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Perspectives on Agents and Artificial Intelligence

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Explore key concepts in modern AI, including rational agents, state-of-the-art technologies, and popular tasks. Dive into the principles of determining rationality and issues in AI development. Learn about different types of agents and their roles in intelligent systems.

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Perspectives on Agents and Artificial Intelligence

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Presentation Transcript

  1. 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.)

  2. Popular Tasks of Today • Data mining • Intelligent agents and internet applications • softbots • believable agents • intelligent information access • Scheduling applications • Configuration applications

  3. 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.

  4. 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.

  5. Views of AI • Weak vs. strong • Scruffy vs. neat • Engineering vs. cognitive

  6. 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

  7. 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

  8. 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.

  9. Issues in Determining Rationality • Omniscience • Autonomy

  10. Task Environment Specification • Performance measure • Environment • Actuators • Sensors

  11. 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

  12. Types of Agents • Simple Reflex • Model-based Reflex • Goal-based • Utility-based • Learning

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