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For Wednesday. Read chapter 3, sections 1-4 Homework: Chapter 2, exercise 4 Explain your answers (Identify any assumptions you make. Where you think there’s a question, explain your thinking.). Popular Tasks of Today. Data mining Intelligent agents and internet applications softbots

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For wednesday

For Wednesday

  • Read chapter 3, sections 1-4

  • Homework:

    • Chapter 2, exercise 4

    • Explain your answers (Identify any assumptions you make. Where you think there’s a question, explain your thinking.)


Popular tasks of today

Popular Tasks of Today

  • Data mining

  • Intelligent agents and internet applications

    • softbots

    • believable agents

    • intelligent information access

  • Scheduling applications

  • Configuration applications


State of the art

State of the Art

  • Deep Blue beats Kasparov

  • Sojourner, Spirit and Opportunity explore Mars

  • NASA Remote Agent in Deep Space I explores solar system

  • DARPA grand challenge: Autonomous vehicle navigates across desert and then urban environment.

  • Usable machine translation thru Google.


State of the art1

State of the Art

  • iRobotRoomba automated vacuum cleaner, and PackBot used in Afghanistan and Iraq wars

  • Automated speech/language systems on telephone.

  • Fairly accurate speech recognition

  • Spam filters using machine learning.

  • Question answering systems automatically answer factoid questions.


Views of ai

Views of AI

  • Weak vs. strong

  • Scruffy vs. neat

  • Engineering vs. cognitive


What is an agent

What Is an Agent?

  • In this course (and your textbook):

    • An agent can be viewed as perceiving its environment

      • Note that perception and environment may be very limited

    • An agent can be viewed as acting upon it environment (presumably in response to its perceptions)

  • Agent is a popular term with nebulous meaning--so don’t expect it to mean the same thing all of the time in the literature


Rational agents

Rational Agents

  • Organizing principle of textbook

  • A rational agent is one that chooses the best action based on its perceptions

  • This does not have to be the best action that could have been taken--perception may be limited


Determining rationality

Determining Rationality

  • Must have a performance measure.

  • Rationality depends on

    • The performance measure.

    • Agent’s prior knowledge.

    • Agent’s possible actions.

    • Agent’s percept sequence to date.


Issues in determining rationality

Issues in Determining Rationality

  • Omniscience

  • Autonomy


Task environment specification

Task Environment Specification

  • Performance measure

  • Environment

  • Actuators

  • Sensors


Environment issues

Environment Issues

  • Observability

  • Single or multi-agent

    • Cooperative or competitive

  • Deterministic or stochastic

  • Episodic or sequential

  • Static or dynamic

  • Discrete or continuous

  • Known or unknown


Types of agents

Types of Agents

  • Simple Reflex

  • Model-based Reflex

  • Goal-based

  • Utility-based

  • Learning


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