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Understanding Everything NPCs Can Do: Metrics for Action Similarity in Non-Player Characters

Understanding Everything NPCs Can Do: Metrics for Action Similarity in Non-Player Characters. J. Timothy Balint, Jan M. Allbeck, Rafael Bidarra Foundations of Digital Games 2018. What are Actions?. Daze. An action is the definition of what NPCs can do Contains everything needed to: reason

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Understanding Everything NPCs Can Do: Metrics for Action Similarity in Non-Player Characters

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  1. Understanding Everything NPCs Can Do: Metrics for Action Similarity in Non-Player Characters J. Timothy Balint, Jan M. Allbeck, Rafael Bidarra Foundations of Digital Games 2018

  2. What are Actions? Daze • An action is the definition of what NPCs can do • Contains everything needed to: • reason • operate

  3. What are Actions? Roles Prisoner Guard • Actions have intrinsic parameters and semantics • We call these Components

  4. What are Actions? Roles Conditions - Assertions C: Prisoner can walk to guard Prisoner Guard C: Prisoner is not holding anything A: Guard is Dazed • Actions have intrinsic parameters and semantics • We call these Components

  5. What are Actions? Roles Conditions - Assertions Behavior Daze C: Prisoner can walk to guard Sequence Prisoner Guard C: Prisoner is not holding anything A: Guard is Dazed • Actions have intrinsic parameters and semantics • We call these Components Hit Approach

  6. What are Actions? Roles Conditions - Assertions Behavior Daze C: Prisoner can walk to guard Sequence Prisoner Guard C: Prisoner is not holding anything A: Guard is Dazed Behavior parts can be actions as well! • Actions have intrinsic parameters and semantics • We call these Components Hit Approach

  7. Organizing Actions Can action sets be organized?

  8. Organizing Actions Can action sets be organized?

  9. Organizing Actions Have Roles Prisoner and Guard Can action sets be organized?

  10. Organizing Actions No connection between all actions • Can action sets be organized? • Taxonomy based on name

  11. Organizing Actions • Can action sets be organized? • Taxonomy based on Roles

  12. Organizing Actions Have Roles Prisoner and Guard • Can action sets be organized? • Taxonomy based on roles

  13. Organizing Actions • How can action sets be organized? • Metrics for Similarity of Actions • Metrics for Taxonomy

  14. Similarity Metrics for Actions • Previous work looks at Levenshtein Distance • Differences in actions are binary comparison • Finds the minimum cost to convert one behavior into another

  15. Similarity Metrics for Actions Sequence Sequence Sequence Selector Previous work used Levenshtein distance for behaviors

  16. Similarity Metrics for Actions Sequence Sequence Sequence Sequence Role: Prisoner, Guard Role: Prisoner, Guard 1 Role: Prisoner, Guard Role: Prisoner, Guard Role: Prisoner, Guard 2 Role: Prisoner, Guard 1 Previous work used Levenshtein distance for behaviors

  17. Similarity Metrics for Actions • Total cost considers edit distance and set comparison • Edit distance allows sequence comparison and different costs • Similarity is 1 – normalized cost • We define costs for: • Action (if organized into a taxonomy) • Components • Behavior

  18. A Real World Example Used Shoulson et al.’s Event-Centric Planning Action Set A. Shoulson, M. L. Gilbert, M. Kapadia, and N. I. Badler, “An event-centric planning approach for dynamic real-time narrative,” in Motion in Games, Doublin, Ireland, 2013, pp. 121–130.

  19. A Real World Example Levenshtein Distance Edit Distance and Role Component Similarity

  20. A Real World Example Difference between Levenshtein and Edit Distance with Roles Component Similarity

  21. A Real World Example Difference between Levenshtein and Edit Distance with Roles Component Large Difference in Measurements Similarity

  22. A Real World Example Trap Guards Alarm Distract Guard Sequence Sequence Parallel Press (Prisoner, Button) Press (Prisoner, Button) Hide (Prisoner, Waypoint) Call (Prisoner, Guard) Trap (Guard, Trap) Parallel Approach (Prisoner, Button) Parallel Parallel Hide (Prisoner, Waypoint) Trap (Guard, Trap) Roles: 1 Guards, 3 Prisoners, 2 Waypoints, 1 door Call (Prisoner, Guard) Approach (Guard, Prisoner) Approach (Guard, Prisoner) Trap (Guard, Trap) Call (Prisoner, Guard) Approach (Guard, Prisoner) Approach (Guard, Prisoner) Trap (Guard, Trap) Call (Prisoner, Guard) Approach (Guard, Prisoner) Approach (Guard, Prisoner) Call (Prisoner, Guard) Approach (Guard, Prisoner) Approach (Guard, Prisoner) Roles: 4 Guards, 2 Prisoners, 2 buttons, 1 trap

  23. Organizing Actions into a Taxonomy Taxonomy AB AB AB ABG AG Added category actions (black) Actions in Set (Colored) Component of Action Similarity can help us set up a taxonomy Measure taxonomy using components

  24. Organizing Actions into a Taxonomy AB AB AB ABG AG Similarity can help us set up a taxonomy Measure taxonomy using components

  25. Organizing Actions into a Taxonomy Taxonomy AB AB AB ABG AG AB A G Similarity can help us set up a taxonomy Measure taxonomy using components

  26. Organizing Actions into a Taxonomy Taxonomy A AB AB AB ABG AG AB A G Similarity can help us set up a taxonomy Measure taxonomy using components

  27. Organizing Actions into a Taxonomy Taxonomy A AB AB AB ABG AG AB A G Similarity can help us set up a taxonomy Measure taxonomy using components

  28. Organizing Actions into a Taxonomy Taxonomy A B AB A G Similarity can help us set up a taxonomy Measure taxonomy using components

  29. Organizing Actions into a Taxonomy Taxonomy A B B G Similarity can help us set up a taxonomy Measure taxonomy using components

  30. Organizing Actions into a Taxonomy Taxonomy Taxonomy (Original) A B AB AB AB ABG AG B G

  31. Another Real World Example Using Conditions and Assertions Using Roles Component similarity guides creation

  32. Another Real World Example A. Shoulson, M. L. Gilbert, M. Kapadia, and N. I. Badler, “An event-centric planning approach for dynamic real-time narrative,” in Motion in Games, Doublin, Ireland, 2013, pp. 121–130. Event Centric Planning* action set No organization and three organization strategies Binary task comparison

  33. Another Real World Example • Two different agent reasoning systems • Crowd (uses roles) • Define 10 areas for each role set • 1000 trials • Traditional Planner (uses condition and assertions) • Time to find a plan that satisfies three randomly chosen assertions • Average found plan is 8 to 10 actions • 20 trials

  34. Another Real World Example Using a Role-Based Action Selector

  35. Another Real World Example Using a Role-Based Action Selector

  36. Another Real World Example Using a Condition and Assertion Based Action Selector

  37. Another Real World Example Using a Condition and Assertion Based Action Selector

  38. Conclusions • Action taxonomies can assist in NPC decision making • Generalize Data • Pre-prune Tree • Action Similarity teases out difference in meaning • More information = More insight

  39. Limitations • Comparing semantics means we need semantics • Similarity is only a guide • More complicated reasoning makes it more difficult to build taxonomies

  40. Questions? • Email: j.t.balint@tudelft.nl • Action Metric Repository: • https://github.com/j-timothy-balint/Action-Comparison

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