Character AI: Sensing & Perception
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- Letitia Davidson
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1 Lecture 21 Character AI:
2 Take Away for Today Sensing as primary bottleneck Why is sensing so problematic? What types of things can we do to improve it? Optimized sense computation Can we improve sense computation performance? Can we share sensing between NPCs? Sense event matching What are events and how are y represented? What is advantage of an event system? 2
3 Review: Sense-Think-Act Sense: Perceive world Reading game state Example: enemy near? Think: Choose an action Often merged with sense Example: fight or flee Act: Update state Simple and fast Example: reduce health? 3
4 Recall: Sensing Performance Sensing may be slow! Consider all objects Example: morale n knights, n skeletons Knights fear skeletons Proportional to # seen Count skeletons in view O(n) to count skeletons O(n 2 ) for all units Time per tick 3 units 2 units 1 unit 4
5 Recall: Sensing Performance Sensing may be slow! Consider all objects Example: morale n knights, n skeletons Knights fear skeletons Proportional to # seen Count skeletons in view O(n) to count skeletons O(n 2 ) for all units Time per tick 3 units 2 units 1 unit 5
6 Aggregation Idea taken from databases Unordered set of information Combine into single value Used in statistical analysis Examples: sum, avg, mode Decomposable Aggregates Split set up into subsets Aggregate on each subset Combine values from subsets Only for some aggregates
7 Aggregation Idea taken from databases Unordered set of information Combine into single value Used in statistical analysis Examples: sum, avg, mode Decomposable Aggregates Split set up into subsets Aggregate on each subset Combine values from subsets Only for some aggregates avg = 8 (16,2) avg = 6 (12,2) avg = 8 (16,2) avg = 6 (12,2) 7
8 Aggregation Idea taken from databases Unordered set of information Combine into single value Used in statistical analysis Examples: sum, avg, mode avg = 8 (16,2) avg = 6 (12,2) avg = 7 (28,4) Decomposable Aggregates Split set up into subsets Aggregate on each subset Combine values from subsets Only for some aggregates avg = 8 (16,2) avg = 6 (12,2) avg = 7 (28,4) 8
9 Aggregation Idea taken from databases Unordered set of information Combine into single value Used in statistical analysis Examples: sum, avg, mode Decomposable Aggregates Split set up into subsets Aggregate on each subset Combine values from subsets Only for some aggregates avg = 8 (16,2) avg = 6 (12,2) avg = 8 (16,2) avg = 6 (12,2) avg = 7 (28,4) avg = 7 (28,4) avg = 7 (56,8) 9
10 Aggregation Idea taken from databases Unordered set of information Combine into single value Used in statistical analysis Examples: sum, avg, mode Decomposable Aggregates Split set up into subsets Aggregate on each subset Combine values from subsets Only for some aggregates avg = 8 (16,2) avg = 6 (12,2) avg = 8 (16,2) avg = 6 (12,2) avg = 7 (28,4) avg = 7 (28,4) avg = 7 (56,8) 10
11 AI and Aggregation Trees Slide courtesy of Dave Mark Number of Allies Strength of Allies Number of Enemies Strength of Enemies Proximity to Base My Health Proximity to Leader 11
12 AI and Aggregation Trees Slide courtesy of Dave Mark Number of Allies Strength of Allies Number of Enemies Strength of Enemies Allied Strength Enemy Strength Proximity to Base My Health Proximity to Leader 12
13 AI and Aggregation Trees Slide courtesy of Dave Mark Number of Allies Strength of Allies Number of Enemies Strength of Enemies Allied Strength Threat Ratio Enemy Strength Proximity to Base My Health Proximity to Leader 13
14 AI and Aggregation Trees Slide courtesy of Dave Mark Number of Allies Strength of Allies Number of Enemies Strength of Enemies Allied Strength Threat Ratio Enemy Strength Proximity to Base My Health Proximity to Leader Urgency 14
15 AI and Aggregation Trees Slide courtesy of Dave Mark Number of Allies Strength of Allies Number of Enemies Strength of Enemies Allied Strength Threat Ratio Enemy Strength Proximity to Base My Health Proximity to Leader Urgency My Morale 15
16 AI and Aggregation Trees Slide courtesy of Dave Mark Number of Allies Strength of Allies Number of Enemies Strength of Enemies Allied Strength Threat Ratio Enemy Strength Proximity to Base My Health Proximity to Leader Urgency My Morale Retreat % 16
17 AI and Aggregation Trees Slide courtesy of Dave Mark Number of Allies Strength of Allies Number of Enemies Strength of Enemies Allied Strength Enemy Strength My Health Proximity to Leader Threat Ratio Computable independent Urgency Proximity to Base My Morale of NPC Retreat % 17
18 Related Approach: Tactical Managers Invisible NPC Assigned to NPC Group Performs all thinking Tactical Manager NPCs just follow orders Applications Protecting special units Flanking NPC NPC NPC NPC Covering fire Leapfrogging advance 18 Thinking and Acting
19 Protecting Special Units Slide courtesy of Dave Mark 19 Thinking and Acting
20 Protecting Special Units Slide courtesy of Dave Mark 20 Thinking and Acting
21 Protecting Special Units Slide courtesy of Dave Mark 21 Thinking and Acting
22 Protecting Special Units Slide courtesy of Dave Mark 22 Thinking and Acting
23 Protecting Special Units Slide courtesy of Dave Mark 23 Thinking and Acting
24 Protecting Special Units Slide courtesy of Dave Mark 24 Thinking and Acting
25 Protecting Special Units Slide courtesy of Dave Mark 25 Thinking and Acting
26 Protecting Special Units Slide courtesy of Dave Mark 26 Thinking and Acting
27 Protecting Special Units Slide courtesy of Dave Mark 27 Thinking and Acting
28 Performance: Loop Inversion Normal Sensing Inverted Sensing sound1 NPC Loop Sense Loop sound2 sight1 sight1 Loop over all NPCs Check what NPC senses Loop over sensations Send se to each NPC 28
29 Performance: Loop Inversion Normal Sensing Inverted Sensing sound1 NPC Loop Sense Loop sound2 sight1 sight1 Loop over all NPCs Check what NPC senses Loop over sensations Send se to each NPC 29
30 Sense Events Event: encoded sense data Tagged with sense type Pre-aggregated Information self-contained O(n) data is aggregated O(1) to combine w/ NPC Sensing is event matching Each event has a type NPCs register for a type Send NPC registered events Check if event is relevant 30
31 Sense Event Matching sound sight sound Register events of interest Event Handler Game Loop sound smell 31
32 Sense Event Matching Notify of any matching events Event Handler Game Loop Check for any matching events 32
33 Event Handling in LibGDX MessageDispatcher Send with dispatchmessage delay (0 if immediate) sender (can be null) target (null for subscribers) type (user defined int code) data (object, like Box2D) Subscribe with addlistener NPC to receive message Type (int) to subscribe to Telegram Stores event message Entries of dispatchmessage Except for delay value Preaggregated sense in data Received by Telegraph Interface for receiver Implemented by NPC One method: handlemessage 33
34 Event Handling in LibGDX MessageDispatcher Send with dispatchmessage delay (0 if immediate) sender (can be null) target (null for subscribers) type (user defined int code) data (object, like Box2D) Subscribe with addlistener NPC to receive message Type (int) to subscribe to Telegram Stores event message Entries of dispatchmessage Except for delay value Preaggregated sense in data Received by Telegraph Interface for receiver Implemented by NPC One method: handlemessage 34
35 Recall: S-T-A Architecture Actor1 Controller Actor2 Controller Compute Sensing GameState Actor1 Actor2 35 Thinking and Acting
36 Recall: S-T-A Architecture Actor1 Controller Actor2 Controller Compute Sensing GameState Actor1 Actor2 36 Thinking and Acting
37 Recall: S-T-A Architecture Actor1 Controller Actor2 Controller Compute Thinking GameState Actor1 Actor2 37 Thinking and Acting
38 Sensing: Perception Groups Vision: limited field of view Gives exact object location, information Limited by obstacles and range Little information (motion) at periphery Sound: omni-directional Gives direction & distances Requires you track sounds actions make Smell: omni-directional No direction or distance; proximity only Requires you track smells actions make 38
39 Case Study: Thief Series 39
40 40
41 Line-of-Sight in Thief Long Distance Focused View Peripheral Vision Short Distance 41
42 Line-of-Sight in Thief Long Distance Focused View Motion Detection Peripheral Vision Short Distance 42
43 Sounds in Thief Easier than vision Primarily distance-based Decays probabilistically Tag with level of interest Sounds can be blocked Not same as line-of-sight Use alternate level map Or tag your visible map Not physically realistic Echoes? Reflections 43
44 Sounds in Thief Easier than vision Primarily distance-based Decays probabilistically Tag with level of interest Sounds can be blocked Not same as line-of-sight Use alternate level map Or tag your visible map Not physically realistic Echoes? Reflections 44
45 Sounds in Thief Sounds are general purpose Resuable framework Code is lightweight Encodes or senses Example: Smell Treated as pseudo-sound Generate like any sound * sniff * Again, ignores or factors * sniff * Wind direction Masking smells 45
46 Custom Data in Events Lightweight Heavyweight Memory Target Memory Target ThiefExposed Reference to target ThiefExposed Copy of target 46
47 Custom Data in Events Lightweight Advantages Fast to create event No additional memory Disadvantages Must be used immediately Lost over frame boundary Ideal for fast decisions Heavyweight Advantages Can persist past frame Can retain as memory Disadvantages Must allocate memory Object ownership is tricky Ideal for communication 47
48 Communicating Senses 48
49 Communicating Senses First Hand LOS Sight & Sound 49
50 Communicating Senses First Hand LOS Sight & Sound First Hand LOS Sight & Sound 50
51 Communicating Senses First Hand LOS Sight & Sound First Hand LOS Sight & Sound Second Hand Sight & Sound 51
52 Communicating Senses First Hand LOS Sight & Sound First Hand LOS Sight & Sound Second Hand Sight & Sound? 52
53 Alertness: Active Senses High Alert First Hand LOS Sight & Sound First Hand LOS Sight & Sound Second Hand Sight & Sound 53
54 Alertness: Active Senses High Alert First Hand LOS Sight & Sound Medium Alert First Hand LOS Sight & Sound Second Hand Sight & Sound 54
55 Alertness: Active Senses High Alert First Hand LOS Sight & Sound Medium Alert First Hand LOS Sight & Sound Second Hand Sight & Sound High Alert First Hand Sound 55
56 Alertness: Active Senses High Alert First Hand LOS Sight & Sound Medium Alert First Hand LOS Sight & Sound Second Hand Sight & Sound High Alert 56
57 Alertness: Active Senses High Alert First Hand LOS Sight & Sound Medium Alert First Hand LOS Sight & Sound Second Hand Sight & Sound High Alert First Hand Sound 57
58 Spatial Optimizations Restrict to nearby NPCs Have detection range Limits events sensed Easy to combine with event matching system Works in both directions Nimbus: can see radius Aura: can be seen radius Area of interest management 58
59 Spatial Optimizations Restrict to nearby NPCs Have detection range Limits events sensed Easy to combine with event matching system Works in both directions Nimbus: can see radius Aura: can be seen radius Area of interest management 59
60 Spatial Optimizations Restrict to nearby NPCs Have detection range Limits events sensed Easy to combine with event matching system Works in both directions Nimbus: can see radius Aura: can be seen radius Area of interest management 60
61 Thief: Sense Events and Aggregation Position Lighting Movement Exposure Sound System Visibility Sound Queue Viewcone Selector Viewcone Non-specific Spatial Events Look Listen Sense Pulse Receiver Game Mechanics and Configuration Inter-Agent Communication Ramp Up Delay Cool-down Capacitor Inter-Agent Observation 61 Sense Links
62 Summary Sensing is most expensive part of AI Each character looks at every object in game Often leads to O(n 2 ) behavior (bad!) Can optimize sense garing Aggregation is amenable to parallelization Can piggyback some data onto pathfinding Event matching inverts sensing problem Creation of sense makes a data event Forward event to relevant NPCs 62
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