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Foundations of Artificial Intelligence and Intelligent Agents: Lecture 1 Overview

This lecture introduces the foundations of Artificial Intelligence (AI) and the concept of intelligent agents as part of the CS482/682 course at the University of Nevada, Reno, taught by Kostas Bekris. Topics covered include the distinction between human-like and rational thinking, decision-making in deterministic and stochastic environments, and learning in unknown environments. The lecture further explores single-agent and multi-agent decision strategies, including dynamic programming, game theory, and machine learning techniques such as decision trees and neural networks.

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Foundations of Artificial Intelligence and Intelligent Agents: Lecture 1 Overview

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  1. Computer Science & Engineering, University of Nevada, Reno CS482/682Artificial Intelligence • Lecture 1: • The Foundations of AI • and Intelligent Agents 25 August 2009 Instructor: Kostas Bekris

  2. What is AI? Humanly vs. Rationally Thinking vs. Acting

  3. What is AI? Humanly vs. Rationally Thinking vs. Acting

  4. Acting Humanly

  5. What is AI? Humanly vs. Rationally Thinking vs. Acting

  6. Thinking Humanly

  7. What is AI? Humanly vs. Rationally Thinking vs. Acting

  8. Thinking Rationally

  9. What is AI? Humanly vs. Rationally Thinking vs. Acting

  10. Acting Rationally

  11. Intelligent Agents

  12. Environments and their properties

  13. Environments and their properties

  14. Structure of the Course • Part 1. • Decision-Making in Deterministic Environments • Single-agent: Dynamic programming and search, informed search and heuristics, randomized search, genetic algorithms, constraint satisfaction and path planning • Multi-agent: Adversarial search (mini-max and expecti-mini-max) • Part 2. • Decision-Making in Stochastic Environments • Single-agent: Bayesian networks, Hidden Markov Models, Kalman and Particle filters, Decision and Utility theory, Markov Decision Processes • Multi-agent: Introduction to Game Theory • Part 3. • Learning in Unknown Environments • Supervised learning: Decision trees, Support Vector Machines, Neural Networks • Unsupervised learning: Introduction to Reinforcement Learning

  15. Where are we now?

  16. What are my personal interests? • Agents that must and do appropriately model and reason about the physical properties of their environment: • algorithmic generation of motion (motion planning) • state estimation problems given noisy sensors • and distributed message-passing coordination Robotics Physically-Grounded Agents Computer Games Human Assistants

  17. How do agents work?

  18. Reflex Agents

  19. Model-based Reflex Agents

  20. Goal-based Agents

  21. Utility-based Agents

  22. Learning Agents

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