Qualitative Spatial Analysis

Chris Mansley

Qualitative Quantitative items are known, definable

quantities (x,y) coordinates Remaining health Location of units

Qualitative items are much more intangible but intimately related to qualitative items

By decoupling the two, more general code/ideas can be used about the AI

An example Trafficablity

Given a map and an echelon Restricted (Impassible) Restricted (Passable) Unrestricted

Mobility Corridors Avenues of Approach

Example (cont.)

Motivations

Game worlds are increasingly more complex

NPC must be able to reason about them in an intelligent fashion

Dynamic environments Makes scripting less of an option Leads to more problems

Two Implementations Spatial Database

Driven by the dynamic environments associated with FPS

Terrain Analysis Needed a realistic way to analyze

strategic value in RTS

Conventional Methods Conventional Spatial Ideas

NPCs are guided by “hints” placed in the level by designers

Offline analysis of level to build a data structure the NPCs can use to interact with the environment

AI Nodes

Conventional Methods (cont.) Flaws

Only works for static environments! May be difficult with random maps

Spatial Databases 2-D Grid overlaid on the game

world Each cell can have many layers of

data Can be thought of as several grids

laid on top of one another

Spatial Databases

Search layer

Occupancy layer

Openness layer

Data Layers Openness Layer (Static) Cover Layer (Static) Area Search Layer (Dynamic) Area Occupancy Layer (Dynamic) Line-of-Fire Layer (Dynamic) Light Level Layer (Dynamic)

Using Layers Layers can be combine to form

desirability layers for different scenarios

Combat Desirability Openness Occupancy Cover

Using Layers (cont.) Benefits

Implicitly coordinate multiple AI units Generate multiple movements for

different actions

Using Layers (cont.) Drawbacks

Does not handle 3-D environments easily

Simple Solution – break 3-D environment into smaller grids

Potentially processor intensive Streamline resolution of grid to get best

possible results

Calculating Desirability

Cover layer

Occupancy layer

Openness layer

1

0.6

0

Desirability layer

0

1

0.8

10.8

0.8

0.8

0.6 0.6 0.6

0.6

0.6

0.6 0.6

0 0

1

0.6

0.6

0 0

00

0 0=

Openness Layer A cell containing a wall or blocking

object contains a zero All other cells contain a one The zero is propagated to

neighboring cells to be able to determine proximity to the wall

Openness Layer (cont.)

Solid Wall

Area Occupancy Layer Similar to fog-of-war feature from

RTS Nonzero values are placed where

the unit is located That value is propagated to the

neighboring cells

Area Occupancy Layer (cont.)

Line-of-Fire Layer Useful with ranged weapons Helps AI avoid shooting each other Has a nonzero value in each cell in

the direction of fire

Line-of-Fire (cont.)

Light Level Dynamic lighting is an important

part of modern games Visibility/area lighting can be taken

into account by NPCs The layer has the light intensity at

any cell

Area Search Layer In many “sneaker” games, the NPCs

spend time searching for the player Once searched, the area layer

contains true or some nonzero value Over time that layer can decay, so

the NPC will research it if necessary

Thief: Deadly Shadows

References AI Game Programming Wisdom 2 Forbus, K., “How Qualitative Spatial Reasoning Can

Improve Strategy Game AIs”, IEEE Intelligent Systems, July/August 2002

Thief: Deadly Shadows, Ion Storm/Eidos Interactive 2004