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33 Cards in this Set
- Front
- Back
what is knowledge?
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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. |
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Define the term "rule" in AI.
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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. |
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The IF part of a rule is called the ....
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antecedent (premise or condition)
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The THEN part of a rule is called the
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consequent (conclusion or
action) |
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Formal names for AND and OR operations?
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AND (conjunction), OR (disjunction)
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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’ |
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Name the five members in the development of an expert system.
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the domain expert, the
knowledge engineer, the programmer, the project manager and the end-user. |
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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 |
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What is the knowledge base in an expert system?
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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. |
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What is the database in an expert system?
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The database includes a set of facts used to match
against the IF (condition) parts of rules stored in the knowledge base. |
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What is the inference engine in an expert system?
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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. |
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What is the explanation facilities in an expert system?
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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. |
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What is Forward Chaining?
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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. |
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What is Backward Chaining?
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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. |
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Give 4 advantages of Rule based expert systems.
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Natural knowledge representation. An expert
usually explains the problem-solving procedure with expressions like “In such-and-such situation, I do so-and-so”. 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. |
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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 rules”, 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. |
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What is Fuzzy Logic?
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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. |
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How do you draw fuzzy sets compared to normal sets?
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Fuzzy sets are often drawn as sort of triangles instead of being rectangular shapes. These triangles have intersections at their corners.
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Mamdani fuzzy inference steps.
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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. |
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What is Michio Sugeno well known for contributing to fuzzy evaluatino?
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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. |
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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. |
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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. |
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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. |
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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. |
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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. |
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What is Roulette wheel selection?
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Roulette Wheel build a roulette wheel of all the chromosomes and organizes the chromosomes according to fitness.
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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. |
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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. |
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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. |
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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. |
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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. |
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Best combination?
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“British Police, German Mechanics, French Cuisine, Swiss Banking andItalian Love”
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What is a Neuro Fuzzy System?
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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. |