Artificial Intelligence And Machine Learning Quiz
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Do you think you know enough about artificial intelligence and machine learning? If yes, play this quiz and prove it to us. This quiz is specially designed for experts who think they can easily answer any machine learning and artificial intelligence question. Some questions are related to the Turing machine also in this quiz. Why don't you give this quiz a try? Your scores will clear all your doubts whether you still need more practice or you're really a pro. All the best, buddy!
Questions and Answers
- 1.
Which British mathematician is often credited as being the key founder of AI?
- A.
Isaac Asimov
- B.
John McCarthy
- C.
Marvin Minsky
- D.
Alan Turing
Correct Answer
D. Alan TuringExplanation
Alan Turing is often credited as being the key founder of AI because of his groundbreaking work in the field of theoretical computation and artificial intelligence. Turing's concept of the "Turing machine" laid the foundation for modern computer science and his paper "Computing Machinery and Intelligence" introduced the idea of a machine that could exhibit intelligent behavior. His contributions to AI, including the development of the concept of the Turing Test, have had a lasting impact on the field and cemented his status as a key figure in the early development of AI.Rate this question:
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- 2.
In which year did Alan Turing present his famous Turing Test?
- A.
1940
- B.
1945
- C.
1950
- D.
1955
Correct Answer
C. 1950Explanation
Alan Turing presented his famous Turing Test in the year 1950. This test was designed to determine a machine's ability to exhibit intelligent behavior that is indistinguishable from that of a human. Turing proposed that if a machine can successfully convince a human evaluator that it is human during a conversation, then it can be considered as having passed the test. The year 1950 marks the presentation of this groundbreaking test by Turing.Rate this question:
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- 3.
Consider the overall ”picture” of AI which will be represented as sets. In total we have a set for DL, ML and AI. Which of the following 6 statements are true?
- A.
Machine Learning is the direct superset of AI
- B.
Machine Learning is the direct superset of Deep Learning
- C.
Machine Learning is the direct subset of AI
- D.
Deep Learning is the direct superset of AI
- E.
Deep Learning is the direct subset of AI
- F.
Deep Learning is the direct subset of Machine Learning
Correct Answer(s)
B. Machine Learning is the direct superset of Deep Learning
C. Machine Learning is the direct subset of AI
F. Deep Learning is the direct subset of Machine LearningExplanation
Machine Learning is the direct superset of Deep Learning because Deep Learning is a specialized form of Machine Learning that focuses on neural networks. Machine Learning is the direct subset of AI because Machine Learning is a subfield of AI that focuses on algorithms and models that enable computers to learn from and make predictions or decisions based on data. Deep Learning is the direct subset of Machine Learning because Deep Learning is a specific technique within the broader field of Machine Learning that uses neural networks with multiple layers to learn and extract complex patterns from data.Rate this question:
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- 4.
AI is about making computer based humans
- A.
True
- B.
False
Correct Answer
B. FalseExplanation
The statement "AI is about making computer based humans" is incorrect. Artificial Intelligence (AI) is the field of study and development of computer systems that can perform tasks that would typically require human intelligence. While AI aims to mimic human intelligence, it does not involve making computer-based humans. Therefore, the correct answer is False.Rate this question:
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- 5.
The turing test is about to beat humans in chess.
- A.
True
- B.
False
Correct Answer
B. FalseExplanation
The statement is false because the Turing test is not about beating humans in chess. The Turing test is a test of a machine's ability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human. It is not specifically focused on chess or any other specific task. Therefore, the statement is incorrect.Rate this question:
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- 6.
What is Machine Learning?
- A.
Is only used in mechanical machines like robots
- B.
A other word for Deep Learning
- C.
A subsector of Artificial Intelligence
- D.
Was founded in the 21st century
- E.
Computer programs that automatically improve with experience
- F.
Makes se of algorithms and statistics to analyse and draw inferences from patterns in data
Correct Answer(s)
C. A subsector of Artificial Intelligence
E. Computer programs that automatically improve with experience
F. Makes se of algorithms and statistics to analyse and draw inferences from patterns in dataExplanation
Machine Learning is a subsector of Artificial Intelligence that involves computer programs automatically improving with experience. It makes use of algorithms and statistics to analyze and draw inferences from patterns in data. This explanation accurately describes the concept of Machine Learning, highlighting its relationship with Artificial Intelligence and its reliance on algorithms and statistical analysis to learn from data.Rate this question:
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- 7.
When can a machine be considered as intelligent?
- A.
Solving mathematical equations
- B.
Having a brain (CPU)
- C.
Running a straight forward programmed task without errors
- D.
Passing the turing test
Correct Answer
D. Passing the turing testExplanation
Passing the Turing test is considered a benchmark for determining whether a machine can be considered intelligent. The Turing test involves a human evaluator engaging in a conversation with a machine and a human, without knowing which is which. If the machine is able to convince the evaluator that it is the human, it is considered to have exhibited intelligent behavior. This test evaluates the machine's ability to understand and respond to natural language, exhibit human-like behavior, and demonstrate a level of intelligence that is comparable to a human.Rate this question:
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- 8.
What is true for an algorithm?
- A.
Can have different solutions for the same input if it is used multiple times
- B.
A good algorithm solves the task in less time
- C.
Different algorithms can lead to the same result
- D.
Is made by Artificial Intelligence so there are no human made algorithms
- E.
Is only used in computer science so it has no real world applications
- F.
Is a step by step procedure
Correct Answer(s)
B. A good algorithm solves the task in less time
C. Different algorithms can lead to the same result
F. Is a step by step procedureExplanation
A good algorithm solves the task in less time because efficiency is one of the key characteristics of a good algorithm. Different algorithms can lead to the same result because there can be multiple approaches to solving a problem. An algorithm is a step by step procedure as it consists of a sequence of well-defined instructions to solve a problem.Rate this question:
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- 9.
Who showed how to write logic in the form of analytical equations?
- A.
George Boole
- B.
Leonardo da Vinci
- C.
Ada Lovelace
- D.
Ren´e Descartes
Correct Answer
A. George BooleExplanation
George Boole showed how to write logic in the form of analytical equations. He developed Boolean algebra, which is a mathematical system that represents logic using symbols and equations. Boole's work laid the foundation for modern computer science and digital logic circuits. His ideas revolutionized the way logic is expressed and analyzed, and his work is still widely used in fields such as computer programming and electrical engineering today.Rate this question:
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- 10.
What are the three origional Laws of Robotic of Isaac Asimov.
- A.
A robot must protect its own existence as long as such protection does not conflict with the First or Second Law.
- B.
A Robot has to defend humanity from any harm
- C.
A robot may not injure a human being or, through inaction, allow a human being to come to harm.
- D.
A Robot must improve humanity as long as it does not conflict with the First or Second Law.
- E.
A Robot should comply with all existing laws and human rights, including privacy, except where such orders would conflict with the First Law.
- F.
A robot must obey the orders given it by human beings except where such orders would conflict with the First Law.
Correct Answer(s)
A. A robot must protect its own existence as long as such protection does not conflict with the First or Second Law.
C. A robot may not injure a human being or, through inaction, allow a human being to come to harm.
F. A robot must obey the orders given it by human beings except where such orders would conflict with the First Law.Explanation
The three original Laws of Robotics by Isaac Asimov are as follows:
1. A robot may not injure a human being or, through inaction, allow a human being to come to harm. This law emphasizes the importance of preserving human safety and preventing harm.
2. A robot must obey the orders given it by human beings except where such orders would conflict with the First Law. This law highlights the necessity for robots to follow human commands, unless doing so would cause harm to a human.
3. A robot must protect its own existence as long as such protection does not conflict with the First or Second Law. This law emphasizes the self-preservation aspect of robots, ensuring that they can continue to function and carry out their duties, as long as it doesn't contradict the first two laws.Rate this question:
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- 11.
Who defined the first chess program in Plankalkul?
- A.
Sissa ibn Dahir
- B.
Konrad Zuse
- C.
Hoki Kunihito
- D.
Alan Turing
Correct Answer
B. Konrad ZuseExplanation
Konrad Zuse is the correct answer because he was a German engineer and computer pioneer who is credited with creating the first chess program in Plankalkul. Plankalkul was a programming language that Zuse developed in the 1940s, and he used it to create a chess program called "Chess-Plankalkul." This program was able to play a complete game of chess against a human opponent, making it the first of its kind. Zuse's work in developing this chess program was a significant milestone in the history of computer science and artificial intelligence.Rate this question:
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- 12.
The first ideas about AI systems where in the 20th century.
- A.
True
- B.
False
Correct Answer
A. TrueExplanation
The statement is stating that the first ideas about AI systems were in the 20th century. This means that the concept of AI systems was developed and discussed during the 20th century. Therefore, the answer "True" is correct as it aligns with the statement provided.Rate this question:
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- 13.
What was the first mythical automaton with artificial intelligence?
- A.
Hephaistos
- B.
Optimus Prime
- C.
Argonaut
- D.
Talos
Correct Answer
D. TalosExplanation
Talos was the first mythical automaton with artificial intelligence. In Greek mythology, Talos was a giant bronze automaton created by Hephaistos, the god of blacksmiths and craftsmen. Talos was given the ability to move and think on his own, making him the first automaton with artificial intelligence. He was tasked with protecting the island of Crete and preventing any invaders from entering. Talos would patrol the shores, throwing rocks at approaching ships and heating himself up to burn any enemies. His advanced capabilities and independent thinking set him apart as the first mythical automaton with artificial intelligence.Rate this question:
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- 14.
Who said :” all B’s are A, All C’s are B therfore all C’s are A’s
- A.
Robert Bacon
- B.
Sokrates
- C.
Aristotle
- D.
Leonardo da Vinci
Correct Answer
C. AristotleExplanation
Aristotle is the correct answer because he was a Greek philosopher who made significant contributions to logic and reasoning. This statement reflects a logical syllogism, which is a form of deductive reasoning. In this case, it follows the pattern of a categorical syllogism, where the first premise states that all B's are A, the second premise states that all C's are B, and the conclusion states that all C's are A. Aristotle's work in logic and reasoning laid the foundation for many principles still used in philosophy and mathematics today.Rate this question:
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- 15.
When was the ”Birth” of AI
- A.
1969
- B.
1956
- C.
1959
- D.
1971
Correct Answer
B. 1956Explanation
In 1956, the "Birth" of AI occurred. This refers to the Dartmouth Conference, where the term "Artificial Intelligence" was coined and the field of AI was officially established. The conference brought together researchers and experts to discuss the potential of creating machines that could simulate human intelligence. This event marked a significant milestone in the history of AI, paving the way for further advancements and research in the field.Rate this question:
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- 16.
Which of the following disciplines are main contributers to AI?
- A.
Neuroscience
- B.
Ecology
- C.
Logic
- D.
Sports
- E.
Psychology
- F.
Architecture
Correct Answer(s)
A. Neuroscience
C. Logic
E. PsychologyExplanation
The main contributors to AI are disciplines that involve understanding the human mind and behavior, as well as logical reasoning. Neuroscience studies the brain and its functions, which is crucial in developing AI systems that mimic human cognition. Logic provides the foundation for AI algorithms and reasoning processes. Psychology contributes to AI by studying human behavior and decision-making, which helps in designing intelligent systems. Therefore, neuroscience, logic, and psychology are the main disciplines that contribute to AI.Rate this question:
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- 17.
Alan Turing coined the term ”Artificial Intelligence” in 1956 at the Dartmouth Conference.
- A.
True
- B.
False
Correct Answer
B. FalseExplanation
Alan Turing did not coin the term "Artificial Intelligence" in 1956 at the Dartmouth Conference. The term was actually coined by John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon during the Dartmouth Conference in 1956. Alan Turing is widely recognized for his contributions to the field of computer science and artificial intelligence, but he did not specifically coin the term.Rate this question:
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- 18.
Which of the following were present at the Dartmouth conference and then dominated the field for the next 20 years?
- A.
Herbert Simon
- B.
Magnus Carlsen
- C.
Ludwig Morgenstein
- D.
Allan Newell
- E.
Nygoen Hirohita
- F.
John McCarthy
Correct Answer(s)
A. Herbert Simon
D. Allan Newell
F. John McCarthyExplanation
Herbert Simon, Allan Newell, and John McCarthy were present at the Dartmouth conference and then dominated the field for the next 20 years. These individuals were pioneers in the field of artificial intelligence (AI) and made significant contributions to its development. Herbert Simon was known for his work on problem-solving and decision-making, Allan Newell for his research on computer science and cognitive psychology, and John McCarthy for his role in the development of the programming language LISP and the concept of AI. Together, they played a crucial role in shaping the field of AI and their work had a lasting impact for the next two decades.Rate this question:
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- 19.
” A robot may not injure a human being or, through inaction, allow a human being to come to harm.“ is one of Asimov’s Three Laws of Robotics.
- A.
True
- B.
False
Correct Answer
A. TrueExplanation
This statement is true because it reflects one of Asimov's Three Laws of Robotics, which state that a robot must not harm a human being or, by not taking any action, allow harm to come to a human being. These laws were created to ensure the safety and well-being of humans in the presence of robots, emphasizing the importance of protecting human life.Rate this question:
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- 20.
What was the name of the first turing-complete digital computer?
- A.
FTDC
- B.
TCDC
- C.
ENIAC
- D.
DCK1
Correct Answer
C. ENIACExplanation
ENIAC is the correct answer because it was the first turing-complete digital computer. ENIAC, which stands for Electronic Numerical Integrator and Computer, was developed in the 1940s and was capable of performing a wide range of calculations. It was a significant milestone in the history of computing and laid the foundation for future advancements in the field.Rate this question:
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- 21.
Which of the following artificial entities did Hephaistos build?
- A.
Bellows
- B.
Orchestra Players
- C.
Cooks
- D.
Black Smiths
- E.
Waiters
- F.
Tripods
Correct Answer(s)
A. Bellows
E. Waiters
F. TripodsExplanation
Hephaistos, the Greek god of blacksmiths and craftsmen, is known for creating various artificial entities. Bellows are devices used to produce a strong blast of air to fuel fires, making them essential tools for blacksmiths. Waiters, although not specifically mentioned in Greek mythology, could be considered artificial entities as they are created to serve and assist in various tasks. Tripods, on the other hand, are three-legged structures or vessels that were often created by Hephaistos. Therefore, the correct answer includes Bellows, Waiters, and Tripods as artificial entities built by Hephaistos.Rate this question:
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- 22.
Why brought Deep Learning unexpected breakthroughs in diverse areas?
- A.
It was the first successfull knowledge-intensive system
- B.
It came up with fundamentally new ideas and techniques.
- C.
Smaller models and less training data were needed.
- D.
Much more training data, bigger models and computational power were available
Correct Answer
D. Much more training data, bigger models and computational power were availableExplanation
Deep learning brought unexpected breakthroughs in diverse areas because much more training data, bigger models, and computational power were available. With access to a larger amount of data, deep learning models could learn more complex patterns and make more accurate predictions. Additionally, the availability of bigger models allowed for more complex architectures and improved performance. The increase in computational power also enabled faster training and inference, making deep learning more practical and efficient in various applications.Rate this question:
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- 23.
The idea of Artificial Intelligence is relatively young and only emerged in the 20th century.
- A.
True
- B.
False
Correct Answer
B. FalseExplanation
The idea of Artificial Intelligence is not relatively young and did not only emerge in the 20th century. The concept of artificial beings or intelligent machines can be traced back to ancient civilizations, such as Greek mythology and ancient Egyptian and Chinese texts. However, the term "Artificial Intelligence" was coined in the 1950s, which is why it is often associated with the 20th century.Rate this question:
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- 24.
What did Leonardo da Vinci design in 1495?
- A.
First logical statements combined in a addition machine
- B.
The first humanoid robots (tripod, waiters and bellows)
- C.
The first bird like flying machine
- D.
The first humanoid mechanical knight
Correct Answer
D. The first humanoid mechanical knightExplanation
In 1495, Leonardo da Vinci designed the first humanoid mechanical knight. This invention showcased his engineering skills and creativity. The humanoid mechanical knight was a groundbreaking creation that demonstrated da Vinci's ability to design and construct complex mechanical systems. This invention paved the way for future advancements in robotics and automation.Rate this question:
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- 25.
Which of the following machines are NOT covered in the ancienct history of artificial intelligence?
- A.
Talos the giant intelligent bronze robot
- B.
James Watt’s steam engine
- C.
The chess playing turk
- D.
Clay golem from Prague
Correct Answer
B. James Watt’s steam engineExplanation
The correct answer is James Watt's steam engine. This machine is not covered in the ancient history of artificial intelligence because it was developed during the Industrial Revolution in the 18th century, while artificial intelligence research and development began much later. Ancient history refers to a period before the Middle Ages, and the development of steam engines falls outside of this timeframe.Rate this question:
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- 26.
Who wrote logic in the form of analytical equations first?
- A.
John McCarthy
- B.
George Boole
- C.
Ada Lovelace
- D.
Alan Turing
Correct Answer
B. George BooleExplanation
George Boole is considered the first person to write logic in the form of analytical equations. He developed a mathematical system called Boolean algebra, which uses symbols and equations to represent logical operations. Boole's work laid the foundation for modern computer science and digital logic, as his algebraic system became the basis for designing and analyzing electronic circuits. His contributions to logic and mathematics have had a significant impact on various fields, including computer science, philosophy, and engineering.Rate this question:
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- 27.
Who are founding fathers of AI?
- A.
Alan Turing
- B.
George Boole
- C.
Ada Lovelace
- D.
Allan Newell
- E.
John McCarthy
- F.
Marvin Minsky
Correct Answer(s)
D. Allan Newell
E. John McCarthy
F. Marvin MinskyExplanation
The founding fathers of AI are Allan Newell, John McCarthy, and Marvin Minsky. These individuals made significant contributions to the field of artificial intelligence. Allan Newell was a computer scientist who developed the General Problem Solver, a program that could solve a wide range of problems. John McCarthy coined the term "artificial intelligence" and organized the Dartmouth Conference, which is considered the birth of AI as a field of study. Marvin Minsky was a cognitive scientist and co-founder of the MIT AI Lab, where he made pioneering contributions to the development of AI technologies.Rate this question:
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- 28.
What were early successes?
- A.
First weather prediction from a computer based on previous data in prague
- B.
Chess playing machine beats former grandmaster
- C.
Pioneering many ideas in game playing and machine learning
- D.
Checker game playing machine beating a regional master
- E.
First russion to english language converter
- F.
Logic orientated advice taker
Correct Answer(s)
B. Chess playing machine beats former grandmaster
C. Pioneering many ideas in game playing and machine learning
D. Checker game playing machine beating a regional masterExplanation
The early successes mentioned in the answer include the chess playing machine beating a former grandmaster, pioneering many ideas in game playing and machine learning, and the checker game playing machine beating a regional master. These achievements demonstrate advancements in artificial intelligence and machine learning in the field of game playing.Rate this question:
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- 29.
Why is ”local search” not the optimal strategy for sudoku solving?
- A.
No guarantee for completeness
- B.
The puzzle solving consists of random patterns
- C.
Uses a lot of memory
- D.
Takes a lot of time to solve
Correct Answer
A. No guarantee for completenessExplanation
Local search is not the optimal strategy for sudoku solving because it does not provide a guarantee for completeness. This means that there is no assurance that the local search algorithm will be able to find a solution for every sudoku puzzle. While local search may be able to find solutions for some puzzles, it is not a reliable method for solving sudoku puzzles in general.Rate this question:
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- 30.
Does in search algorithms, a heuristic often denotes a function that estimates the quality of a given state?
- A.
True
- B.
False
Correct Answer
A. TrueExplanation
In search algorithms, a heuristic is commonly used to estimate the quality of a given state. Heuristics are used to guide the search process by providing an estimate of how close a particular state is to the goal state. This estimate helps in determining which states should be explored further and which can be ignored. By using a heuristic function, search algorithms can make informed decisions and efficiently navigate through the search space to find the optimal solution. Therefore, the statement "True" is correct.Rate this question:
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- 31.
What is true for local exhaustive search?
- A.
Is faster by applying different techniques
- B.
Has 65536 cases
- C.
Uses heuristics to select an appropriate search
- D.
Looks up all possible cases
Correct Answer
D. Looks up all possible casesExplanation
Local exhaustive search refers to a search algorithm that examines all possible cases or solutions within a given local region to find the optimal solution. This means that it looks up all possible cases, rather than applying different techniques or using heuristics to select an appropriate search. Therefore, the correct answer is "looks up all possible cases".Rate this question:
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- 32.
Does in search algorithms, a heuristic often denotes a function that estimates the quality of a given state?
- A.
True
- B.
False
Correct Answer
A. TrueExplanation
In search algorithms, a heuristic is commonly used to estimate the quality of a given state. Heuristics provide a way to prioritize different states during the search process by assigning a value that represents how close a state is to the goal. This value helps guide the algorithm in selecting the most promising states to explore next. By using heuristics, search algorithms can efficiently navigate large search spaces and find optimal or near-optimal solutions. Therefore, the statement "True" accurately reflects the role of heuristics in search algorithms.Rate this question:
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- 33.
State representation describes the current state of the solving process
- A.
True
- B.
False
Correct Answer
A. TrueExplanation
State representation is a concept used in problem-solving to describe the current state of the solving process. It refers to the way in which the problem is represented or modeled, including the variables, constraints, and relationships between them. By having a clear and accurate state representation, it becomes easier to analyze and manipulate the problem, leading to more effective problem-solving strategies. Therefore, the statement "State representation describes the current state of the solving process" is true.Rate this question:
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- 34.
What is NOT true about Heuristics?
- A.
Knowledge that is helpful for solving a problem
- B.
Can also go wrong
- C.
Guarantees a result that satisfies our problem
- D.
Mostly a function that estimates the quality of a state
Correct Answer
C. Guarantees a result that satisfies our problemExplanation
Heuristics are a set of rules or strategies that are used to solve problems or make decisions, based on past experiences or common sense. They provide helpful knowledge for problem-solving but do not guarantee a result that satisfies our problem. Heuristics can sometimes go wrong or lead to suboptimal solutions. They are mostly a function that estimates the quality of a state, helping us make decisions based on incomplete information.Rate this question:
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- 35.
What is the main problem with Hill-Climbing search?
- A.
Can lead to a local minima
- B.
Can lead to a global minima
- C.
Reaches maybe a local optima
- D.
Cannot reach the global optima
Correct Answer
C. Reaches maybe a local optimaExplanation
Hill-Climbing search can reach a local optima, meaning it may find a solution that is the best among its neighboring states but not necessarily the globally best solution. This is a problem because it can get stuck in a suboptimal solution and fail to explore other potentially better solutions in the search space.Rate this question:
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- 36.
What is one principle of solving problems like the towers of hanoi?
- A.
Divide-and-Conquer
- B.
Simulated annealing search
- C.
Genetic algorithm
- D.
Local exhaustive search
Correct Answer
A. Divide-and-ConquerExplanation
Divide-and-Conquer is a principle of solving problems like the towers of hanoi. This approach involves breaking down a complex problem into smaller, more manageable subproblems, solving each subproblem independently, and then combining the solutions to solve the original problem. In the case of the towers of hanoi, the problem of moving a stack of disks from one peg to another can be divided into smaller subproblems of moving smaller stacks of disks. By recursively applying this principle, the problem can be solved efficiently.Rate this question:
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- 37.
Simple Exhaustive Search uses either heuristics or constraints.
- A.
True
- B.
False
Correct Answer
B. FalseExplanation
Simple Exhaustive Search does not use heuristics or constraints. It is a basic search algorithm that systematically explores all possible solutions to a problem, without any additional guidance or restrictions. It exhaustively checks every possible combination until it finds a solution or determines that none exist. Therefore, the correct answer is False.Rate this question:
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- 38.
What is one negative characteristic of depth-first search?
- A.
Depth-first search never finds the worst solution
- B.
Depth-first search could be exponentional
- C.
Depth-first search can never finds the perfect solution
- D.
Depth-first search is deprecated
Correct Answer
B. Depth-first search could be exponentionalExplanation
Depth-first search (DFS) can be exponential in certain scenarios, meaning that the time complexity of the algorithm can grow exponentially with the size of the input. This occurs when the search encounters a deep branch or a cycle in the graph being traversed. In such cases, DFS may continue to explore these branches or cycles indefinitely, leading to a significant increase in the time required to find a solution. However, it is important to note that DFS is not always exponential and can be efficient in many cases, especially when applied to graphs with limited depth or when combined with other optimization techniques.Rate this question:
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- 39.
Which three statements about examples for different types of games are true?
- A.
Games of chance with imperfect information include battleship, kriegspiel and matching pennies
- B.
Games of chance with imperfect information include bridge, poker and scrabble
- C.
Deterministic games with perfect information include chess, checkers and Go
- D.
Games of chance with imperfect information include backgammon and monopoly
- E.
Games of chance with perfect information include backgammon and monopoly
- F.
Deterministic games with perfect information include battleship, kriegspiel and matching pennies
Correct Answer(s)
B. Games of chance with imperfect information include bridge, poker and scrabble
C. Deterministic games with perfect information include chess, checkers and Go
E. Games of chance with perfect information include backgammon and monopolyExplanation
The statement "Games of chance with imperfect information include bridge, poker and scrabble" is true because in these games, players do not have complete information about the cards or letters held by their opponents, and luck plays a significant role in determining the outcome.
The statement "Deterministic games with perfect information include chess, checkers and Go" is true because in these games, players have complete information about the state of the game and there is no element of chance involved. The outcome is solely determined by the players' strategies and decisions.
The statement "Games of chance with perfect information include backgammon and monopoly" is true because in these games, players have complete information about the state of the game and luck also plays a significant role in determining the outcome. However, players have full knowledge of the game state and can make strategic decisions based on that information.Rate this question:
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- 40.
Zero-Sum Games are games where one player’s gain is the other player’s gain
- A.
True
- B.
False
Correct Answer
B. FalseExplanation
Zero-sum games are actually games where one player's gain is exactly equal to the other player's loss. In other words, the total payoff in a zero-sum game is always zero. Therefore, it is incorrect to say that one player's gain is the other player's gain in zero-sum games.Rate this question:
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- 41.
What is NOT a real world CSP?
- A.
Time tabling problems
- B.
Assignment problems
- C.
Scheduling problems
- D.
Solving problems in physics
Correct Answer
D. Solving problems in physicsExplanation
The question asks for a real-world CSP (Constraint Satisfaction Problem), and the answer "solving problems in physics" does not fit this category. CSPs involve finding solutions that satisfy a set of constraints, such as scheduling or assignment problems. Solving problems in physics may involve mathematical modeling and problem-solving techniques, but it does not necessarily involve the constraints and variables typically associated with CSPs.Rate this question:
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- 42.
Which one do you like?How many colors are needed to color a map in different colors, such that neighbouring states have not the same color?
- A.
3
- B.
4 for even number of states, 3 for odd number
- C.
4
- D.
N (=number of states)
Correct Answer
B. 4 for even number of states, 3 for odd numberExplanation
The correct answer is that 4 colors are needed for an even number of states, while 3 colors are needed for an odd number of states. This is because in a map with an even number of states, it is always possible to color the map in such a way that no neighboring states have the same color. However, in a map with an odd number of states, it is not always possible to avoid having neighboring states with the same color. Therefore, an extra color is needed to ensure that all states can be colored without any neighboring states having the same color.Rate this question:
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- 43.
A binary constraint concerning graph coloring is for example: ”Upper Austria not equal red”
- A.
True
- B.
False
Correct Answer
B. FalseExplanation
The statement is false because a binary constraint concerning graph coloring would typically involve two variables or nodes in the graph, rather than a region or area like "Upper Austria." Additionally, the constraint would specify a relationship between the colors of the two nodes, rather than stating that a specific color cannot be used for a specific region.Rate this question:
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- 44.
What are preferences concerning CSP’s?
- A.
Can be unary, binary or higher-order constraints
- B.
Should be respected as much as possible
- C.
State how to solve the problem
- D.
Have to be implemented for solving CSP’s
Correct Answer
B. Should be respected as much as possibleExplanation
Preferences concerning CSP's should be respected as much as possible. This means that when solving a constraint satisfaction problem, the preferences or desired outcomes should be given priority and considered to the greatest extent possible. This suggests that even though preferences may not always be fully satisfied due to conflicting constraints, efforts should be made to prioritize and respect them as much as possible in the solution.Rate this question:
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- 45.
How can CSP’s be solved?
- A.
By mutation and cross over
- B.
By divide and conquer combined with heuristics
- C.
By constraint propagation
- D.
Through search and backtracking
- E.
Using search or constraint propagation combined with heuristics
- F.
By genetic algorithms
Correct Answer(s)
C. By constraint propagation
D. Through search and backtracking
E. Using search or constraint propagation combined with heuristicsExplanation
CSPs (Constraint Satisfaction Problems) can be solved through constraint propagation, search and backtracking, or using a combination of search or constraint propagation with heuristics. Constraint propagation involves applying constraints to reduce the search space and eliminate inconsistent values. Search and backtracking involve systematically exploring the search space and backtracking when a dead end is reached. Heuristics can be used to guide the search process and make it more efficient. Genetic algorithms, which involve mutation and crossover, are not specifically mentioned as methods for solving CSPs in the given options.Rate this question:
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- 46.
What is NOT a general-purpose heuristic?
- A.
Minimum remaining value heuristic
- B.
Maximum remaining value heuristic
- C.
Degree heuristic
- D.
Least constraining value heuristic
Correct Answer
B. Maximum remaining value heuristicExplanation
The maximum remaining value heuristic is not a general-purpose heuristic because it does not prioritize the minimum remaining values or degrees of freedom. Instead, it focuses on selecting the variable with the maximum remaining values in its domain. This heuristic is commonly used in constraint satisfaction problems where the goal is to assign values to variables while satisfying a set of constraints. However, it is not applicable in all problem-solving scenarios and does not provide a general approach for solving different types of problems.Rate this question:
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- 47.
Which ratio (R=number of constraints divided by number of variables) can’t be computed that easy concerning performance?
- A.
R ≪ 1
- B.
The ratio does not affect the performance at all
- C.
R ≫ 1
- D.
R ≈ 1
Correct Answer
D. R ≈ 1Explanation
The explanation for the given correct answer, R ≈ 1, is that this ratio is difficult to compute concerning performance because it implies a balanced number of constraints and variables. When the ratio is close to 1, it suggests that there is an equal number of constraints and variables, which can lead to more complex calculations and potentially slower performance.Rate this question:
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- 48.
What is true about Quevdeo’s KRK machine?
- A.
Can automatically play and win the KRK chess endgame
- B.
Was a hoax (a real person was hidden in the machine)
- C.
Is from the 1940’s
- D.
Was able to play against local masters in chess and win against some of them
Correct Answer
A. Can automatically play and win the KRK chess endgameExplanation
The correct answer is that the Quevdeo's KRK machine can automatically play and win the KRK chess endgame. This means that the machine has the capability to play the specific endgame scenario in chess known as KRK and has been programmed to make the best moves to win the game.Rate this question:
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- 49.
Consider solving a game, what does ultra weak solving mean?
- A.
Provide an algorithm which can produce perfect play from any position
- B.
Provide an algorithm which secures a win for one player, or a draw for either, against any possible moves by the opponent from the initial position only
- C.
Provide an algorithm for a guaranteed draw
- D.
Proving whether the first player will win, lose or draw from the initial position, given perfect play on both sides
Correct Answer
D. Proving whether the first player will win, lose or draw from the initial position, given perfect play on both sidesExplanation
Ultra weak solving refers to proving whether the first player will win, lose, or draw from the initial position, assuming perfect play from both sides. It involves analyzing all possible moves and counter-moves to determine the outcome of the game. This algorithm provides a comprehensive evaluation of the game's possibilities, allowing players to make informed decisions based on the predicted result.Rate this question:
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- 50.
Consider solving a game, what does weak solving mean?
- A.
Proving whether the first player will win, lose or draw from the initial position, given perfect play on both sides
- B.
Provide an algorithm for a guaranteed draw
- C.
Provide an algorithm which can produce perfect play from any position
- D.
Provide an algorithm which secures a win for one player, or a draw for either, against any possible moves by the opponent from the initial position only
Correct Answer
D. Provide an algorithm which secures a win for one player, or a draw for either, against any possible moves by the opponent from the initial position onlyExplanation
Weak solving in game theory refers to providing an algorithm that guarantees a win for one player or a draw for either player, regardless of the opponent's moves, but only from the initial position. This means that the algorithm is designed to find a strategy that ensures the best possible outcome for the player, considering all possible moves by the opponent. It does not consider the entire game tree or future positions, but focuses on the initial position only.Rate this question:
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Mar 22, 2023Quiz Edited by
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Jan 27, 2022Quiz Created by
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