Ai part 1

INTRO TO INTELLIGENT AND
AUTONOMOUS AGENTS

Steering behaviors and pathfinding

Game AI

 What we’re covering today
 And what we’re not
 How AI can improve your games
 Autonomous agents

Steering Behaviors

 What they are
 When they’re used
 Quite often
 http://www.youtube.com/watch?v=e2YYtSJhmJg
 Movies
 Let’s try some out!

Seek

 Move entity from current position to target
position as quickly as possible

Our entity is the triangle. How do we get to the
target?

Desired velocity = target.position - myPosition

You can get a vector pointing from A to B by B - A

Don’t worry about
overshooting, it’ll be
corrected

Other steering behaviors

 Arrive
 Obstacle Avoidance

 Make sure we don’t have a force that’s too
big!

Using SBs in your Entities

 ChasePlayer State
 Obstacle avoidance, Pursue or Seek
 Idle State
 Wander, maybe flocking?

 Sheep
 Flocking, Wander, Obstacle Avoidance, Evade

Wouldn’t this be lovely? Well then, let’s find a path

A graph of points! (navigation graph)

Building a Nav Graph

 Goal: A list of nodes and edges
 What is needed:
 Start point, cast distance, interval
 (optional)Max number of nodes

Data Structures

GraphNode GraphEdge

Vector3 position GraphNode fromNode
GraphEdge[4] edges GraphFrom toNode
(Float weight)

List<GraphNodes> Nodes

For ( i = 1; i <= xCastDistance; ++i)
For (j = 1; j <= zCastDistance; ++j)
{
cast a ray downward from above for each potential neighbor node
(i.e. (curX + invertal, curZ), (curX, curZ + interval), etc.)
if raycast hit the ground
{
new GraphNode(GraphNode(Raycast hit point))
if we haven’t already found this node
{
Add new graph node to Nodes
}

(calculate edge cost)
new GraphEdge(current node, new graph node, cost))
Add new graph edge to the current node’s Edge list
}

Start from initial position, start casting out!

What happens when node cast hits out of bounds?

Nothing new, just adding another node/edge

:o A box!
Hit an object not tagged ground, skip it, keep moving

:OOOOO But we already processed that node!
Result depends on algorithm implementation. Easy route (the way the pseudo code was
setup) is to have each node responsible for itself. So in our case add the edge and ignore
the node.

Improvements Worth Mentioning

 Reducing node density
 Indexing GraphNodes and GraphEdges

Data Structures for Indexing

GraphNode GraphEdge

int index int fromNodeIndex
Vector3 position int toNodeIndex
GraphEdge[4] mEdges float weight

Instead of storing the GraphNodes that a GraphEdge is connected to, we
store the index of each GraphNode, so we’re not storing a GraphNode twice

Search Algorithms

 Depth-first search
 Breadth-first search
 Dijkstra’s algorithm
 A* (aka Dijkstra++)
 ^ yahtzee

Dijkstra’s Algorithm

 Usable with weighted graphs. Graphs without
weight can be processed by assuming edges
of equal weight
 Guaranteed to find shortest path, if it exists.
 Implementation
 Examine nodes on our search frontier, find the
one with the smallest total weight, and add it to
our path and keep going

Picture from “Programming Game AI by Example” by Mat Buckland

A*

 Dijkstra with an additional weight factor
(heuristic)
 Heuristic: Making a decision based on some knowledge

 There’s something troublesome in the Dijsktra
implementation that seems like it would be an
easy fix

We know where our target is, so let’s
approximate the cost from the nodes we are
looking at to the target and factor that
into what we choose
In this case, Euclidean distance

Results
Picture from “Programming Game AI by Example” by Mat Buckland

Review

 Steering behaviors
 Navigation graphs
 Search algorithms

 So how do they work together?
 You have a point on the graph you want to reach.
 You find a sequence of nodes to follow.
 Use steering behaviors to get from point to
point, and create interesting behavior on the way

Next time…

 Organizing these lower level decisions into
functional, autonomous agents (in a
smart, extendable, debug-friendly way )

 Questions?

(can someone let Jordan know he can wake up now? kthx)

Ai part 1

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Ai part 1

Editor's Notes