Overcoming Limited Pathfinding And Ai For Characters And Vehicles

Simplifying Complex Behaviors

The core problem plaguing video game developers is the predictable, unrealistic movements of non-player characters (NPCs) and vehicles resulting from deficient artificial intelligence (AI) systems and pathfinding algorithms. The NPCs seem to move along predefined pathways, colliding with obstacles and lacking awareness of their surroundings. Vehicles traverse landscapes devoid of intelligent route planning, often driving through buildings or getting stuck on impassable terrain. The immersion breaks when behaviors fail to match real-world expectations.

The foundation for overcoming these limitations lies in implementing robust steering behaviors and flocking rules. Steering behaviors refer to the continuous calculations determining movement direction and speed to achieve goals like obstacle avoidance, path following, fleeing and pursuit. Flocking simulates decentralized coordination between entities, allowing groups to demonstrate swarm intelligence by maintaining cohesion, alignment and separation.

Optimizing Navigation Meshes

Pathfinding efficiency improves dramatically by constraining movement to navigation meshes (navmeshes) – specially crafted interconnected polygons defining traversable game surfaces. Navmeshes prune vast environmental datasets down to manageable waypoint graphs better suited for rapid analysis. Optimal paths connect waypoints while avoiding collision with world boundaries or obstacles.

The following C++ code generates a navmesh from a 3D landscape mesh:

NavmeshGenerator::NavmeshGenerator() {

  // Set properties for navmesh generation
  
  maxEdgeLength = 4.0f;
  maxPolyArea = 25.0f;
  maxNavmeshVertices = 256;

  // Initialize navmesh data structures
  
  navmesh = new Navmesh();
  polygons = new Vector();
  openList = new Vector();

}

void NavmeshGenerator::generateFromLandscape(Landscape landscape) {

  // Step 1: Sample landscape vertices
  
  Vector3[] vertices = landscape.getVertexPositions();
  int numVertices = vertices.length;
  
  for(int i = 0; i < numVertices; ++i) {
    landscapeVertices.add(vertices[i]);  
  }

  // Step 2: Generate initial polygon from vertices
  
  Polygon firstPolygon = new Polygon(landscapeVertices);
  openList.add(firstPolygon);
  
  ...
  
}

Upgrading Decision Making

More adaptive NPC behavior arises from replacing simple finite state machines with modular logic defined through behavior trees or utility-based reasoning. Behavior trees build behaviors from tree-connected nodes representing logic tokens like sequences, selects, parallels and decorators. Utility AI assigns numeric rewards to possible actions, selecting those with maximum utility.

The following behavior tree controls a forest guard NPC, directing attention and patrols according to detected events:

BehaviorTree guardBehaviorTree {

  SelectorNode rootNode {

    SequenceNode investigateNoise {
       MoveTo nodeNoiseLocation
       Wait 5 seconds
       ReturnToPatrol
    }

    SequenceNode confrontTrespasser {
      MoveTo nodeTrespasserLocation  
      InitiateDialog "Halt! This forest is protected"
      WaitForInput
      TakeActionBasedOnInput
    }

    PatrolForestArea node {
      SetPatrolWaypoints //list of waypoints
      WalkBetweenWaypoints
    }

  }

}

Dynamic event data feeds into the tree, triggering appropriate responses - investigating noises, confronting trespassers, or default patrolling.

Integrating Neural Networks

For learning behaviors, neural networks provide state-of-the-art machine learning capabilities. Neural nets mimic biological brains, passing sensory inputs through interconnected nodes weighted by previous experiences. Deep reinforcement learning (DRL) repeatedly tunes these networks through trial-and-error simulation, rewarding desired behaviors.

This Python code demonstrates a basic DRL network controlling a humanoid character:

import torch
import gym 

class ActorCriticNetwork(torch.nn.Module):

  def __init__(self, input_dim, output_dim):

    super().__init__()

    self.network = torch.nn.Sequential(
      torch.nn.Linear(input_dim, 128),
      torch.nn.ReLU(),
      torch.nn.Linear(128, 256),
      torch.nn.ReLU(), 
      torch.nn.Linear(256, output_dim)
    )

  def forward(self, state):
    return self.network(state)

env = gym.make("Humanoid-v3") 

input_dim = env.observation_space.shape[0]
output_dim = env.action_space.shape[0]

model = ActorCriticNetwork(input_dim, output_dim)

...

This network takes body pose and velocity data as input, feeds forward through fully connected layers, and outputs torque commands to the character's joints, learning to walk from repeated simulation.

Putting It All Together

Robust AI requires the seamless integration of steering, pathfinding, decision making and learning algorithms. Steering behaviors and flocking handle local movement, navigation meshes and graphs facilitate planning, behavior trees or utility reasoning determine actions, and neural networks adapt to new scenarios.

For optimal realism, continuously tune parameter weights and topology across components. Allocate sufficient computing budget for expensive operations like graph searches and neural net inferences. Ensure interfaces between modules transfer appropriate data - steering requests to pathfinding, behavior triggers to decision making, sensory inputs to learning. Establish universally accessible data repositories for entities like spatial coordinates, animation states, velocities.

With careful architectural design, even mobile platforms can support sophisticated AI yielding dramatic improvements over conventional approaches.