cs344m autonomous multiagent systems

CS344M Autonomous Multiagent Systems Patrick MacAlpine Department - PowerPoint PPT Presentation

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


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

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

  3. Good Afternoon, Colleagues Are there any questions? • Pending questions: − How are agents like automatons? − What is episodic? − What is deterministic? − Set theory in states/actions? − Is a pencil an agent? Patrick MacAlpine

  4. Logistics • First assignment: how did it go? Patrick MacAlpine

  5. Logistics • First assignment: how did it go? • Next soccer assignment: score a goal Patrick MacAlpine

  6. Logistics • First assignment: how did it go? • Next soccer assignment: score a goal − Help each other with C issues — parsing strings Patrick MacAlpine

  7. Logistics • First assignment: how did it go? • Next soccer assignment: score a goal − Help each other with C issues — parsing strings − Evaluating mostly on the logic — does the agent “do the right thing?” Patrick MacAlpine

  8. Logistics • First assignment: how did it go? • Next soccer assignment: score a goal − Help each other with C issues — parsing strings − Evaluating mostly on the logic — does the agent “do the right thing?” • 2D or 3D? Patrick MacAlpine

  9. Self-Introductions • Speak loudly Patrick MacAlpine

  10. Self-Introductions • Speak loudly • Name, year, major Patrick MacAlpine

  11. Self-Introductions • Speak loudly • Name, year, major • At least one other thing about yourself Patrick MacAlpine

  12. Discussion An autonomous agent is a system situated within and a part of an environment that senses that environment and acts on it, over time, in pursuit of its own agenda and so as to affect what it senses in the future. • Is this a good definition? • The authors claim is is a “formal” definition of agents. Is it? Patrick MacAlpine

  13. Discussion An autonomous agent is a system situated within and a part of an environment that senses that environment and acts on it, over time, in pursuit of its own agenda and so as to affect what it senses in the future. • Is this a good definition? • The authors claim is is a “formal” definition of agents. Is it? • Can you do better? Patrick MacAlpine

  14. Discussion An autonomous agent is a system situated within and a part of an environment that senses that environment and acts on it, over time, in pursuit of its own agenda and so as to affect what it senses in the future. • Is this a good definition? • The authors claim is is a “formal” definition of agents. Is it? • Can you do better? • Do they need to be social? persistent? • Can they cease to be agents in a different environment? • Autonomy Patrick MacAlpine

  15. Varieties of Autonomy • Do we have complete freedom over our beliefs, goals, and actions? Patrick MacAlpine

  16. Varieties of Autonomy • Do we have complete freedom over our beliefs, goals, and actions? • Software service has no autonomy — does what it’s told. Patrick MacAlpine

  17. Varieties of Autonomy • Do we have complete freedom over our beliefs, goals, and actions? • Software service has no autonomy — does what it’s told. • What’s Wooldridge’s take on where autonomous agents lie on the spectrum? Patrick MacAlpine

  18. Varieties of Autonomy • Do we have complete freedom over our beliefs, goals, and actions? • Software service has no autonomy — does what it’s told. • What’s Wooldridge’s take on where autonomous agents lie on the spectrum? − Decide how to act so as to accomplish delegated goals Patrick MacAlpine

  19. Varieties of Autonomy • Do we have complete freedom over our beliefs, goals, and actions? • Software service has no autonomy — does what it’s told. • What’s Wooldridge’s take on where autonomous agents lie on the spectrum? − Decide how to act so as to accomplish delegated goals • Also mentions adjustable autonomy Patrick MacAlpine

  20. My Requirements of Agents • They must sense their environment. • They must decide what action to take (“think”). • They must act in their environment. Patrick MacAlpine

  21. My Requirements of Agents • They must sense their environment. • They must decide what action to take (“think”). • They must act in their environment. Complete Agents Patrick MacAlpine

  22. My Requirements of Agents • They must sense their environment. • They must decide what action to take (“think”). • They must act in their environment. Complete Agents Multiagent systems: Interact with other agents Patrick MacAlpine

  23. My Requirements of Agents • They must sense their environment. • They must decide what action to take (“think”). • They must act in their environment. Complete Agents Multiagent systems: Interact with other agents Learning agents: Improve performance from experience Patrick MacAlpine

  24. My Requirements of Agents • They must sense their environment. • They must decide what action to take (“think”). • They must act in their environment. Complete Agents Multiagent systems: Interact with other agents Learning agents: Improve performance from experience Autonomous Bidding, Cognitive Systems, Traffic management, Robot Soccer Patrick MacAlpine

  25. Environments Environment = ⇒ sensations, actions Patrick MacAlpine

  26. Environments Environment = ⇒ sensations, actions • fully observable vs. partially observable (accessible) Patrick MacAlpine

  27. Environments Environment = ⇒ sensations, actions • fully observable vs. partially observable (accessible) • deterministic vs. non-deterministic Patrick MacAlpine

  28. Environments Environment = ⇒ sensations, actions • fully observable vs. partially observable (accessible) • deterministic vs. non-deterministic • episodic vs. non-episodic Patrick MacAlpine

  29. Environments Environment = ⇒ sensations, actions • fully observable vs. partially observable (accessible) • deterministic vs. non-deterministic • episodic vs. non-episodic • static vs. dynamic Patrick MacAlpine

  30. Environments Environment = ⇒ sensations, actions • fully observable vs. partially observable (accessible) • deterministic vs. non-deterministic • episodic vs. non-episodic • static vs. dynamic • discrete vs. continuous Patrick MacAlpine

  31. Environments Environment = ⇒ sensations, actions • fully observable vs. partially observable (accessible) • deterministic vs. non-deterministic • episodic vs. non-episodic • static vs. dynamic • discrete vs. continuous • single-agent vs. multiagent Patrick MacAlpine

  32. The Decision Patrick MacAlpine

  33. The Decision • reactive vs. deliberative Patrick MacAlpine

  34. The Decision • reactive vs. deliberative • multiagent reasoning? Patrick MacAlpine

  35. The Decision • reactive vs. deliberative • multiagent reasoning? • learning? Patrick MacAlpine

  36. Formalizing My Example Knowns: • O = { Blue , Red , Green , Black , . . . } • 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

  37. Standard/Reactive/State-based Agents • Standard agent: Patrick MacAlpine

  38. Standard/Reactive/State-based Agents action : P ∗ �→ A • Standard agent: Patrick MacAlpine

  39. Standard/Reactive/State-based Agents action : P ∗ �→ A • Standard agent: • Reactive agent: Patrick MacAlpine

  40. Standard/Reactive/State-based Agents action : P ∗ �→ A • Standard agent: • Reactive agent: action : P �→ A Patrick MacAlpine

  41. Standard/Reactive/State-based Agents action : P ∗ �→ A • Standard agent: • Reactive agent: action : P �→ A − Decision based entirely on the present Patrick MacAlpine

  42. Standard/Reactive/State-based Agents action : P ∗ �→ A • Standard agent: • Reactive agent: action : P �→ A − Decision based entirely on the present • State-based agent: Patrick MacAlpine

  43. Standard/Reactive/State-based Agents action : P ∗ �→ A • Standard agent: • Reactive agent: action : P �→ A − Decision based entirely on the present • State-based agent: action : I �→ A , next : I × P �→ I Patrick MacAlpine

  44. Standard/Reactive/State-based Agents action : P ∗ �→ A • Standard agent: • Reactive agent: action : P �→ A − Decision based entirely on the present • State-based agent: action : I �→ A , next : I × P �→ I It is worth observing that state-based agents as defined here are in fact no more powerful than the standard agents we introduced earlier. In fact, they are identical in their expressive power. Patrick MacAlpine

  45. Standard/Reactive/State-based Agents action : P ∗ �→ A • Standard agent: • Reactive agent: action : P �→ A − Decision based entirely on the present • State-based agent: action : I �→ A , next : I × P �→ I It is worth observing that state-based agents as defined here are in fact no more powerful than the standard agents we introduced earlier. In fact, they are identical in their expressive power. Reactive agents for next Thursday’s assignment task? Patrick MacAlpine

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