Untitled Quiz

Early AI programs, like the General Problem Solver, aimed to mimic human problem-solving processes.

An early neural network model developed by Frank Rosenblatt.

A period of reduced funding and interest in AI due to unmet expectations and technological limitations.

AI systems that used knowledge-based approaches to solve specific problems, like medical diagnosis.

A pioneering AI program developed by Newell and Simon that tried to simulate human problem-solving processes.

Models inspired by the human brain aiming to reproduce human learning abilities.

AI's transition from a research field to a commercial and widely applied industry.

New technologies that showed promise in AI development, helping AI overcome limitations.

Software programs that perform tasks by understanding and responding to human speech.

AI model that creates human-like text and engages in conversations.

Quadruped robot for inspection and data collection.

AI that plays games by learning through games against itself.

AI system that plays the game Dota 2.

AI that plays StarCraft II at a high level.

AI virtual assistant.

Robotics company known for advanced, and uniquely capable robots.

Devices gathering environmental info, converted to agent-processable data.

Turning raw sensor data into meaningful information.

Structured method of storing world info, facts, and rules.

Agent's dynamic understanding of the environment, updated with data

System applying rules to the knowledge base to reach conclusions.

Creating action sequences to reach goals, managing multiple objectives.

Methods for agents to learn from data/experiences (supervised, unsupervised, reinforcement).

Devices allowing agents to interact with the environment (e.g., robotic arms).

Environments where agents make decisions based on market conditions.

Environments focused on allocating and optimizing resources, like in logistics and supply chains.

How agents gather information about their surroundings and internal state using sensors.

How agents change the environment through actuators or other means.

Information agents receive from the environment in response to actions.

How agents adjust behavior based on environmental changes, learning experiences.

Programs that model virtual environments to mimic real-world conditions.

Software that simulates environmental processes and impacts.

A method of finding a solution in a problem space.

Creating a structured problem representation for AI.

The starting point of a problem.

The desired outcome of a problem.

All states reachable from the initial one.

Describing how states change with actions.

Assigning a cost to actions/transitions.

A way to represent a problem using initial, goal, actions, transitions, and costs to be usable by search algorithms.

A search algorithm that expands the most promising node based on a heuristic estimate, but may not always find the optimal solution.

Optimization techniques that mimic natural selection by creating, combining, and mutating populations of solutions, often effective for large, complex search spaces.

An optimization technique that introduces randomness to escape local maxima, gradually reducing randomness, often useful for optimization problems with large search spaces.

An optimization technique that iteratively moves to neighboring states with a higher value, but can get stuck in local maxima.

A technique for solving Constraint Satisfaction Problems (CSPs) by systematically exploring possible solutions, backtracking when a constraint violation is found.

Technique that reduces the search space in Constraint Satisfaction problems(CSP) by enforcing constraints locally.

Used for pathfinding, combining cost and heuristic to efficiently find the shortest path.

Algorithm for solving game-tree problems by evaluating possible moves recursively, focusing on winning while minimizing opponent's chances.