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Questions and Answers
A robot in the Wumpus World wants to locate the gold. The robot's sensors can detect the presence of a pit and the presence of a breeze, but not the presence of the gold. Which type of environment does this represent?
A robot in the Wumpus World wants to locate the gold. The robot's sensors can detect the presence of a pit and the presence of a breeze, but not the presence of the gold. Which type of environment does this represent?
A robotic agent in the Wumpus World is exploring a maze. It takes an action, and the outcome is never the same, even when the agent takes the same action again from the same position. Which type of environment is this?
A robotic agent in the Wumpus World is exploring a maze. It takes an action, and the outcome is never the same, even when the agent takes the same action again from the same position. Which type of environment is this?
In the Wumpus World, a robotic agent is trying to reach the gold. If the agent moves into a square that has a pit, it dies instantly. The agent's decision-making process is based on a set of predefined, strict rules. Which type of environment is most suitable for this scenario?
In the Wumpus World, a robotic agent is trying to reach the gold. If the agent moves into a square that has a pit, it dies instantly. The agent's decision-making process is based on a set of predefined, strict rules. Which type of environment is most suitable for this scenario?
A robotic agent in the Wumpus World aims to collect gold while avoiding pits and the Wumpus. The agent has a map, but it does not know the exact location of the Wumpus or pits. The agent relies on its sensors to detect the stench of the Wumpus or the breeze from a pit. Which type of environment does this describe?
A robotic agent in the Wumpus World aims to collect gold while avoiding pits and the Wumpus. The agent has a map, but it does not know the exact location of the Wumpus or pits. The agent relies on its sensors to detect the stench of the Wumpus or the breeze from a pit. Which type of environment does this describe?
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Imagine a Wumpus World game where the Wumpus's movement is unpredictable. The agent cannot determine where the Wumpus will be next, even if it knows the Wumpus's current location. Would this scenario be best described by a deterministic or a stochastic environment?
Imagine a Wumpus World game where the Wumpus's movement is unpredictable. The agent cannot determine where the Wumpus will be next, even if it knows the Wumpus's current location. Would this scenario be best described by a deterministic or a stochastic environment?
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In the context of the Wumpus world, what does the 'path cost' represent?
In the context of the Wumpus world, what does the 'path cost' represent?
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What is a key difference between a 'toy problem' and a 'real-world problem' in the context of problem-solving agents?
What is a key difference between a 'toy problem' and a 'real-world problem' in the context of problem-solving agents?
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In the context of the Wumpus world, what would be considered a state?
In the context of the Wumpus world, what would be considered a state?
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Why is the Wumpus world considered a 'toy problem'?
Why is the Wumpus world considered a 'toy problem'?
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Imagine you are designing a problem-solving agent for the Wumpus world. Which of these factors would be considered a part of your agent's 'performance measure'?
Imagine you are designing a problem-solving agent for the Wumpus world. Which of these factors would be considered a part of your agent's 'performance measure'?
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What is the importance of the 'goal test' in the context of the Wumpus world?
What is the importance of the 'goal test' in the context of the Wumpus world?
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What is the role of the 'transition model' in the Wumpus world?
What is the role of the 'transition model' in the Wumpus world?
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Which of these options are correct regarding the 8-puzzle problem? (Select all that apply)
Which of these options are correct regarding the 8-puzzle problem? (Select all that apply)
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What is the primary difference between the route-finding problem and the touring problem?
What is the primary difference between the route-finding problem and the touring problem?
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What is the primary goal of the 8-Queens problem?
What is the primary goal of the 8-Queens problem?
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Which of the following options describes the actions available in the 8-puzzle problem?
Which of the following options describes the actions available in the 8-puzzle problem?
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In the context of the 8-puzzle problem, what does the term 'path cost' represent?
In the context of the 8-puzzle problem, what does the term 'path cost' represent?
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Which of these accurately describes the 'transition model' in the context of the 8-puzzle problem?
Which of these accurately describes the 'transition model' in the context of the 8-puzzle problem?
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How does the transition model function in the 8-Queens problem?
How does the transition model function in the 8-Queens problem?
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Regarding the 8-Queens problem, what does the 'goal test' check for?
Regarding the 8-Queens problem, what does the 'goal test' check for?
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Which of the following statements accurately describes the states in the 8-Queens problem?
Which of the following statements accurately describes the states in the 8-Queens problem?
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What is the primary distinction between the initial state and the goal state in the 8-Queens problem?
What is the primary distinction between the initial state and the goal state in the 8-Queens problem?
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Flashcards
Task Environments
Task Environments
Contexts in which rational agents operate to solve problems.
PEAS
PEAS
An acronym for Performance, Environment, Actuators, Sensors related to task environments.
Fully Observable Environment
Fully Observable Environment
An environment where an agent can sense the complete state at all times.
Partially Observable Environment
Partially Observable Environment
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Deterministic Environment
Deterministic Environment
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Path Cost
Path Cost
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Optimal Solution
Optimal Solution
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Standardized/Toy Problem
Standardized/Toy Problem
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Real-world Problems
Real-world Problems
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State
State
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Actions
Actions
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Goal Test
Goal Test
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8-puzzle
8-puzzle
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State in 8-puzzle
State in 8-puzzle
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Initial state in 8-puzzle
Initial state in 8-puzzle
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Actions in 8-puzzle
Actions in 8-puzzle
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Transition model in 8-puzzle
Transition model in 8-puzzle
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Goal test in 8-puzzle
Goal test in 8-puzzle
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Path cost in 8-puzzle
Path cost in 8-puzzle
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8-queens problem
8-queens problem
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State in 8-queens problem
State in 8-queens problem
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Goal test in 8-queens problem
Goal test in 8-queens problem
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Study Notes
Course Information
- Course title: BPSY361: Artificial Intelligence (AI)
- Institution: CHRIST (Deemed to be University)
- Location: Bangalore, India
Mission and Vision
- Mission: CHRIST is a nurturing ground for individual holistic development, enabling effective contributions.
- 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: problem solving agents, example of problems.
- Artificial: Made by humans, especially in imitation of something natural.
- Artificial Intelligence: Leveraging computers and machines to mimic the problem-solving and decision-making capabilities of the human mind.
What is Intelligence?
- Ability to acquire and apply knowledge and skills.
- Artificial Intelligence is a method of making a computer, computer-controlled robot, or a software think intelligently like the human mind.
- Psychologists define intelligence as the ability to learn, recognize problems, and solve problems.
Intelligence (Specific to Einstein)
- Stronger connection between brain hemispheres.
- Findings show Einstein had more extensive connections between certain parts of his brain compared to younger and older controls.
- Einstein was 26 in 1905, his Annus Mirabilis (Miracle Year).
Introduction to AI (Further Points)
- Artificial intelligence is a field which combines computer science and robust datasets, to enable problem-solving.
- References: https://positivepsychology.com/artificial-intelligence-in-psychology/#apps
Brief History of Artificial Intelligence
- 1956: John McCarthy coined the term "artificial intelligence" and held the first AI conference.
- 1969: Shakey, the first general-purpose mobile robot was built.
- 1997: Deep Blue, a supercomputer designed by IBM, defeated the world champion chess player.
- 2002: The first commercially successful robotic vacuum cleaner was created.
- 2005-2019: Speech recognition, robotic process automation (RPA), dancing robots, smart homes, and other innovations emerged.
- 2020: Baidu LinearFold AI algorithm was released, helping medical and scientific teams develop a COVID-19 vaccine (RNA virus prediction in 27 seconds, 120 times faster than other methods).
Intelligent Agents
- Definition: A computer program or system designed to perceive its environment, make decisions, take actions to achieve a specific goal, operate autonomously.
- Sensors: Tools for perceiving the environment.
- Percepts: Data collected from sensors.
- Actions: The agent's responses.
- Effectors: Tools for performing actions in the environment.
- Rational agents: Systems that reasonably can be called intelligent.
- Agent function: Maps any given percept sequence to an action.
- Agent = Architecture + Agent Program
- Architecture: The machinery on which the agent runs, including sensors and actuators
- Agent program: the implementation of an agent function; a concrete implementation running within a physical system
Example: Vacuum-Cleaner World
- Percept sequence - a list of observations an agent makes about its environment.
- Example: [A, Clean], [A, Dirty], [B, Clean].
- Action - an action the agent performs.
- Example: Right, Suck, Left, Suck
- Tabular examples of how an agent in a two location world reacts to perceived states and performs actions.
Agent and Environment
- Agent: Anything capable of perceiving its environment through sensors and acting upon it through effectors. Human agents have sensory organs (eyes, ears) and effectors (hands). Robotic agents use cameras and motors. Software agents use data and algorithms.
- Environment: Contains the surroundings, in which the agent exists
Agent Terminology
- Performance Measure of Agent: Criteria for determining how successful an agent is.
- Behavior of Agent: The action an agent performs in response to a sequence of percepts.
- Percept: The agent's perceptual input at a particular instance.
- Percept Sequence: The history of all perceptions the agent has received.
- Agent Function: Maps a percept sequence to an action.
Good Behavior: The Concept of Rationality
- Rational agent: An agent that conceptually does the right thing, meaning every entry of the agent function (action maps) is correctly filled out, considering all the agent's behavior consequences.
- Rationality at any given time depends on: Performance measure (criterion of success), Agent's prior knowledge of the environment, Actions that the agent can perform, Agent's percept sequence.
Definition of a Rational Agent
- Selects an action maximizing expected performance measure given the evidence in percept sequence, plus other available knowledge
Properties of Task Environments
- Fully Observable vs Partially Observable: Fully observable environments allow the agent to perceive the complete state at each time step. Partially observable environments do not offer a complete state view.
- Deterministic vs Stochastic: Deterministic environments have predictable next states; stochastic environments have unpredictable next states.
- Episodic vs Sequential: In episodic environments, each interaction is independent from previous ones. In sequential environments, each interaction depends on previous ones.
- Dynamic vs Static: Dynamic environments change while the agent interacts; static environments remain constant.
- Discrete vs Continuous: Discrete environments have a finite number of possible actions. Continuous environments have a continuous set of actions.
- Known vs Unknown: Known environments have completely understood dynamics; unknown environments require the agent to learn how the environment functions.
Structure of Agents
- Agent = Architecture + Agent Program
- Architecture: The physical machinery (e.g., robotic car, computer).
- Agent program: The implementation of the agent function.
- Agent function: Maps a percept sequence to an action.
Structure of Agents (Types)
- Simple reflex agents
- Model-based reflex agents
- Goal-based agents
- Utility-based agents
- Learning Agents
Simple Reflex Agents
- Respond directly to percepts (current percept).
- Ignored percept history.
- Condition-action rules: if condition is met, perform the action.
- Example: "If car in front is braking, then initiate braking".
Model-Based Reflex Agents
- Keep track of parts of the world.
- Combine current percept with old internal state to generate an updated internal description of the current state of the world
- Example chart with components interacting.
Goal-Based Agents
- Description of current state.
- Goal information (describing desirable situations).
- Action selection based on goal.
Utility-Based Agents
- Goal state description
- Behavior with high quality (utility).
- Multiple sequences: choose the one that is more reliable, safer, quicker, and cheaper
Learning Agent
- Learns from past experiences.
- Starts with basic knowledge, adapts over time.
- Four key components: Learning Element, Critic, Performance Element, Problem Generator
Problem-Solving Agents
- Finding actions sequences to reach desirable states.
- Search: Investigating alternative actions.
- Problem Definition: Details of desired inputs & acceptable solutions.
- Problem Analysis: Thoroughly analyzing the problem.
- Knowledge Representation: Collecting detailed info & defining methods.
- Problem Solving: Selecting the best technique.
Problem-Solving Agent Process
- Goal Formulation: Defining a target/goal to be achieved.
- Problem Formulation: Identifying the steps towards reaching a goal
- Search: Locating actions achieving the target/goal
- Execution: Carrying out determined actions
Formal Definition of Problem
- Initial State
- Actions
- Transition Model
- Goal Test
- Path Cost
Example Problems (Categorized)
- 8-Puzzle: Moving numbered tiles to a goal state.
- 8-Queens Problem: Arranging eight chess queens on a chessboard without attacking each other.
- Taxi Agent Route Finding: Traveling from one location to another on roads.
- Touring Problems: Visiting various locations (e.g., cities)
- Traveling Salesman Problem: Minimizing the total distance of traveling to multiple cities (locations).
Real World Problems
- Examples of real world problems.
Example Diagrams and Charts
- Diagrams for describing how different agent components and relationships interact. (Including diagrams and charts related to various types of agents, problem solving components, and different types of environments)
- Diagram of state space for a vacuum world to illustrate actions.
- Examples of a 8-Queen problem and how its state and actions are determined
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Description
Test your understanding of the core concepts of Artificial Intelligence as introduced in BPSY361. This quiz covers topics such as intelligent agents, nature of environments, and the problem-solving capabilities of AI. Challenge yourself and see how well you grasp the fundamentals of this fascinating field!
