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Matt Mitchell David Pennenga John Formica.  Topics to cover  Levels of threat  Straying from the team  Use of caution  Team context  Speed of movement.

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Presentation on theme: "Matt Mitchell David Pennenga John Formica.  Topics to cover  Levels of threat  Straying from the team  Use of caution  Team context  Speed of movement."— Presentation transcript:

1 Matt Mitchell David Pennenga John Formica

2  Topics to cover  Levels of threat  Straying from the team  Use of caution  Team context  Speed of movement

3  The more targets, the greater the possibility of obstructing line of sight (LOS)  How to fix this?  If the player is looking in the direction of NPC  Move to the angle of LOS that has less targets

4  If the player is looking away from NPC  NPC should move in opposite direction that the player is looking

5  If above heuristics fail, prioritize obstruction  Threat level of enemy  A player is more likely to target:  enemies with greater threat levels ▪ Obstruct view of enemies with lesser threat levels  Groups of enemies over individuals ▪ Opt for obstructing view of smaller groups or individuals ▪ Threat level also should be considered

6  NPCs should stay close to the team  Set maximum radius from team  Why?  Straying from the team could: ▪ Alert enemy combatants ▪ Distract player from game ▪ Trying to find lost teammates ▪ Cause failure to assist player

7  Teammates should avoid risking the welfare of the team  NPCs should not be the first to engage an enemy  Explore a room  Move over a hill or around a corner  This could undermine the player’s play style

8  Take different play styles of the player into consideration  Some games allow player to set play styles ▪ Aggressive vs passive ▪ E.G. Republic Commando ▪ “search and destroy” ▪ “form up” ▪ “hold position”  Move at the same pace as player  But not obstructing LOS  Consider tactically sound behavior ▪ We will cover more about this shortly

9  Types of behavior  Use of cover  Selective firing  reloading

10  Path-finding algorithm  Prefer cover and the shadows ▪ Causes player to believe in tactical prowess of NPCs ▪ Adds to realism of game play ▪ Prevents accidentally alerting NPCs  Take player cues If NPC behavior does not match player behavior ▪ E.G. Speed of player movement could cancel priority for cover in path-finding algorithm if player appears to be in a rush

11  Only fire when within range and in view  Controlled bursts ▪ Maintain accuracy ▪ Conserve ammunition  Reaction times ▪ Should be manipulated if individual combat-related AI is too powerful ▪ Balance high accuracy with low reaction times ▪ Certain manipulations also make contextual sense: Rainbow Six style squad games ▪ Team reaction time should not be lower than enemy team’s reaction time

12  Cycling aggressive NPCs  Reloading NPCs fall back or take cover ▪ Return when finished  Stagger fire of NPCs  So NPCs do not run out of ammunition at the same time  “taking turns” at attacking ensures that player will most likely always have supporting fire

13  Close support of the player  Protecting the rear  Reporting to the player  Selecting a target  Responding to orders

14  Protect player’s sides  Report threats via audio or visual cues ▪ Messages are a staple of squad game play ▪ Do not overwhelm player ▪ Reporting threats while in combat is not usually necessary  Pick appropriate engagement times ▪ Not in a fire fight ▪ Do not aggravate more enemies than already fighting

15  Chose targets other than the player’s  Chose targets farthest from the player’s LOS  NPCs are there to complement game play not hinder it  Also should take threat level into account  Player might need assistance ▪ Health of player’s target versus player’s own health  Consider higher concentration of targets

16  Weapon selection  NPC weapon should not be more powerful than player’s unless dictated by player  Weapon and item selection  Allow player first choice ▪ Implemented by tracking player proximity to items ▪ If the player approaches items and walks away then NPCs may pick up items  Reaction time  Allow player to attack enemies first ▪ Unless team is under attack

17  Line of sight avoidance hierarchy  Each child avoids the line of sight of the parent  Allows for easy restructuring in the event of casualties ▪ Node replacement algorithms  Threat awareness  Use LOS algorithm to find and report targets ▪ Maintain group awareness of threats by “looking around” so that NPC LOS updates, player awareness of environment ▪ Used in some groups’ Robocode this semester  Environment awareness  Identify cover/restricted areas/straying from player

18  NPCs reference a team knowledge base that governs NPC behavior  Finding available NPCs  Availability = (1+N)(1+O)(1+P)+(Q*[infinity]) ▪ N= # enemies in covering area (integer value) ▪ O= # enemies within range (integer value) ▪ P= # enemies threatening team (integer value) ▪ Q= supporting another teammate (boolean value)  The above formula ▪ Provides a return value of greatest value in the event that the NPC in query is supporting a teammate (optimal behavior) ▪ If the NPC is not supporting teammate, it provides a finite return value which can easily be compared with the availability of other NPCs

19  Rainbow Six: Raven Shield  Team of AI finish a mission   Visible in this video  Maintaining constant awareness of environment  Reaction time alterations for context ▪ Very fast for mimicking elite soldier behavior  “Stick to the shadows” path-finding  Controlled fire  NPCs stay close to the team  Appears to use LOS dominance hierarchy

20  Decentralized approach  Squad members exchange info equally ▪ Messages!  No commanding force ▪ Instead, equally shared knowledge bases for reference of each member  Squad behavior is an extension of individual AI  Centralized approach  Leader receives and interprets info from soldiers  Orders soldier behavior ▪ Differing strength of orders ▪ This will be covered shortly

21  Pros  Extension of individual AI (easier to implement) ▪ All AI simply must communicate with each other either directly (messages) or indirectly (communicating with an abstract knowledge base)  Robustly handles situations ▪ Emergent behavior facilitates problem solving without having to specifically code for given tasks  Deals well with variations in capabilities of teammates ▪ Individual AI is permitted to interpret or act upon supplied info differently ▪ hence this approach being an extension of individual AI  Easily combined with scripted AI ▪ Same reason as above  Cons  Weak at autonomy or tight coordination ▪ Lack of specific plans for problems hinders team cohesion  Cannot deal with individual strengths/weaknesses

22  Tightly coordinated  Members rely on detailed, repeatedly rehearsed drills, and continuous flow of info ▪ In CSE terms, events are anticipated and coded specifically to be solved  Loosely coordinated  Does not rely on planned maneuvers  Relies more on verbal communications

23  Fire and maneuver behavior  individual behavior of either firing or moving ▪ Communicate this within the squad ▪ Emergent behavior of position cycling  Goal behavior achieved via communication  Stay close to cover ▪ Communicate info of cover whereabouts  Prevent blocking LOS of others ▪ Communicate direction of LOS  Take weapon capabilities into account ▪ Damage, area of effect, etc.  Maintain group cohesion ▪ NPC location and intentions  Spread out ▪ Communicate distance from other NPCs  Keep LOS on targets ▪ Target locations, if the target is moving in the direction of another NPC

24  For each member  Current position and activity  Claimed destination position  LOS  For each opponent  Last known position/state  Estimated current position  Members engaging opponent  Members able to observe opponent  LOS  For other hazards/threats  Known/estimated position  Damage radius

25  Why so preferred?  Model communication latency by queuing messages  Present the message in game play  Can filter messages for player benefit ▪ Prioritize info of messages  Sends to dead members assumed alive for realism ▪ Only serves a purpose when player witnesses it  Can use scripted entities to direct members ▪ The content of messages serves a purpose whether the source of the info is adaptable AI or not  Accommodates human members

26  Goals  Wait for enemy to enter kill zone  Pull back to predefined rally point after engagement  Fall back when discovered  How does a decentralized approach handle ambushing?

27  Decentralized approach…  Returns fire well  Can prefer being near rally point  BUT ▪ Cannot reach unanimous behavior on attacking because each agent will carry out its own behavior ▪ Cannot orchestrate movement well because there is no central executive force to create a consensus of behavior

28  Centralized approach styles  Authoritarian command style ▪ Focuses team performance by forcing agents to perform commands ▪ Cannot be ignored by individual AI  Coaching command style ▪ Advises, rather than forces, agents ▪ Allows for agents to override commands ▪ Problems could arise from agents wanting to always perform their own behavior

29  Things to consider  Force ratio ▪ ratio of allies to enemies  Line of fire ratio ▪ ratio of # of enemies in allies’ line of fire to the opposite  Consider Boolean states to make judgment calls  Knowledge bases  First order logic conjunctions

30  Chosen path is a good combination of  Short travel time  Concealment from pursuing enemies  Sufficient space to maneuver

31  Progress should be monitored  Arriving at position informs the squad  Provides individuals with a new command  Pros of this approach  Allows for changes in squad behavior mid- maneuver ▪ E.G. a member is trapped in a spot which forces a reevaluation of the path finding algorithm and sends it far away from the squad

32  Use maps or an alteration of the A* function  New cost function considers  Nodes that can be fired at from preceding nodes  Nodes that provide insufficient space to bypass  Nodes that are a bad position from which to fight  The time and spatial complexity of this new function force it to be applied to the squad aggregate for practicality

33  To properly pull back  Mark each position just before a bend in the path that blocks the LOS of many preceding positions on the path  Features sufficient room for members to bypass it ▪ If first choice of bypass is obstructed some how, a second choice should be available  Is a good position from which to fire ▪ Pulling back across an open field versus a rocky hillside

34  Chain of command:  Commander -> captain -> sergeant -> soldier  More subdivisions are allowed if necessary  Creates an organized and efficient way of commanding soldiers

35  Strategic Decision Process  Check if maneuver is possible/appropriate ▪ Doors are accessible (referencing door IDs so there are no duplicate actions)  Compare ▪ Distance between opponent and door with distance between soldier and door  Maneuver organization  Consider distance to closest door so maneuver is as fast as possible

36  Strategic decision process  Try to determine whether the maneuver is possible/advantageous ▪ Check if ally must cross a specific room in order to get from their room to a target

37  Maneuver organization  Find how many soldiers are needed to cover exits  Order soldiers to cover designated positions  Decide on a room to search  Order soldiers to move to reachable doors of the first room  When all of the above have been completed, a search command is sent to squad

38  Types of formations  Line  Left flank  Right flank  Column  Box  Wedge  Vee

39  Different facing applies to different tactics  Defense vs offense  LOS  Friendly fire  Project more firepower  Staggered line  Outward facing vs inward facing

40  Mixed unit type ordering  Weaker/longer-ranged units in the back of the line or center of a box  Faster units placed at flanks  Closest position  Calculating the best match of unites to positions ▪ Sort units based on their minimum distance to the closest position ▪ Iterate through the sorted list ▪ Assign each unit the closest unused position

41  Direction may play a role if turning is difficult  Create formation positions that avoid crossing paths of units  Use movement vector and parallel paths  Arriving time  Fix variations in arrival time by making all units’ paths lead to the center of the formation and order is determined by arrival time (illustration on next slide)


43  Spacing distance  Must be scaled so that the size difference in units is considered  Ranks  Each formation has a maximum occupant limit ▪ Start new formation once reached  Playbook  If certain places are reserved for specific units restrictions must be coded ▪ If no match found for a position, slot may be skipped (hole is made)

44  When do the units fall into formation?  Upon initial movement  Might delay movement  Might cause unit backtracking  Just before destination  Might give player impression that nothing is happening  Might be best choice for coordinated attack

45  do formation at destination  Faster movement/no delays  Formations made as distance to destination decreases  Not as effective while en route  Not as organized upon arrival

46  Group path finding and movement  Calculate path for lead, others apply formation offsets to path  Flocking style where group follows slightly behind lead in rough formation  When mixed units move as a group, differences in locomotion can break formation integrity ▪ Solution: all units move like the slowest unit ▪ Problem -> faster units might appear to move in slow motion

47  React to ambush  Spread out to avoid area of effect or converge to chase down an enemy  After passing obstacle or attack regroup in the same fashion that the group was originally formed  Reassign positions to make up for lost units if necessary

48  Algorithms capable of tactical assessments in a dynamic environment  Modification of influence mapping  Cells of influence are calculated from unit positions outward  Total influence: a factor which assesses bot tactical situation in the engagement area  Each “cell” superimposed on the influence map has a calculated value associated with agent and environmental qualities

49  Learning algorithms that give an NPC the ability to adapt to opponents’ playing styles in real time  Pros  Provide more challenge as opponents because they are unpredictable  Facilitate the discovery of new strategies  Cons  Developers cannot test what behaviors the NPCs will exhibit

50  Entire team of NPCs view as one chromosome  Each agent derived from a different part of the chromosome ▪ Creates heterogeneous teams  Five node sets, total of fifty nodes used in evolution

51  Fitness calculation  Takes into consideration the game’s duration and remaining health of team and enemy agents  Selection  Elitism: m copies of the best n chromosomes from each generation are copied directly into the next generation  Roulette wheel selection ▪ Any chromosomes selected are subject to randomness

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