cs344m autonomous multiagent systems
play

CS344M Autonomous Multiagent Systems Patrick MacAlpine Department - PowerPoint PPT Presentation

CS344M Autonomous Multiagent Systems Patrick MacAlpine Department or Computer Science The University of Texas at Austin Good Afternoon, Colleagues Patrick MacAlpine Good Afternoon, Colleagues Are there any questions? Patrick MacAlpine


  1. CS344M Autonomous Multiagent Systems Patrick MacAlpine Department or Computer Science The University of Texas at Austin

  2. Good Afternoon, Colleagues Patrick MacAlpine

  3. Good Afternoon, Colleagues Are there any questions? Patrick MacAlpine

  4. Logistics • Questions about the syllabus? Patrick MacAlpine

  5. Logistics • Questions about the syllabus? • Class registration Patrick MacAlpine

  6. Logistics • Questions about the syllabus? • Class registration • Problems with the assignment? Patrick MacAlpine

  7. Logistics • Questions about the syllabus? • Class registration • Problems with the assignment? • Piazza and Canvas — announcements yesterday Patrick MacAlpine

  8. Logistics • Questions about the syllabus? • Class registration • Problems with the assignment? • Piazza and Canvas — announcements yesterday • Last week’s slides are up Patrick MacAlpine

  9. Logistics • Questions about the syllabus? • Class registration • Problems with the assignment? • Piazza and Canvas — announcements yesterday • Last week’s slides are up • Next week’s readings are up: − Brooks’ reactive robots − A more deliberative architecture − RoboCup challenge paper Patrick MacAlpine

  10. Logistics • Questions about the syllabus? • Class registration • Problems with the assignment? • Piazza and Canvas — announcements yesterday • Last week’s slides are up • Next week’s readings are up: − Brooks’ reactive robots − A more deliberative architecture − RoboCup challenge paper • Seating arrangement Patrick MacAlpine

  11. Thermostats • Are they agents or not? • How does Wooldridge resolve this? Patrick MacAlpine

  12. Intelligent (autonomous) Agents • Autonomous robot Patrick MacAlpine

  13. Intelligent (autonomous) Agents • Autonomous robot • Information gathering agent − Find me the cheapest? Patrick MacAlpine

  14. Intelligent (autonomous) Agents • Autonomous robot • Information gathering agent − Find me the cheapest? • E-commerce agents − Decides what to buy/sell and does it Patrick MacAlpine

  15. Intelligent (autonomous) Agents • Autonomous robot • Information gathering agent − Find me the cheapest? • E-commerce agents − Decides what to buy/sell and does it • Air-traffic controller Patrick MacAlpine

  16. Intelligent (autonomous) Agents • Autonomous robot • Information gathering agent − Find me the cheapest? • E-commerce agents − Decides what to buy/sell and does it • Air-traffic controller • Meeting scheduler Patrick MacAlpine

  17. Intelligent (autonomous) Agents • Autonomous robot • Information gathering agent − Find me the cheapest? • E-commerce agents − Decides what to buy/sell and does it • Air-traffic controller • Meeting scheduler • Computer-game-playing agent Patrick MacAlpine

  18. Not Intelligent Agents • Thermostat • Telephone • Answering machine • Pencil • Java object Patrick MacAlpine

  19. Your Agent Examples Patrick MacAlpine

  20. Your Agent Examples Simple home alarm; cat food dispenser Software: anti-virus/malware agent; spam filter; web crawler; iOS autocorrect daemon Automotive: smart keys; digitial highway speed sign; traffic light with sensors; autonomous car; cruise control Telecom: GPS device; cell phone Physical Control: Roomba; lawn watering system Health: pacemaker Game/Entertainment: chess player; first person shooter AI Patrick MacAlpine

  21. An Example Patrick MacAlpine

  22. An Example • You, as a class, act as a learning agent Patrick MacAlpine

  23. An Example • You, as a class, act as a learning agent • Actions : Wave, Stand, Clap Patrick MacAlpine

  24. An Example • You, as a class, act as a learning agent • Actions : Wave, Stand, Clap • Observations : colors, reward Patrick MacAlpine

  25. An Example • You, as a class, act as a learning agent • Actions : Wave, Stand, Clap • Observations : colors, reward • Goal : Find an optimal policy Patrick MacAlpine

  26. An Example • You, as a class, act as a learning agent • Actions : Wave, Stand, Clap • Observations : colors, reward • Goal : Find an optimal policy − Way of selecting actions that gets you the most reward Patrick MacAlpine

  27. How did you do it? Patrick MacAlpine

  28. How did you do it? • What is your policy? • What does the world look like? Patrick MacAlpine

  29. Formalizing My Example Knowns: Patrick MacAlpine

  30. Formalizing My Example Knowns: • O = { Blue , Red , Green , Yellow , . . . } • Rewards in IR • A = { Wave, Clap, Stand } o 0 , a 0 , r 0 , o 1 , a 1 , r 1 , o 2 , . . . Patrick MacAlpine

  31. Formalizing My Example Knowns: • O = { Blue , Red , Green , Yellow , . . . } • Rewards in IR • A = { Wave, Clap, Stand } o 0 , a 0 , r 0 , o 1 , a 1 , r 1 , o 2 , . . . Unknowns: Patrick MacAlpine

  32. Formalizing My Example Knowns: • O = { Blue , Red , Green , Yellow , . . . } • Rewards in IR • A = { Wave, Clap, Stand } o 0 , a 0 , r 0 , o 1 , a 1 , r 1 , o 2 , . . . Unknowns: • S = 4x3 grid • R : S × A �→ IR • P = S �→ O • T : S × A �→ S Patrick MacAlpine

  33. Formalizing My Example Knowns: • O = { Blue , Red , Green , Yellow , . . . } • Rewards in IR • A = { Wave, Clap, Stand } o 0 , a 0 , r 0 , o 1 , a 1 , r 1 , o 2 , . . . Unknowns: • S = 4x3 grid • R : S × A �→ IR • P = S �→ O • T : S × A �→ S o i = P ( s i ) Patrick MacAlpine

  34. Formalizing My Example Knowns: • O = { Blue , Red , Green , Yellow , . . . } • Rewards in IR • A = { Wave, Clap, Stand } o 0 , a 0 , r 0 , o 1 , a 1 , r 1 , o 2 , . . . Unknowns: • S = 4x3 grid • R : S × A �→ IR • P = S �→ O • T : S × A �→ S o i = P ( s i ) r i = R ( s i , a i ) Patrick MacAlpine

  35. Formalizing My Example Knowns: • O = { Blue , Red , Green , Yellow , . . . } • Rewards in IR • A = { Wave, Clap, Stand } o 0 , a 0 , r 0 , o 1 , a 1 , r 1 , o 2 , . . . Unknowns: • S = 4x3 grid • R : S × A �→ IR • P = S �→ O • T : S × A �→ S o i = P ( s i ) r i = R ( s i , a i ) s i +1 = T ( s i , a i ) Patrick MacAlpine

Download Presentation
Download Policy: The content available on the website is offered to you 'AS IS' for your personal information and use only. It cannot be commercialized, licensed, or distributed on other websites without prior consent from the author. To download a presentation, simply click this link. If you encounter any difficulties during the download process, it's possible that the publisher has removed the file from their server.

Recommend


More recommend