Nothing Special   »   [go: up one dir, main page]
Scribd Logo
Upload

Welcome to Scribd!

  • 24 views

    CSC462-AI Lec02 Slides

    Uploaded by

    Tariq Mehmood
    This 3-credit lecture discusses agents and environments in artificial intelligence. It introduces key concepts such as rational agents that behave optimally given their environment, performance measures to evaluate agents, and properties of task environments including observability, determinism, and more. The lecture also covers specifying the PEAS (Performance, Environment, Actuators, Sensors) properties of an environment and different types of agent programs from simple reflex to learning agents.

    Copyright:

    © All Rights Reserved
    Download as PDF, TXT or read online from Scribd

    CSC462-AI Lec02 Slides

    Uploaded by

    Tariq Mehmood
    24 views27 pages
    This 3-credit lecture discusses agents and environments in artificial intelligence. It introduces key concepts such as rational agents that behave optimally given their environment, performance measures to evaluate agents, and properties of task environments including observability, determinism, and more. The lecture also covers specifying the PEAS (Performance, Environment, Actuators, Sensors) properties of an environment and different types of agent programs from simple reflex to learning agents.

    Original Description:

    AI

    Original Title

    CSC462-AI Lec02 slides

    Copyright

    © © All Rights Reserved

    Available Formats

    PDF, TXT or read online from Scribd

    Did you find this document useful?

    Is this content inappropriate?

    This 3-credit lecture discusses agents and environments in artificial intelligence. It introduces key concepts such as rational agents that behave optimally given their environment, performance measures to evaluate agents, and properties of task environments including observability, determinism, and more. The lecture also covers specifying the PEAS (Performance, Environment, Actuators, Sensors) properties of an environment and different types of agent programs from simple reflex to learning agents.

    Copyright:

    © All Rights Reserved
    Download as PDF, TXT or read online from Scribd
    Download as pdf or txt
    24 views27 pages

    CSC462-AI Lec02 Slides

    Uploaded by

    Tariq Mehmood
    This 3-credit lecture discusses agents and environments in artificial intelligence. It introduces key concepts such as rational agents that behave optimally given their environment, performance measures to evaluate agents, and properties of task environments including observability, determinism, and more. The lecture also covers specifying the PEAS (Performance, Environment, Actuators, Sensors) properties of an environment and different types of agent programs from simple reflex to learning agents.

    Copyright:

    © All Rights Reserved
    Download as PDF, TXT or read online from Scribd
    Download as pdf or txt
    of 27

    Lecture # 2

    CSC462 ARTIFICIAL INTELLIGENCE


    CREDIT HOURS: 3(2, 1)

    Course Instructor: SAIF ULLAH IJAZ


    Lecturer CS Dept, CUI Vehari
    MSc University of Leicester, UK
    BSc COMSATS University Islamabad
    AGENTS AND ENVIRONMENTS

    2
    AGENTS
    systems that can reasonably be called intelligent. e.g. Human
    Agent, Robotic Agent, Software Agent (software robot or softbot)

    ENVIRONMENT
    The environment could be everything—the entire
    universe! or a subset of universe e.g. Road, classroom,
    virtual environments

    3
    AGENTS AND ENVIRONMENTS

    • The observation that some agents behave better than


    others leads naturally to the idea of a rational agent—
    one that behaves as well as possible.
    • How well an agent can behave depends on the nature of
    the environment; some environments are more difficult
    than others.
    • An agent perceive environment through sensors, and then
    act upon that environment through actuators.

    4
    5
    PERCEPT SEQUENCE
    percept sequence is the complete history of everything
    the agent has ever perceived.

    In general, an agent’s choice of action at any given


    instant can depend on its built-in knowledge and on the
    entire percept sequence observed to date, but not on
    anything it hasn’t perceived.

    6
    AGENT FUNCTION
    an agent’s behavior is described by the agent function
    that maps any given percept sequence to an action.

    Internally, the agent function for an artificial agent will


    be implemented by an agent program

    7
    8
    RATIONAL AGENT
    A rational agent is one that does the right thing.

    PERFORMANCE MEASURES
    • We evaluate an agent’s behavior by its consequences.
    • When an agent is plunked down in an environment, it generates a sequence of
    actions according to the percepts it receives.
    • This sequence of actions causes the environment to go through a sequence of states.
    • If the sequence is desirable, then the agent has performed well.
    • This notion of desirability is captured by a performance measure that evaluates
    any given sequence of environment states.
    10
    RATIONALITY
    What is rational at any given time depends on four things:

    • The performance measure that defines the criterion of success.


    • The agent’s prior knowledge of the environment.
    • The actions that the agent can perform.
    • The agent’s percept sequence to date.

    11
    SPECIFYING THE TASK ENVIRONMENT
    In designing an agent, the first step must always be to specify the task
    environment as fully as possible including
    • the performance measure
    • the environment
    • the agent’s actuators
    • the agent’s sensors

    PEAS (Performance, Environment, Actuators, Sensors)

    12
    PROPERTIES OF TASK ENVIRONMENTS
    • FULLY OBSERVABLE VS. PARTIALLY OBSERVABLE
    • SINGLE-AGENT VS. MULTI-AGENT
    • DETERMINISTIC VS. NONDETERMINISTIC
    • EPISODIC VS. SEQUENTIAL
    • STATIC VS. DYNAMIC
    • DISCRETE VS. CONTINUOUS
    • KNOWN VS. UNKNOWN
    AGENT PROGRAM & AGENT ARCHITECTURE
    • The job of AI is to design an agent program that implements the
    agent function—the mapping from percepts to actions.
    • this program will run on some sort of computing device with physical
    sensors and actuators—we call this the agent architecture:

    agent = architecture + program

    17
    AGENT PROGRAMS
    • SIMPLE REFLEX AGENTS
    • MODEL-BASED REFLEX AGENTS
    • GOAL-BASED AGENTS
    • UTILITY-BASED AGENTS
    • LEARNING AGENTS
    SIMPLE REFLEX AGENT
    SIMPLE REFLEX AGENTS
    AN AGENT PROGRAM FOR VACUUM AGENT
    MODEL-BASED REFLEX AGENT
    GOAL-BASED AGENTS
    UTILITY-BASED AGENTS
    LEARNING AGENT

    You might also like