production rules have the
uniform IF-THEN structure. Each rule is an
independent piece of knowledge. The very syntax
of production rules enables them to be selfdocumented.
Separation of knowledge from its processing.
The structure of a rule-based expert system
provides an effective separation of the knowledge
base from the inference engine. This makes it
possible to develop different applications using the
same expert system shell.
Dealing with incomplete and uncertain
knowledge. Most rule-based expert systems are
capable of representing and reasoning with
incomplete and uncertain knowledge. - Name 3 disadvantages of Rule based expert systems.
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Opaque relations between rules. Although the
individual production rules are relatively simple and
self-documented, their logical interactions within the
large set of rules may be opaque. Rule-based
systems make it difficult to observe how individual
rules serve the overall strategy.
Ineffective search strategy. The inference engine
applies an exhaustive search through all the
production rules during each cycle. Expert systems
with a large set of rules (over 100 rules) can be slow,
and thus large rule-based systems can be unsuitable
for real-time applications.
Inability to learn. In general, rule-based expert
systems do not have an ability to learn from the
experience. Unlike a human expert, who knows
when to “break the rulesâ€, an expert system cannot
automatically modify its knowledge base, or adjust
existing rules or add new ones. The knowledge
engineer is still responsible for revising and
maintaining the system. - What is Fuzzy Logic?
-
Fuzzy logic is a set of mathematical principles
for knowledge representation based on degrees
of membership.
Unlike two-valued Boolean logic, fuzzy logic is
multi-valued. It deals with degrees of
membership and degrees of truth. Fuzzy logic
uses the continuum of logical values between 0
(completely false) and 1 (completely true). Instead
of just black and white, it employs the spectrum of
colours, accepting that things can be partly true and
partly false at the same time. - How do you draw fuzzy sets compared to normal sets?
- Fuzzy sets are often drawn as sort of triangles instead of being rectangular shapes. These triangles have intersections at their corners.
- Mamdani fuzzy inference steps.
-
The Mamdani-style fuzzy inference process is
performed in four steps:
fuzzification of the input variables,
rule evaluation,
aggregation of the rule outputs, and finally
defuzzification. - What is Michio Sugeno well known for contributing to fuzzy evaluatino?
-
Michio Sugeno suggested to use a single spike, a
singleton, as the membership function of the rule
consequent. A singleton, or more precisely a fuzzy
singleton, is a fuzzy set with a membership
function that is unity at a single particular point on
the universe of discourse and zero everywhere else.
Sugeno-style fuzzy inference is very similar to the
Mamdani method. Sugeno changed only a rule
consequent. Instead of a fuzzy set, he used a
mathematical function of the input variable. The
format of the Sugeno-style fuzzy rule is
IF x is A
AND y is B
THEN z is f (x, y)
where x, y and z are linguistic variables; A and B are
fuzzy sets on universe of discourses X and Y,
respectively; and f (x, y) is a mathematical function. - How to make a decision on which method to apply − Mamdani or Sugeno??
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Mamdani method is widely accepted for capturing
expert knowledge. It allows us to describe the
expertise in more intuitive, more human-like
manner. However, Mamdani-type fuzzy inference
entails a substantial computational burden.
On the other hand, Sugeno method is
computationally effective and works well with
optimisation and adaptive techniques, which makes
it very attractive in control problems, particularly
for dynamic nonlinear systems. - What is a Artificial Neural Network?
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A neural network can be defined as a model of
reasoning based on the human brain. The brain
consists of a densely interconnected set of nerve
cells, or basic information-processing units, called
neurons. - Structure of a Neuron?
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A neuron consists of a cell body, soma, a
number of fibers called dendrites, and a single
long fiber called the axon. - How does an individual neuron work?
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The neuron computes the weighted sum of the input
signals and compares the result with a threshold
value, θ. If the net input is less than the threshold,
the neuron output is –1. But if the net input is greater
than or equal to the threshold, the neuron becomes
activated and its output attains a value +1. - What are Genetic Algorithms?
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All methods of evolutionary computation simulate
natural evolution by creating a population of
individuals, evaluating their fitness, generating a
new population through genetic operations, and
repeating this process a number of times.
We will start with Genetic Algorithms (GAs) as
most of the other evolutionary algorithms can be
viewed as variations of genetic algorithms.
The GA uses a measure of fitness of individual
chromosomes to carry out reproduction. As
reproduction takes place, the crossover operator
exchanges parts of two single chromosomes, and
the mutation operator changes the gene value in
some randomly chosen location of the
chromosome. - What is Roulette wheel selection?
- Roulette Wheel build a roulette wheel of all the chromosomes and organizes the chromosomes according to fitness.
- What is the Mutation Operator?
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Mutation is a background operator. Its role is to
provide a guarantee that the search algorithm is
not trapped on a local optimum. - What is Genetic Programming?
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One of the central problems in computer science is
how to make computers solve problems without
being explicitly programmed to do so.
Genetic programming offers a solution through the
evolution of computer programs by methods of
natural selection. - Basic evolution strategies
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In its simplest form, termed as a (1+1)-evolution
strategy, one parent generates one offspring per
generation by applying normally distributed
mutation. The (1+1)-evolution strategy can be
implemented as follows:
Choose the number of parameters N to
represent the problem, and then determine a feasible
range for each parameter: Define a standard deviation for each parameter and
the function to be optimised.
Randomly select an initial value for each
parameter from the respective feasible range. The
set of these parameters will constitute the initial
population of parent parameters:
Calculate the solution associated with the
parent parameters:
X = f (x1, x2, . . . , xN)
Create a new (offspring) parameter by
adding a normally distributed random variable a
with mean zero and pre-selected deviation δ to each
parent parameter:
i = 1, 2, . . . , N
Normally distributed mutations with mean zero
reflect the natural process of evolution (smaller
changes occur more frequently than larger ones).
Calculate the solution associated with the
offspring parameters:
xi⬲ = xi + a (0, δ)
X⬲ = f (x1⬲, x2⬲ , . . . , x⬲N)
Compare the solution associated with the
offspring parameters with the one associated with
the parent parameters. If the solution for the
offspring is better than that for the parents, replace
the parent population with the offspring
population. Otherwise, keep the parent
parameters.
Go to Step 4, and repeat the process until a
satisfactory solution is reached, or a specified
number of generations is considered. - What is a Hybrid System?
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A hybrid intelligent systemis one that combines
at least two intelligent technologies. For example,
combining a neural network with a fuzzy system
results in a hybrid neuro-fuzzy system. - What is Soft Computing?
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The combination of probabilistic reasoning, fuzzy
logic, neural networks and evolutionary
computation forms the core of soft computing, an
emerging approach to building hybrid intelligent
systems capable of reasoning and learning in an
uncertain and imprecise environment. - Best combination?
- “British Police, German Mechanics, French Cuisine, Swiss Banking andItalian Loveâ€
- What is a Neuro Fuzzy System?
-
A neuro-fuzzy system is a neural network which
is functionally equivalent to a fuzzy inference
model. It can be trained to develop IF-THEN
fuzzy rules and determine membership functions
for input and output variables of the system.
Expert knowledge can be incorporated into the
structure of the neuro-fuzzy system. At the same
time, the connectionist structure avoids fuzzy
inference, which entails a substantial
computational burden.