Artificial Intelligence Concepts Quiz

Introduction to Artificial Intelligence

• Artificial intelligence is the study of how to make computers do things that humans excel at.
• Intelligence is a natural process, and heredity plays a role. Knowledge is essential for intelligence, though no single human possesses complete expert knowledge. Solutions can often be improved by combining knowledge from different people.
• Artificial intelligence is a programmed process, not hereditary. Knowledge bases (KB) and electricity are crucial to generating outputs. Expert systems aggregate the experience and insights of multiple individuals.

Defining Artificial Intelligence

• Artificial intelligence is the ability of a computer to act like a human.
• This involves systems that think like humans, systems that act like humans, systems that think rationally, and systems that act rationally.
• Artificial intelligence encompasses the effort to create computers with minds, fully capable of thought. It involves the automation of tasks usually associated with human intelligence, including decision-making and problem-solving. The field aims to create machines that perform functions requiring intelligence when done by humans.

Acting Humanly: The Turing Test Approach

• The Turing Test, proposed by Alan Turing, is a method for determining if a machine can exhibit intelligent behavior equivalent to, or indistinguishable from, a human.
• In the test, a human interrogator interacts with both a human and a machine, both unseen, and judges which responses are from which. The machine passes if the interrogator cannot reliably distinguish the responses.
• This approach focuses on creating machines that behave like humans.

Thinking Humanly: The Cognitive Modeling Approach

• This approach involves designing computer models to simulate human cognitive processes, drawing parallels to how humans think.
• Cognitive science is a crucial interdisciplinary field combining AI techniques and psychological experiments to develop accurate and testable theories of the workings of the human mind.
• The goal is to use human cognition to inform AI development.

Thinking Rationally: The "Laws of Thought" Approach

• This approach focuses on using logical reasoning to create AI systems.
• Early attempts to codify "right thinking" include Aristotle's syllogisms.
• Logic is crucial for establishing a framework for AI reasoning.

Acting Rationally: The Rational Agent Approach

• Acting rationally is the ability of an agent to act in a way that best achieves its goals based on its current knowledge and beliefs.
• This approach focuses on agents choosing actions to maximize their expected utility given their beliefs.

Future of Artificial Intelligence

• AI has many potential applications and is evolving rapidly.
• Autonomous cars, manufacturing robots, advanced healthcare systems, and personalized educational tools are all possible outcomes of AI development.
• The use of AI in customer service is another key area of potential advancement.

Characteristics of Intelligent Agents

• Situatedness - An agent interacts with an environment through sensors and actuators, taking actions that modify the environment. Environments can include the physical world or the internet.
• Autonomy - Agents can act without human intervention, controlling their own actions and state.
• Adaptability - Agents should react to changes and learn through experience to excel in dynamic environments.

Agents and their Types

• An agent is a system that perceives its environment through sensors and acts on it through actuators.
• Agents include humans, robots, and software. Agents work in various environments through sensory input, actuation outputs, and percept flows.

Properties of Environment

• Fully observable vs. Partially observable - A fully observable environment allows an agent to immediately know the current state of the environment, whereas a partially observable environment requires the agent to infer the current state.
• Deterministic vs. Stochastic - A deterministic environment allows an agent to precisely predict the future state given the current state and actions. A stochastic environment has inherent uncertainty and randomness in the environment, making predictions challenging.
• Episodic vs. Sequential - In an episodic environment, each task is independent of previous tasks, while in a sequential environment, the current task depends on past tasks.
• Static vs. Dynamic - Environments are static when they do not change during the time an agent operates, and dynamic when they do.
• Discrete vs. Continuous - Discrete environments contain a finite number of states and actions, whereas continuous environments have an infinite number of possible states and actions.
• Knowable vs. Unknowable - whether or not the agents know the environment's states
• Accessible vs. Inaccessible - whether or not the agent can access the total information about the environment.

Problem Solving Approach to Typical AI Problems

• Search techniques are universal problem-solving methods in AI.
• Goal-based agents, using atomic representations, employ these techniques.
• Common problems addressed by these methods include chess, traveling salesperson problems, Hanoi towers, water jug, and N-Queen problems.

The Structure of Intelligent Agents

• Agent = Architecture + Agent Program
• Architecture refers to the hardware/machinery upon which the agent operates.
• The agent program is the implementation of the agent function, using algorithms and software.

Types of Agents

• Simple reflex agents, model-based reflex agents, goal-based agents, utility-based agents, and learning agents
• Simple reflex agents react to the current situation without considering past information.
• Model-based reflex agents use a model of the environment to predict future states.
• Goal-based and utility-based agents are guided by goals and utilities to maximize goal satisfaction.
• Learning agents improve their performance through past experiences.