CSCE 552 Fall 2012

AI

By Jijun Tang

Homework 3

List of AI techniques in games you have played;

Select one game and discuss how AI enhances its game play or how its AI can be improved

Due Nov 28th

Command Hierarchy

Strategy for dealing with decisions at different levels From the general down to the foot soldier

Modeled after military hierarchies General directs high-level strategy Foot soldier concentrates on combat

Dead Reckoning

Method for predicting object’s future position based on current position, velocity and acceleration

Works well since movement is generally close to a straight line over short time periods

Can also give guidance to how far object could have moved

Example: shooting game to estimate the leading distance

Emergent Behavior

Behavior that wasn’t explicitly programmed

Emerges from the interaction of simpler behaviors or rules Rules: seek food, avoid walls Can result in unanticipated individual or

group behavior

Flocking/Formation

Mapping Example

Level-of-Detail AI

Optimization technique like graphical LOD Only perform AI computations if player will

notice For example

Only compute detailed paths for visible agents Off-screen agents don’t think as often

Manager Task Assignment

Manager organizes cooperation between agents Manager may be invisible in game Avoids complicated negotiation and

communication between agents Manager identifies important tasks and

assigns them to agents For example, a coach in an AI football team

Example

Amit [to Steve]: Hello, friend! Steve [nods to Bryan]: Welcome to CGDC. [Amit exits left.]

Amit.turns_towards(Steve); Amit.walks_within(3); Amit.says_to(Steve, "Hello, friend!"); Amit.waits(1); Steve.turns_towards(Bryan); Steve.walks_within(5); Steve.nods_to(Bryan); Steve.waits(1); Steve.says_to(Bryan, "Welcome to CGDC."); Amit.waits(3); Amit.face_direction(DIR_LEFT); Amit.exits();

Example

Player escapes in combat, pop Combat off, goes to search; if not find the player, pop Search off, goes to patrol, …

Example

Bayesian Networks

Performs humanlike reasoning when faced with uncertainty

Potential for modeling what an AI should know about the player Alternative to cheating

RTS Example AI can infer existence or nonexistence of

player build units

Example

Bayesian Networks

Inferring unobserved variables Parameter learning Structure learning

Blackboard Architecture

Complex problem is posted on a shared communication space Agents propose solutions Solutions scored and selected Continues until problem is solved

Alternatively, use concept to facilitate communication and cooperation

Decision Tree Learning

Constructs a decision tree based on observed measurements from game world

Best known game use: Black & White Creature would learn and form

“opinions” Learned what to eat in the world based

on feedback from the player and world

Filtered Randomness

Filters randomness so that it appears random to players over short term

Removes undesirable events Like coin coming up heads 8 times in a row

Statistical randomness is largely preserved without gross peculiarities

Example: In an FPS, opponents should randomly spawn

from different locations (and never spawn from the same location more than 2 times in a row).

Genetic Algorithms

Technique for search and optimization that uses evolutionary principles

Good at finding a solution in complex or poorly understood search spaces

Typically done offline before game ships Example:

Game may have many settings for the AI, but interaction between settings makes it hard to find an optimal combination

Flowchat

N-Gram Statistical Prediction

Technique to predict next value in a sequence

In the sequence 18181810181, it would predict 8 as being the next value

Example In street fighting game, player just did

Low Kick followed by Low Punch Predict their next move and expect it

Neural Networks

Complex non-linear functions that relate one or more inputs to an output

Must be trained with numerous examples Training is computationally expensive making

them unsuited for in-game learning Training can take place before game ships

Once fixed, extremely cheap to compute

Example

Planning

Planning is a search to find a series of actions that change the current world state into a desired world state

Increasingly desirable as game worlds become more rich and complex

Requires Good planning algorithm Good world representation Appropriate set of actions

Player Modeling

Build a profile of the player’s behavior Continuously refine during gameplay Accumulate statistics and events

Player model then used to adapt the AI Make the game easier: player is not good at

handling some weapons, then avoid Make the game harder: player is not good at

handling some weapons, exploit this weakness

Production (Expert) Systems

Formal rule-based system Database of rules Database of facts Inference engine to decide which rules trigger –

resolves conflicts between rules Example

Soar used experiment with Quake 2 bots Upwards of 800 rules for competent opponent

Reinforcement Learning

Machine learning technique Discovers solutions through trial and

error Must reward and punish at appropriate

times Can solve difficult or complex problems

like physical control problems Useful when AI’s effects are uncertain

or delayed

Reputation System

Models player’s reputation within the game world

Agents learn new facts by watching player or from gossip from other agents

Based on what an agent knows Might be friendly toward player Might be hostile toward player

Affords new gameplay opportunities “Play nice OR make sure there are no

witnesses”

Smart Terrain

Put intelligence into inanimate objects Agent asks object how to use it: how to

open the door, how to set clock, etc Agents can use objects for which they

weren’t originally programmed for Allows for expansion packs or user created

objects, like in The Sims Enlightened by Affordance Theory

Objects by their very design afford a very specific type of interaction

Speech Recognition

Players can speak into microphone to control some aspect of gameplay

Limited recognition means only simple commands possible

Problems with different accents, different genders, different ages (child vs adult)

Text-to-Speech

Turns ordinary text into synthesized speech Cheaper than hiring voice actors Quality of speech is still a problem

Not particularly natural sounding Intonation problems Algorithms not good at “voice acting”: the mouth

needs to be animated based on the text Large disc capacities make recording human

voices not that big a problem No need to resort to worse sounding solution

Weakness Modification Learning

General strategy to keep the AI from losing to the player in the same way every time

Two main steps1. Record a key gameplay state that precedes a

failure

2. Recognize that state in the future and change something about the AI behavior AI might not win more often or act more intelligently,

but won’t lose in the same way every time Keeps “history from repeating itself”

Artificial Intelligence: Pathfinding

PathPlannerApp Demo

Representing the Search Space

Agents need to know where they can move Search space should represent either

Clear routes that can be traversed Or the entire walkable surface

Search space typically doesn’t represent: Small obstacles or moving objects

Most common search space representations: Grids Waypoint graphs Navigation meshes

Grids

2D grids – intuitive world representation Works well for many games including

some 3D games such as Warcraft III Each cell is flagged

Passable or impassable Each object in the world can occupy

one or more cells

Characteristics of Grids

Fast look-up Easy access to neighboring cells Complete representation of the level

Waypoint Graph

A waypoint graph specifies lines/routes that are “safe” for traversing

Each line (or link) connects exactly two waypoints

Characteristicsof Waypoint Graphs

Waypoint node can be connected to any number of other waypoint nodes

Waypoint graph can easily represent arbitrary 3D levels

Can incorporate auxiliary information Such as ladders and jump pads Radius of the path

Navigation Meshes

Combination of grids and waypoint graphs Every node of a navigation mesh represents

a convex polygon (or area) As opposed to a single position in a waypoint

node Advantage of convex polygon

Any two points inside can be connected without crossing an edge of the polygon

Navigation mesh can be thought of as a walkable surface

Navigation Meshes (continued)

Computational Geometry

CGAL (Computational Geometry Algorithm Library)

Find the closest phone Find the route from point A to B Convex hull

Example—No Rotation

Space Split

Resulted Path

Improvement

Example 2—With Rotation

Example 3—Visibility Graph

Random Trace

Simple algorithm Agent moves towards goal If goal reached, then done If obstacle

Trace around the obstacle clockwise or counter-clockwise (pick randomly) until free path towards goal

Repeat procedure until goal reached

Random Trace (continued)

How will Random Trace do on the following maps?

Random Trace Characteristics

Not a complete algorithm Found paths are unlikely to be optimal Consumes very little memory

A* Pathfinding

Directed search algorithm used for finding an optimal path through the game world

Used knowledge about the destination to direct the search

A* is regarded as the best Guaranteed to find a path if one exists Will find the optimal path Very efficient and fast

Understanding A*

To understand A* First understand Breadth-First, Best-First,

and Dijkstra algorithms These algorithms use nodes to

represent candidate paths

Class Definition

class PlannerNode

{

public:

PlannerNode *m_pParent;

int m_cellX, m_cellY;

...

};

The m_pParent member is used to chain nodes sequentially together to represent a path

Data Structures

All of the following algorithms use two lists The open list The closed list

Open list keeps track of promising nodes When a node is examined from open list

Taken off open list and checked to see whether it has reached the goal

If it has not reached the goal Used to create additional nodes Then placed on the closed list

Overall Structure of the Algorithms

1. Create start point node – push onto open list2. While open list is not empty

A. Pop node from open list (call it currentNode)B. If currentNode corresponds to goal, break from

step 2C. Create new nodes (successors nodes) for cells around currentNode and push them onto open listD. Put currentNode onto closed list

Breadth-First

Finds a path from the start to the goal by examining the search space ply-by-ply

Breadth-First Characteristics

Exhaustive search Systematic, but not clever

Consumes substantial amount of CPU and memory

Guarantees to find paths that have fewest number of nodes in them Not necessarily the shortest distance!

Complete algorithm

Best-First

Uses problem specific knowledge to speed up the search process

Head straight for the goal Computes the distance of every node

to the goal Uses the distance (or heuristic cost) as a

priority value to determine the next node that should be brought out of the open list

Best-First (continued)

Best-First (continued)

Situation where Best-First finds a suboptimal path

Best-First Characteristics

Heuristic search Uses fewer resources than Breadth-

First Tends to find good paths

No guarantee to find most optimal path Complete algorithm

Dijkstra

Disregards distance to goal Keeps track of the cost of every path No guessing

Computes accumulated cost paid to reach a node from the start Uses the cost (called the given cost) as a

priority value to determine the next node that should be brought out of the open list

Dijkstra Characteristics

Exhaustive search At least as resource intensive as

Breadth-First Always finds the most optimal path Complete algorithm

Example

A*

Uses both heuristic cost and given cost to order the open list

Final Cost = Given Cost + (Heuristic Cost * Heuristic Weight)

A* Characteristics

Heuristic search On average, uses fewer resources than

Dijkstra and Breadth-First Admissible heuristic guarantees it will find

the most optimal path Complete algorithm

Example

Start Node and Costs

F=G+H

First Move

Second Move

Cost Map

Path

Pathfinding with Constraints

More Example