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=e2YYt

SJhmJg 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

Steering behaviors ftw :?

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

GraphNode

Vector3 positionGraphEdge[4] edges

Data Structures

GraphEdge

GraphNode fromNodeGraphFrom 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?

New position

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

GraphNode

int indexVector3 positionGraphEdge[4] mEdges

Data Structures for Indexing

GraphEdge

int fromNodeIndexint toNodeIndexfloat weight

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

Take a look at that graph

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

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

Manhattan Distance

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

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)