Artificial Intelektual
Is an intelligent computer program that uses knowledge and inference procedures to solve problems that are difficult enough to require significant human expertise for their solutions.
Expert system
Knowledge base
Inference engine
Explanation facility
Is used to acquire insights from the information housed in the knowledge base.
Inference engine
Knowledge base
Expert system
Explanation facility
Contains a collection of information in a given field.
Knowledge base
Expert system
Inference engine
Explanation facility
Allows the user to ask the expert system how it reached a particular conclusion and why a specific task (fact) is needed.
Explanation facility
Inference engine
Knowledge base
Expert system
Fix: (by order)
Expert Systems should be able to:
I. Solve complex problems with the same (or greater) solvency as human experts
II. Consider multiple hypotheses simultaneously.
III. Perform heuristic reasoning.
IV. Work with data containing errors.
V. Respond in a reasonable amount of time.
VI. Explain the steps of the reasoning process. It should justify how it arrived at a particular conclusion.
VII. Maintain reliability and should not crash.
VIII. Perform at the level of a human expert.
Expert Systems should be able to:
I. Solve complex problems with the same (or greater) solvency as human experts
II. Consider multiple hypotheses simultaneously.
III. Perform heuristic reasoning.
IV. Work with data containing errors.
V. Respond in a reasonable amount of time.
VI. Explain the steps of the reasoning process. It should justify how it arrived at a particular conclusion.
VII. Maintain reliability and should not crash.
VIII. Perform at the level of a human expert.
I, III, IV, II, VIII, V, VII, and VI
I, III, IV, II, VI, V, VII, and VIII
I, II, III, IV, V, VI, VII, and VIII
VIII, VII, VI, V, IV, III, II, and I
Fix: (by order)
The following are the fundamental modules of an expert system:
I. Knowledge base
II. User interface
III. Explanation facility
IV. Inference engine
V. Database
VI. External interface
VII. Knowledge acquisition facility
The following are the fundamental modules of an expert system:
I. Knowledge base
II. User interface
III. Explanation facility
IV. Inference engine
V. Database
VI. External interface
VII. Knowledge acquisition facility
I, IV, II, III, VII, VI, and V
I, II, III, IV, V, VI, and VII
VII, VI, V, IV, III, II, and I
I, V, III, II, VI, VII, and IV
Based on facts
Factual knowledge
Heuristic knowledge
Control system
Rule interpreter
Based on practice, evaluation, experiences, and the ability to guess
Heuristic knowledge
Control system
Rule interpreter
Explanation mechanism
Determines the order of testing in the knowledge base rules
Control system
Rule interpreter
Explanation mechanism
Factual knowledge
Defines the Boolean application rules
Rule interpreter
Explanation mechanism
Heuristic knowledge
Control system
Justifies the outcome to the user with the reasoning process
Explanation mechanism
Rule interpreter
Control system
Factual knowledge
Repeatedly applies the rules to the working memory, adding new information (obtained from the rules' conclusions) until a goal state is produced or confirmed.
Inference engine
Expert system
Knowledge base
Explanation facility
The inference process moves from the facts of the case to a goal (conclusion). The inference engine attempts to match each rule's condition (IF) in the knowledge base with the facts currently available in the working memory.
Forward chaining
Backward chaining
Side chaining
Rotation chaining
The inference engine attempts to match the assumed conclusion to the goal or sub-goal state with the conclusion (THEN) part of the rule. If such a rule is found, its premise becomes the new sub-goal.
Backward chaining
Forward chaining
Side chaining
Rotation chaining
Are used to solve the open-ended problems of a design or those that involve planning.
Forward chaining
Backward chaining
Side chaining
Rotation chaining
Is best suited for applications in which the possible conclusions are limited in number and well defined.
Backward chaining
Forward chaining
Side chaining
Rotation chaining
(Find the right answ)
The user interface helps in explaining how the expert system has arrived at a particular recommendation. The explanation may appear in the following forms:
The user interface helps in explaining how the expert system has arrived at a particular recommendation. The explanation may appear in the following forms:
Natural language displayed on the screen
Verbal narrations in natural language
A listing of rule numbers displayed on the screen
Defines the Boolean application rules
(Find right ans) The user interface should:
Help users accomplish their goals in the shortest possible way
E designed to work for users' existing or desired work practices
Adapt to the user's requirements, not the other way around
Make efficient use of the user's input
Includes eliciting, collecting, analyzing, modeling, and validating knowledge for knowledge engineering and management projects.
Knowledge acquisition
External interface
Database
Explanation facility
It is considered the major bottleneck in expert system development.
Knowledge acquisition
External interface
Database
Explanation facility
Allows an expert system to work with external files using programs written in conventional programming languages like C and C++.
External interface
Knowledge acquisition
Database
Explanation facility
It provides the communication link between the expert system and the external environment.
External interface
Knowledge acquisition
Database
Explanation facility
Is a collection of organized information that can easily be accessed, manage, and updated.
Database
External interface
Knowledge acquisition
Explanation facility
It is used by the inference engine to hold data while it is working on a problem.
Database
Knowledge acquisition
External interface
Explanation facility
(Find right ans) It holds the data about the current task, which include the following
The user's answers to questions
Any data from outside sources
Any intermediate results of the reasoning
Any conclusions reached so far
The rule has a conditional part on the left-hand side and a conclusion or action part on the right-hand side.
Rule-based system architecture
Non-production systems
Knowledge engineer
Knowledge sources
Is the most common form of architecture used in expert and other knowledge-based systems.
Rule-based system architecture
Non-production systems
Knowledge engineer
Knowledge sources
Use more structured representation schemes, such as the semantic/associative network, frames, tree structure (decision trees), and neural networks.
Non-production systems
Rule-based system architecture
Knowledge engineer
Knowledge sources
Refers to a special type of knowledge-based system that uses different knowledge sources that communicate through a common information field.
Blackboard system architecture
Knowledge sources
Control unit
Neural networks
Are independent modules that contain the knowledge needed for problem solving.
Knowledge sources
Control unit
Neural networks
Blackboard system architecture
Is used as a global database for sharing different information as input data, partial solutions, alternatives and final solutions.
Blackboard
Control unit
Knowledge sources
Neural networks
Determines which knowledge sources to execute for an optimal problem solution.
Control unit
Knowledge sources
Blackboard
Neural networks
are computing systems modeled on the human brain’s mesh-like network of interconnected processing elements called neurons
Neural networks
Blackboard
Control unit
Knowledge sources
Is the process of training a a model, to make useful predictions from data.
Machine learning
Supervised learning
Unsupervised learning
Reinforcement learning
Represents the mathematical relationship between the elements of data that an ML system uses to make predictions.
Machine learning model
Machine learning
Supervised learning
Unsupervised learning
The machine is provided with an established set of data (labeled data).
Supervised learning
Unsupervised learning
Machine learning model
Machine learning
The model predicts the values of a continuous variable.
Regression
Classification
Binary classification model
Multiclass classification model
A classification model predicts the likelihood that an item belongs to a distinct set of categories.
Classification
Regression
Binary classification model
Multiclass classification model
Outputs a value from a class that contains only two (2) values, for example, a model that outputs either rain or no rain.
Binary classification model
Multiclass classification model
Classification
Regression
Outputs a value from a class that contains more than two (2) values, for example, a model that can output rain, hail, snow, or sleet.
Multiclass classification model
Binary classification model
Classification
Regression
The model makes predictions based on a set of data that is not provided with a specific answer (unlabeled data).
Unsupervised learning
Supervised learning
Reinforcement learning
Clustering
Items with common properties are grouped, for example, a model that suggests contacts to add to a group.
Clustering
Reinforcement learning
Unsupervised learning
Supervised learning
The model learns through a trial and error. The algorithm receives feedback from the analysis of the data so the user is guided to the best outcome.
Reinforcement learning
Clustering
Unsupervised learning
Supervised learning
Is based on the following core concepts: data, model, training, evaluation, and inference.
Supervised machine learning
Supervised learning
Unsupervised learning
Unsupervised machine learning
Is the driving force of ML. We store related data in datasets, for example, a dataset of weather information.
Data
Datasets
Features
Label
Are made up of individual examples or entries that contain features and a label.
Datasets
Data
Features
Label
Are the values used to predict the label, while the label is the answer or the value the model has to predict
Features
Model
Data
Label
Is the complex collection of numbers that define the mathematical relationship from specific input feature patterns to specific output label values
Model
Features
Data
Datasets
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