Jumping, Climbing, and Tactical ReasoningSection 2.5

Tom Schaible

CSE 497 – AI & Game Programming

Jumping, Climbing, and Tactical Reasoning

Christopher Reed Benjamic Geisler Raven Software/Activison Soldier of Fortune II: Double Helix

Standard Navigation System Recap

Graph Theory Point – A location Edge – A route to get from location to

location Use A* to efficiently navigate through the

graph in game This can be extended to

Climbing Jumping Throwing Grenades

Finding Cover using Teams

Embed appropriate cover information in points

Teammate waits, Leader chooses cover and advances

Leader waits, Leader chooses cover and advances

Repeat

Finding Cover Using Teams

Finding Cover Using Teams

Additional Information Needed at Edges Obstacle Information Visibility Information More on this later

Throwing Grenades

Choice of scripting behavior or encoding data into decision system

Compute edges to points in grenade throwing radius If it is possible to throw a grenade (encode

trajectory) add a grenade throwing edge

Throwing Grenades

Throwing Grenades

Throwing Grenades

Things to keep in mind Is throwing a grenade feasible?

Uncommon situation, grenades are scarce Reserved more as a last resort, when obstacles

prohibit attacking What position is best to throw a grenade from?

It may be impossible to use a grenade effectively What trajectory is necessary? (through windows,

around corners) .05 to .1 seconds per trajectory to calculate Store in memory instead (store only attacker and

trajectory pair)

How to Bias Edges Data you are adding to points needs to show up in

the edgesParentPoint = OpenList.GetFromOpenList()

For each edge from ParentPoint

Switch edge.type()

Case GRENADE_EDGE:

If ( point.trajectoryGood() )

edge.cost = actor.GrenadeBiasCost()

Case FLY_EDGE:

If ( actor.CanFly() )

edge.cost = actor.FlyBiasCost()

Non-Standard Movement Edges

Instead of scripting behavior, embed type of behavior in edge

Engine will automatically perform movement including animation and set up

Can be used for Jumping Flying Opening Doors

How to Embed Information in Points

Store point size information for each point Use a sphere to define the size

Can Detect obstacles and obstacle features Obstacle Visibility – Vertical Obstacle Visibility – Side to Side

Obstacle Visibility - Point

Object Visibility – Side to Side

Other Embedded Information

Safe Size Use collision detection to determine how big of an actor can

use the path Jumping

Embed invisible geometry in game (preferred) or use extensive collision detection

Vaulting Same as jumping but geometry is not invisible Requires specific constraints for animation

Movable Objects and Doors Collision detection will find tagged object Retry collision detection after object is moved to test validity of

edge (store movement info in the edge) Stairs, Slopes, Ladders, and Ropes

Checking angle between points vertically can cause edges to be labeled with certain actions

Putting it all together

Flying Edge

Flying Edge

Door EdgeVault Edge

Jump Edge

Rappelling Edge

Soldier of Fortune II

Hunting Down the Player in a Convincing Manner

Section 2.6

Hunting Down the Player in a Convincing Manner

Alex McLean Pivotal Game Ltd. Conflict : Desert Storm

Hunting Down a Player in a Convincing Manner

Intelligent hunting is a good thing The game needs to be somewhat

challenging Allows for strategy

Perfect hunting is a bad thing Takes away from gameplay Really isn’t realistic

It should be apparent that the hunter is exploring and cannot see the target when it is not visible

Parameterizing the Process

A parameter based process will work best

Controls the hunter to do different thigns at different times

Two major parameters we want control over Speed – How quickly you are found Directness – How direct is the path from

hunter to target

Destination

Path finding will be used to direct the hunter

A destination is needed Simplest solution is to move directly to the

destination where the hunter is This makes the AI too good Destination must be more variable and

robust

States

Break the decision of destination up into different states The player is visible The player was recently seen The player has never been seen

Allows for more realistic destinations Results in more realistic behaviors

The Player is Visible

Simplest Case Start the attacking behavior

May be a direct route May look for cover

The Player Was Recently Seen

Another simple case Move to the last point where the

character was seen Provides for much more interesting

game play to the player

The Player Was Recently Seen

The Player Was Not Recently Seen

Must start searching “intelligently” Must generate a location

Direction – within an angle θ of the player Distance – Some multiple of the exact direction to

the target in an interval [Smin,Smax] (i.e. [.5,1.5])

Smin

Smax

θ

x

Algorithm Detail

Smin, Smax, and θ control the parameters of hunting

Closeness of Smin, Smax to 1.0 and decreasing θ Increase Speed Increase Directness

Making θ > 180 can make the NPC somewhat dumb

How it Works

Extensions

Three-Dimensions Simply use a 3-D cone to pick destination

Moving Players Need to update player destination along the way In case NPC and player run into each other Make sure you are moving towards the player and not to

where the player was Sounds

Hearing sounds can act as a new location that the player has been “seen”

Target Priority If a more interesting target is seen, go after it

Team based Tactics

Team Based Tactics

Conflict: Dessert Storm

Conclusion

A* and similar path finding algorithms can be used to provide dynamic motion Edges in graphs can be used to encode motion-

specific information Edges can be biased to modify the search

algorithm

Realistic hunting is an important part of an AI The hunting AI should react intelligently to a player It should be smart but not too smart