CSC583 Artificial Intelligence Algorithms Topic 1 Quiz

The ability of machines to solve problems and make decisions, imitating human intelligence.

A thought experiment that assesses a machine's ability to exhibit intelligent behavior indistinguishable from a human.

The study of how collective behavior emerges from decentralized systems of simple agents.

A technique that mimics the social behavior of bird flocks and fish schools to solve optimization problems.

The capacity to solve problems and make decisions.

A program that inherits properties and behaviors from another program, allowing for code reuse and specialization.

A program that provides a general set of properties and behaviors, forming the foundation for other programs.

The relationship between a subclass and its superclass, representing the principle that a subclass 'is a' type of its superclass.

The ability of a subclass to override a method inherited from its superclass, providing specialized implementation.

To create a new class that inherits properties and behaviors from an existing class.

A keyword in programming languages used to indicate inheritance, signifying that a subclass inherits from a superclass.

The principle of reusing code by inheriting properties and behaviors from existing classes, reducing code redundancy.

The level of access control that allows a class's members to be accessed by its subclasses and its own methods.

A diagram used to visually represent the relationships between classes, especially inheritance relationships through the 'IS-A' principle.

A process of using randomly selected individuals (particles) to search for the best solution in a problem space.

The capacity of machines to think like humans, focusing on replicating intelligent behavior through computational processes.

A question addressing whether machines can experience thought without life, exploring the nature of consciousness.

A question exploring the nature of human minds and whether non-humans can possess minds, investigating the essence of consciousness.

The creation of intelligent machines that perform tasks requiring intelligence as humans do, emphasizing the imitation of human capabilities.

The main goal of artificial intelligence is to develop machines capable of performing tasks that typically require human intelligence, such as problem-solving and decision-making.

The key principle of Swarm Intelligence is decentralized collaboration, where individual agents share information and seamlessly coordinate with each other without a central leader.

The basis of increased intelligence in Swarm Intelligence systems is the sharing of information between decentralized agents, leading to collective decision-making and problem-solving.

Swarm Intelligence relies on local communication among simple agents, where each agent interacts with its neighbors without global knowledge of the entire system.

Particle Swarm Optimization (PSO) is inspired by the social behavior of bird flocks and fish schools, where individual agents (particles) cooperate to find the best solution to a problem.

The three simple behaviors of separation, alignment, and cohesion that contribute to the coordination of bird flocks and fish schools are the inspiration for the Particle Swarm Optimization (PSO) technique.

Each particle in a swarm remembers its personal best position (best solution it has found) and the global best value (best solution found by the entire swarm) during the search for the optimum solution.

In Particle Swarm Optimization (PSO), particles move around the search space, adjusting their positions based on their personal best, global best, and random velocity, collaborating to find the optimum solution.

Swarm Intelligence can be modeled to understand transformations from the microscopic (local) level to the macroscopic (global) level, demonstrating how collective behavior emerges from individual interactions.