FPS Bot Implementation

8/7/2019 FPS Bot Implementation

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K a r a n v e e r S i n g h

H a r s h a l P a t i l

FPS Bot Implementation


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AIM

To implement intelligence in a FPS Bot, andenable it to act as humanly as possible.

The field will consist of the bot and a humanplayer controlled character. The bot will try itsbest to win.

Project Extension: to include coordinationbetween a group of bots for common or variedtasks.


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TECHNIQUES

FSMs

Autonomous agent

Neural NetworksGenetic Algorithms


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Functional & Structural Layers


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FSMs

FSMs receive input, and react by either changing thestate or performing some function, or both.

E.g. : Light bulb (2 states : ON or OFF)

Each Bot to be implemented using FSM.

Examples of states : Attack, Pursuit, Find Health,Hide and Evade.


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FPS bot FSM

EVADE

Movement = run

Direction = opp. Player

If Player health is low

ATTACK

Movement = take attackposn .

Direction = to player

If player evadesIf my health low

PURSUIT

Movement = runDirection = to player

If Player position notvisible

If Player position is near

HIDE

Movement = take

coverDirection = opp.player

If Player position isfar

If Player health is low


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WHY FSMs?

Quick and simple to code

Easy to debug as agent behaviour is broken down intomanageable chunks

Little computational overheadIntuitive Logic

Flexible to modify code and extend abilities

Hierarchical FSMs: introduce reusability & increasesperformance; decreases transitions to be specified.


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HOW TO IMPLEMENT FSMs?

Substandard approaches:

Use a series of if-else statements

Each state dependent on other states

State Transition Table

Embedding rules within the states

Improves flexibility

Each state is not dependent on the other states

To modify a states behaviour, no need to change other states


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PSEUDO CODE FOR AGENT

In the main function, we have an infiniteloop :

Agent a

Loop()

A.update()

Class Agent

State CurrentState //State is anotherclass

Update() CurrentState.Execute()


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MESSAGING

To coordinate between multiple bots

Bots can ask and give info to each other

Will make possible group learning : what 1 bot learns,each bot learns


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An AUTONOMOUS AGENT

Autonomous Agent : Being able to sense the environmentand act on its own to achieve a goal.

3 Layers:

Action Selection (Goal selection & Plan making) Steering (Waypoint selection & Behaviours)

Locomotion (the How)

Examples of Behaviours: Seek, Flee, Pursuit, Evade, Wander

Group Behaviours : Flocking, Alignment, etc


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PATHFINDING

Decompose the world into Navigation Meshes.

Thus, our world becomes like a graph. If there areno obstacles between 2 nodes, then join themusing an edge. Allocate the weight acc. To storedtables (large cost for sand, explosive areas, etc)

Now, use a graph search algorithm (like A*) tofind the optimum path to go from A to B.Heuristics : Manhattan Distance.


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PATHFINDING


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NEURAL NETWORKS

Relegate key decision making process to a trained neuralnetwork , thus simplifying a complex state machine

Used as a function approximator

NNs not to be used for everything, since they can beunpredictable.

Example : Decision to engage in combat or not; usingInputs like bots health, enemys health, distance from

enemy, skill level of enemy.Use a 3 layer, feed-forward network (one input, one hidden,one output)

Back propagation Algorithm.


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GENETIC ALGORITHMS

involve a trial-and-error approach

populate the game world with many possible solutions and then determinewhich solutions work the best

Solutions will be different for different players

Looks at a class of player. For it, we need to figure out how to attack it.Attack it using method A, then decide its fitness according to the damageinflicted on the player.

Code the attack in a chromosome, and find the optimum chromosome

Decode the chromosome and get the attack

Attack that class of player using that particular attack strategy.

Libraries : GaLib, GAUL


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PROBABILITY

Use Hit Probabilities according to skill level of the player

In making decisions in Finite State Machines.

Find probability that you can kill the enemy class (duringthe game), then decide according to it. (Adaptability)


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Functional & Structural Layers


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BAYESIAN NETWORS

Give NPCs the ability to predict the next strike that is,to anticipate the next strike at a success rate more thantwice what otherwise can be achieved by just guessing.


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TOOLS

Use a game engine to abstract away graphics, sound,etc and focus on the logic (AI)

Unity game engine (Free for personal use)

Download agent models, and script their behaviour.Will use C# & Boo(a dialect of Python) for scripting.

OR can directly use ray casting for pseudo-3D graphics

OR top-down view, to decrease agent modeling work


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Karanveer Harshal

HFSMs

Path FindingGame Engine

Genetic Algo

NNs

HFSMs

Game EngineAutonomous agent

NNs

Ray Casting

TENTATIVE WORK DIVISION


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TENTATIVE SCHEDULE

12th sept - Get familiar with the engine.

& starting to code the bot(s) and State behaviours

3rd oct - Implement Autonomity

4th oct tentative 2nd presentation

3rd nov - Decisions using Neural Networks

~3rd nov - 3rd Presentation

12th nov - Report Submission


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THANK YOU

Documents

FPS Bot Implementation