BPSY361: Introduction to Artificial Intelligence

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Questions and Answers

What is the primary goal of artificial intelligence?

To create machines that can think and act like humans. (correct)

To develop software that can solve complex mathematical problems.

To design robots that can perform physical tasks.

To study the human brain and its cognitive processes.

What is the meaning of the term "artificial" in artificial intelligence?

Synthetic, made from artificial materials.

Complex, requiring sophisticated algorithms.

Non-biological, created by humans. (correct)

Advanced, surpassing human capabilities.

Which of the following is NOT a characteristic of an intelligent agent?

Ability to learn from its experiences.

Ability to perform specific tasks without any input. (correct)

Ability to make decisions based on its knowledge.

Ability to perceive its environment.

What is the connection between Einstein's brain and intelligence?

Einstein's brain had more connections between specific hemispheres. (C) Signup and view all the answers

What does the term "problem-solving agents" refer to?

Agents that can analyze and solve problems based on defined rules and knowledge. (D) Signup and view all the answers

What is the key difference between the "Nature of Environments" and "Structure of Agents" in AI?

The former focuses on the external factors that influence the agent, while the latter focuses on the internal components of the agent. (A) Signup and view all the answers

What is the difference between "intelligence" and "artificial intelligence"?

Intelligence is natural, while artificial intelligence is created by humans. (B) Signup and view all the answers

Why is the term "Good Behavior" relevant to intelligent agents?

Agents should be designed to achieve their goals effectively and efficiently within a given environment. (A) Signup and view all the answers

What is the role of the 'Performance Element' in a learning agent?

To select external actions for the agent to take. (C) Signup and view all the answers

Which of the following is NOT a characteristic of a utility-based agent?

It always chooses the shortest path to the goal state. (A) Signup and view all the answers

What is the purpose of the 'Problem Generator' in a learning agent?

To suggest actions that lead to new and informative experiences. (D) Signup and view all the answers

What is the primary function of a Problem-Solving Agent?

To find a sequence of actions that leads to a desirable state or solution. (B) Signup and view all the answers

What is the difference between a Learning Agent and a Problem-Solving Agent?

A Learning Agent focuses on adapting to new situations while a Problem-Solving Agent focuses on finding a solution to a specific problem. (D) Signup and view all the answers

Which of the following is NOT a step involved in Problem-Solving?

Performance analysis (B) Signup and view all the answers

Which component in a Learning Agent is responsible for evaluating the agent's performance?

Critic (C) Signup and view all the answers

What is the primary objective of 'Search' in Problem-Solving?

To systematically explore alternative actions to find a sequence leading to a desirable state. (D) Signup and view all the answers

What is the primary purpose of a 'Toy Problem' in problem-solving?

To provide a standardized framework for testing different problem-solving techniques. (A) Signup and view all the answers

What is a key distinction between 'Toy Problems' and 'Real-world Problems' in the context of problem-solving?

Toy problems rely on descriptions for their definition, while real-world problems do not. (D) Signup and view all the answers

In the given example, what is the crucial factor in defining the 'state' of the environment for the agent?

A combination of the agent's location and dirt locations. (D) Signup and view all the answers

How many possible states exist in an environment with 'n' locations, as described in the provided example?

n * 2n (B) Signup and view all the answers

What is the cost associated with each action the agent takes in the given example of the environment?

1 (C) Signup and view all the answers

What is the goal test for the agent in this example?

Ensuring all squares in the environment are clean. (D) Signup and view all the answers

Which action(s) in the given example have no effect on the state of the environment?

All of the above.. (D) Signup and view all the answers

How does the concept of 'Search for Solutions' apply to the agent in this example?

The agent explores different paths and actions to find the most efficient way to achieve the goal. (B) Signup and view all the answers

Which field mainly combines computer science and robust datasets for problem-solving?

Artificial Intelligence (C) Signup and view all the answers

What does Carnap's theory primarily analyze?

The acquisition of knowledge from experience (B) Signup and view all the answers

Who first framed the idea of probability and described it in terms of gambling?

Gerolamo Cardano (C) Signup and view all the answers

What major contribution did Blaise Pascal make regarding probability?

He predicted outcomes of gambling events (C) Signup and view all the answers

Which statistical method is central to modern uncertain reasoning in AI systems?

Bayes' rule (B) Signup and view all the answers

Who introduced new statistical methods that advanced the theory of probability after Cardano?

Pierre Laplace (A), James Bernoulli (B), Thomas Bayes (C) Signup and view all the answers

Which of the following is NOT an area explicitly discussed in the foundations of AI?

Biology (A) Signup and view all the answers

What type of problem-solving does artificial intelligence typically focus on?

Experience-based knowledge acquisition (C) Signup and view all the answers

What is the first phase in the problem-solving process?

Goal Formulation (A) Signup and view all the answers

Which component is NOT part of a formal problem definition?

Decision Making (A) Signup and view all the answers

What does the 'Search' phase involve in the problem-solving process?

Simulating possible actions to reach a goal (A) Signup and view all the answers

What is meant by 'Path Cost' in the context of problem-solving agents?

The cumulative cost associated with a sequence of actions (A) Signup and view all the answers

What does the term 'Goal State' represent in problem-solving?

The end result desired by the agent (D) Signup and view all the answers

In the problem-solving agent process, what role does 'Rule/Constraints' play?

Limits the actions available to the agent (A) Signup and view all the answers

Which of the following describes 'Actions' in the context of a problem-solving agent?

The potential steps to reach the Goal State (B) Signup and view all the answers

What is the primary purpose of a problem-solving agent in AI?

To assist in achieving a specific goal (C) Signup and view all the answers

What is the primary criterion for defining a rational agent's success?

The performance measure (D) Signup and view all the answers

Which of the following is NOT a requirement for an AI agent according to the established rules?

The ability to form emotional responses (A) Signup and view all the answers

What does an omniscient agent possess that differentiates it from a rational agent?

Knowledge of the actual outcomes of its actions (A) Signup and view all the answers

What role does prior knowledge play in a rational agent's decision-making process?

It guides the agent’s expectations and expected outcomes (C) Signup and view all the answers

The actions taken by a rational agent should be based on which of the following elements?

The agent's percept sequence and its built-in knowledge (C) Signup and view all the answers

What does autonomy in a rational agent entail?

Ability to learn from its environment to improve actions (C) Signup and view all the answers

Which aspect is essential for an agent's action to be considered rational?

It is expected to maximize its performance measure (C) Signup and view all the answers

How does learning contribute to the functioning of a rational agent?

It allows the agent to adapt based on experiences (B) Signup and view all the answers

Study Notes

Introduction to BPSY361: Artificial Intelligence (AI)

CHRIST (Deemed to be University) offers BPSY361: Artificial Intelligence (AI).
Mission: To nurture holistic development.
Vision: Excellence and Service.
Core Values: Faith in God, Moral Uprightness, Love of Fellow Beings.

Unit 1: Introduction

Introduction to AI: Basic concepts, Intelligent Agents, agents and environments, good behavior, nature of environments, structure of agents, problem solving, and examples of problems.

Artificial Intelligence (AI)

AI is created by humans, especially for imitation of natural things.

What is Artificial in AI?

Artificial intelligence leverages computers and machines to mimic the problem-solving and decision-making capabilities of the human mind.

What is Intelligence?

The ability to acquire and apply knowledge and skills.
Artificial Intelligence (AI) is a method of making a computer, a computer-controlled robot, or software think intelligently like the human mind.

Intelligence: Key Insight

Einstein had more extensive connections between certain parts of his cerebral hemispheres compared to both younger and older control group brains (1905 Miracle Year).

Introduction to AI (Updated info)

AI is a field that combines computer science and robust datasets to facilitate problem-solving.
A reference is provided: https://positivepsychology.com/artificial-intelligence-in-psychology/#apps

Foundations of AI: Philosophy

The confirmation theory of Carnap and Hempel aimed to analyze the acquisition of knowledge from experience.
Carnap's work (1928) outlined a computational procedure for extracting knowledge.
This was potentially the first mind theory as a computational process.

Foundations of AI: Mathematics

A foundational contribution to AI is the theory of probability.
Gerolamo Cardano (1501–1576) initiated the concept of probability.
Pascal's and Fermat's work provided methods for predicting gambling outcomes.
Bernoulli, Laplace, and others further developed probability theory and statistical methods.
Bayes' rule underpins modern approaches to uncertain reasoning in AI systems.

Foundations of AI: Economics

Economics and operations research contributed significantly to the concept of rational agents.
However, AI research initially diverged from economic approaches.
Herbert Simon's (1916-2001) satisficing work, recognized with the Nobel Prize in 1978, proposed "good enough" decisions over optimal ones, providing a better model of human behaviour.
The 1990s witnessed renewed interest in decision-theoretic techniques for agents.

Foundations of AI: Neuroscience

Brain activity measurement started in 1929 with the EEG invention of Hans Berger.
Functional magnetic resonance imaging (fMRI) and single-cell recording advancements offer detailed brain activity observation.
These measurements relate to cognitive processes.

Foundations of AI: Psychology

AI in psychology started at a 1956 MIT workshop.
Contributors like George Miller, Noam Chomsky, and Allen Newell showed how computer models can solve memory, language, and logic problems.
Psychologists increasingly view cognitive theories as computer programs that detail cognitive function.

Foundations of AI: Computer Engineering

The first operational computer was the electromechanical Heath Robinson (1940).
Colossus (1943) advanced to vacuum tube computers.
Konrad Zuse's Z-3 (1941) and Plankalkul were pioneering programmable computers and languages.
Atanasoff-Berry Computer (ABC) (1937-1942) was a precursor to modern computers.
UNIVAC (1940-1942) and other projects built upon this technology.
AI has transformed computer engineering through quantum computing advancements (optimization of algorithms, error mitigation, etc.).

Intelligent Agents

An agent is a computer program or system designed to perceive its environment.
It makes decisions and takes actions to achieve goals.
The agent operates autonomously.

Intelligent Agents: Environments

Systems that can reasonably be deemed intelligent.
An agent is anything that perceives the environment through sensors and acts upon it through actuators.
Agent behavior is described by a function that maps perceived sequences to actions.

Intelligent Agents: Agents and Environments

Agent function is an abstract mathematical description.
Agent program is the concrete implementation running within a physical system, such as a computer.
Example: vacuum cleaner world with actions like "Right", "Suck", "Left" etc

Intelligent Agents: Properties

Agent Terminology:
- Performance Measure: Determines success criteria.
- Behavior: Agent's action after perceptual inputs.
- Percept: Agent's current perceptual input.
- Percept Sequence: History of all perceived inputs till date.
- Agent Function: Map that maps percept sequence to an action.
Good Behavior (Rationality):
- A rational agent does the right thing.
- Rationality relies on performance measure, prior knowledge, actions, and perceived sequence.
Definition of a Rational Agent:
- A rational agent selects actions maximizing its performance based on current experience and knowledge.

Intelligent Agents: Properties (cont)

Omniscience, Learning, and Autonomy:
- Omniscient agents know outcomes and can act accordingly.
- Learning agents gather information and learn from perceived experiences.
- Autonomous agents adapt to compensate for incorrect prior knowledge.

Intelligent Agents: Nature of Environments

PEAS (Performance, Environment, Actuators, Sensors):
- PEAS describes task environments as "problems" with rational agents as "solutions."
Properties of Task Environments (cont):
- Fully Observable vs. Partially Observable Environments:
  - fully observable environments are easy to manage, no history is needed;
  - partially observable environments need to track history.
- Deterministic vs. Stochastic Environments:
  - deterministic environments have outcomes determined by the current state.
- Episodic vs. Sequential Environments:
  - In sequential environments, prior moves affect future actions (like in checkers).
- Dynamic vs. Static Environments:
  - Dynamic environments change continuously.
- Discrete vs. Continuous Environments:
  - discrete environments have finite actions.
- Known vs. Unknown Environments:
  - in known environments the outcome of each action is known;
  - in unknown environments the agent has to gather knowledge to estimate the actions' outcomes..

Intelligent Agents: Structure of Agents

Agent = Architecture + Agent Program
- Architecture: The computing machinery (sensors, actuators) on which the agent operates.
- Agent Program: The implementation of the agent function.
- Agent Function: A map from a percept sequence to an action (it defines what to do in a particular situation).

Intelligent Agents: Types of Agents

Simple Reflex Agents: Agents that respond immediately to percepts using condition-action rules (if-then).
Model-Based Reflex Agents: Maintain an internal model of the environment over time.
Goal-Based Agents: Choose actions to achieve goals based on the current state and possible future states.
Utility-Based Agents: Choose actions based on the expected utility (overall satisfaction).
Learning Agents: Continuous learning through experience, feedback, performance, and suggestions.

Problem-Solving Agents

Problem-Solving Agents use sequential procedures to reach a target/goal.
- Phases of problem-solving:
  - Goal Formulation: Defining goals.
  - Problem Formulation: Refining the goal specification.
  - Search: Finding a solution sequence.
- A formal problem definition requires initial states, actions, transition models, goal tests, and path costs.
Examples: 8-puzzle, 8-queens problem.

Example Problems:

Toy problems as standardized/toy problems, to demonstrate problem-solving techniques - Ex: puzzles.
Real-world problems that need solutions without requiring detailed descriptions.

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Description

Explore the fundamentals of Artificial Intelligence (AI) in this introductory quiz for BPSY361 at CHRIST (Deemed to be University). Understand key concepts such as intelligent agents, environments, and the nature of problem-solving in AI. This quiz will test your grasp of the essential elements of AI and its capabilities.