Artificial Intelligence · Unit 2 · 4 hrs
Intelligent Agents
Exam-focused notes for Intelligent Agents (Artificial Intelligence, CSC266): what the TU syllabus asks and how it has actually been tested, with 8 solved past questions from this unit.
What this unit covers
- Introduction of agents
- Structure of Intelligent agent
- Properties of Intelligent Agents
- Configuration of Agents
- PEAS description of Agents
- Types of Agents: Simple Reflexive, Model Based, Goal Based, Utility Based
- Environment Types: Deterministic, Stochastic, Static, Dynamic, Observable, Semi-observable, Single Agent, Multi Agent
Environment Types
Justify which type of environments resembles following agents. a. Mission Game with fixed 6 states having two players. b. Tesla Driverless Robovan where road conditions are changing. c. Game Result Predicting Agent where current prediction state is independent of previous state. [5]
Environments are classified along several dimensions: - Deterministic vs Stochastic - Dynamic vs Static - Observable vs Semi-Observable --- This is a game-playing scenario with two players competing against each other. Property Justification ------ Fully Ob...
Full solved answer →Discuss the types of environment where an agent can work on. [5]
In Artificial Intelligence, the environment is the external context in which an agent operates. Different agents work in different types of environments. The properties of the environment directly affect the design of the agent. The main types of environmen...
Full solved answer →Properties of Intelligent Agents
What are the properties of intelligent agent? How simple reflex agents work? Give an example of simple reflex agent. [5]
Intelligent agents possess the following properties, classified into internal and external characteristics: Property Description ------ Learning An agent has the ability to learn from previous experience and successively adapt its own behaviour to the envir...
Full solved answer →PEAS description of Agents
Using your own assumptions, design PEAS framework for following intelligent agents. a. Covid-19 prediction system b. Vaccine recommender system. [5]
PEAS stands for Performance Measure, Environment, Actuators, and Sensors. To design a rational agent, we must specify its task environment using the PEAS description. (Reference: "To design a rational agent we must specify its task environment. Task environ...
Full solved answer →How agent can be configured used PEAS framework? Illustrate with example. [5]
PEAS is a framework used to describe and configure an intelligent agent by specifying four key components: Letter Stands For Description -------------------------------- P Performance Measure Criteria to evaluate agent's success E Environment The surroundin...
Full solved answer →Using your own assumptions, design PEAS framework for following intelligent agents. a. Medicine delivery drone b. Covid medicine prescriber. [5]
To design a rational agent, we must specify its task environment using the PEAS framework: - P - Performance Measure - E - Environment - A - Actuators - S - Sensors --- Assumptions: The drone operates in an urban area, picks up medicines from a pharmacy/hos...
Full solved answer →Types of Agents
What do you mean by Rational Agent? What are differences between Utility based agent and model based agent? [5]
--- A rational agent is an agent that acts so as to achieve the best expected outcome given its knowledge, percepts, and available actions. In other words, a rational agent selects an action that maximizes its performance measure based on: - The evidence pr...
Full solved answer →What is an agent? How utility agent works? Give an example of utility agent. [5]
--- An agent is anything that can perceive its environment through sensors and act upon that environment through actuators. An agent operates in a continuous cycle: An agent takes a percept sequence as input and produces an action as output. The goal of an ...
Full solved answer →Make Unit 2 stick
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