cs344m autonomous multiagent systems

CS344M Autonomous Multiagent Systems Todd Hester Department of - PowerPoint PPT Presentation

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


  1. CS344M Autonomous Multiagent Systems Todd Hester Department of Computer Science The University of Texas at Austin

  2. Good Afternoon, Colleagues Are there any questions? Todd Hester

  3. Good Afternoon, Colleagues Are there any questions? Todd Hester

  4. Logistics • First assignment: how did it go? Todd Hester

  5. Logistics • First assignment: how did it go? • Next soccer assignment: score a goal and passing Todd Hester

  6. Logistics • First assignment: how did it go? • Next soccer assignment: score a goal and passing − Help each other with C issues – parsing strings Todd Hester

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

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

  9. Self-Introductions • Speak loudly Todd Hester

  10. Self-Introductions • Speak loudly • Name, year, major Todd Hester

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

  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? Todd Hester

  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? Todd Hester

  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 Todd Hester

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

  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. Todd Hester

  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? Todd Hester

  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 Todd Hester

  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 Todd Hester

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

  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 Todd Hester

  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 Todd Hester

  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 Todd Hester

  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 Todd Hester

  25. Environments Environment = ⇒ sensations, actions Todd Hester

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

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

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

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

  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 Todd Hester

  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 Todd Hester

  32. The Decision Todd Hester

  33. The Decision • reactive vs. deliberative Todd Hester

  34. The Decision • reactive vs. deliberative • multiagent reasoning? Todd Hester

  35. The Decision • reactive vs. deliberative • multiagent reasoning? • learning? Todd Hester

  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 ) Todd Hester

  37. Standard/Reactive/State-based Agents • Observation P , Action A , Internal State I Todd Hester

  38. Standard/Reactive/State-based Agents • Observation P , Action A , Internal State I • Standard agent: Todd Hester

  39. Standard/Reactive/State-based Agents • Observation P , Action A , Internal State I action : P ∗ �→ A • Standard agent: Todd Hester

  40. Standard/Reactive/State-based Agents • Observation P , Action A , Internal State I action : P ∗ �→ A • Standard agent: • Reactive agent: Todd Hester

  41. Standard/Reactive/State-based Agents • Observation P , Action A , Internal State I action : P ∗ �→ A • Standard agent: • Reactive agent: action : P �→ A Todd Hester

  42. Standard/Reactive/State-based Agents • Observation P , Action A , Internal State I action : P ∗ �→ A • Standard agent: • Reactive agent: action : P �→ A − Decision based entirely on the present Todd Hester

  43. Standard/Reactive/State-based Agents • Observation P , Action A , Internal State I action : P ∗ �→ A • Standard agent: • Reactive agent: action : P �→ A − Decision based entirely on the present • State-based agent: Todd Hester

  44. Standard/Reactive/State-based Agents • Observation P , Action A , Internal State I 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 Todd Hester

  45. Standard/Reactive/State-based Agents • Observation P , Action A , Internal State I 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. Todd Hester

  46. Standard/Reactive/State-based Agents • Observation P , Action A , Internal State I 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. Todd Hester

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