Total War: how AI can deliver entertainment.- Duygu Cakmak & Guy Davidson [ACCU 2018]

on YouTube ↗01h 18m

" summary

Campaign AI uses Monte Carlo Tree Search for both resource allocation and coordination, with domain-specific pruning, topological ordering and group splitting to cut the combinatorial explosion. A lazy-evaluated analysis cache—about 32 analyzers—keeps world-state queries cheap, and diplomacy moves from hard‑coded generators to a data-driven diplomacy system that prioritizes and evaluates hundreds of candidate goals. The stack runs to tens–hundreds of milliseconds per entity and the approach yields playable, credible AI under strict time and memory budgets.

AI-written summary · about

" chapters (45)

1. 0:00Introduction
2. 0:42Campaign overview
3. 3:07Game mechanics
4. 4:48Turn sequence
5. 6:14Seasons
6. 7:48Wind conditions
7. 9:06The model
8. 10:44Perfect information
9. 12:10Expose information
10. 14:08Personality
11. 15:48Team
12. 16:16AI subsystems
13. 18:59World information
14. 23:02Optimization
15. 23:53Analyzers
16. 24:13Analyzer Base
17. 25:07Financial System
18. 25:28Financial Analyzer
19. 26:00Validation
20. 26:43Summary
21. 27:33Diplomacy
22. 31:15Goal generators
23. 32:12Old system
24. 33:29Datadriven approach
25. 34:37Generators
26. 35:19Treaty Keys
27. 35:34Conditions
28. 36:27Condition
29. 37:02Target Group
30. 37:38List Limit
31. 38:02Faction Priority
32. 42:08Evaluation Criteria
33. 43:38Evaluation Value Function
34. 44:02Associated Criteria
35. 44:20Difference in the Game
36. 45:37Dynamics
37. 47:24Task Generation
38. 48:13Generator Task
39. 49:09Test Generator
40. 49:51Resource Allocation
41. 52:28Resource Allocation System
42. 53:45Resource Coordination System
43. 54:46Implementation
44. 59:08Top Places
45. 59:34Order of nodes

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