Adaptive AI Engine for RTS GamesIntroduction to the Project

AbdelRahman Al Ogail

Omar Khaled Enayet

Under the Supervision Of : Dr. Ibrahim Fathy Moawad

Agenda

What’s Game AI? Why AI Engine? Structure of AI Engine What are RTS Games? Elements That Need AI in RTS Games Areas That Need Improvement in RTS

AI Common Used Techniques in AI

Engine So why working on that project

(what’s new)?

Agenda

Why is AI Development slow in RTS Games. AI Areas needing more research in RTS

Games. Latest Research

Introduction. Research Papers and Theses.▪ Introduction▪ The Papers : Intro▪ Case-Based Planning.▪ Reinforcement Learning.▪ Genetic Algorithms.▪ Hybrid Approaches.▪ Opponent Modeling Approaches.▪ Misc. Approaches.

What’s Game AI?

Let the computer think

Goal of Game AI: Entertainment NOT perfection How that guy finds the right answer?

Deeper Blue Example

Why AI Engine

AIEngineAI

Engine

That’s our guy

Structure of AI Engine

What are RTS Games ?

Real-Time-Strategy (RTS) games can be viewed as simplified military simulations. Several players struggle over resources scattered over a terrain by setting up an economy, building armies, and guiding them into battle in real-time.

The current AI performance in commercial RTS games is poor by human standards.

They are characterized by enormous state spaces, large decision spaces, and asynchronous interactions.

RTS games also require reasoning at several levels of granularity, production-economic facility (usually expressed as resource management and technological development) and tactical skills necessary for combat confrontation.

Elements That Need AI in RTS Games

Workers (peons, gatherers)

Individual units (soldiers, tanks…)

Town building: how to build my town to max. benefits

Pathfinding What’s the best (not shortest) way to get

from A to B

Elements That Need AI in RTS Games

Low level strategies Which pathfinding algorithm I should

use?

Medium level strategies How to achieve high level strategies?

High level strategies What are my goals?

Elements That Need AI in RTS Games

Terrain Analysis (keep track of your enemy)

Opponent Modeling (know your enemy)

Resource Management (take the control)

Diplomacy Systems (always have allies)

Areas That Need Improvement in RTS AI

Determine when AI element is stuck

Opponent modeling

More strategies less tactics Construct consistent army (solders,

tanks, planes) Think about support lines How to retreat Setup and detect ambushes

Areas That Need Improvement in RTS AI

Learning Some areas of learning:▪ AI opponent get in the same trap repeatedly▪ Know safe map locations and get away from

kill zones▪ Know how human player attacks and which

units he favors▪ Does the player rushes ?▪ Does the player rely on units that require certain

resources?▪ Does he frequently build a number of critical

structures in a poorly defensive place?▪ Are his attacks balanced? ( rock, paper, scissors

example)

Common Used Techniques

Categories of Used Techniques: Decision Making Other Techniques▪ Data-Driven Techniques▪ Perception Techniques▪ Communication Techniques

Decision Making – Finite State Machine

When to use: to represent states

Decision Making – Fuzzy State Machine

When to use: to represent several states at the same time

Decision Making – Genetic Algorithms

Used to find best solutions to a given problems

Genetic Process Rely on the idea of reproduction

Example of using: finding best optimal # of peons working

in each areas (area = building, money, wood, stone…)

Decision Making – Neural Networks

Rely on simulating human brain Used in:

Classification Opponent modeling

Decision Making – Artificial Life

ALife is about searching to find “governing principles” to the life

Newton theorem

Alife Techniques: Cellular Automata Steering Behaviors

Add the creativity to the AI opponent

Decision Making – Steering Behaviors

Simple rules that produces emergent behaviors

Boids research by Craig Reynolds

Used: To simulate real life In producing emergent

behavior Provide autonomies agents

Decision Making – Planning

Planning is, deciding upon a course of action before acting

Usage in games: Pathfinding algorithms Set plans to high level strategies in RTS Games anticipating ambushes

Some of Planning Techniques: A*, Mean & Analysis, Patch Recalculation,

Minimax

Decision Making – Production Systems

As prolog Used techniques:

Forward changing Backward changing

Decision Making – Decision Trees

Complex IF-ELSE Statements represented as tree

Usage if games: Player modeling High level strategies

Other Techniques – Data Driven AI

Scripting Systems

Using an external resource (not coded) that controls the AI opponent

Advantage Add extendibility to the game

Other Techniques - Messaging

Supplies communication between game objects

Other Techniques – LBI Systems

LBI: Location Based Information Systems

It’s a perception technique

Keeps track of the world attributes

Common techniques: Influence Maps Terrain Analysis Smart Terrain

Other Techniques – LBI Systems

Usage in games: Helps with obstacle avoidance Detecting player, resources places Danger specification (keep track of kill

zones) Discover critical points in the world (as

bridges)

What’s new?

Future of wars is going to be more robotic Sharing & validating plans

MIT asks for researches in this area (10-2008)

Alex J.C. said: “there’s no real learning and adaptation in commercial games”

Researches in this area is so active! Papers Range from 2003-2009

Why is AI Development slow in RTS Games ?

RTS game worlds feature many objects, imperfect information, micro actions, and fast-paced action. By contrast, World–class AI players mostly exist for slow– paced, turn–based, perfect information games in which the majority of moves have global consequences and planning abilities therefore can be outsmarted by mere enumeration.

Market dictated AI resource limitations. Up to now popular RTS games have been released solely by game companies who naturally are interested in maximizing their profit. Because graphics is driving games sales and companies strive for large market penetration only about 15% of the CPU time and memory is currently allocated for AI tasks. On the positive side, as graphics hardware is getting faster and memory getting cheaper, this percentage is likely to increase – provided game designers stop making RTS game worlds more realistic.

Lack of AI competition. In classic two–player games tough competition among programmers has driven AI research to unmatched heights. Currently, however, there is no such competition among real–time AI researchers in games other than computer soccer. The considerable man–power needed for designing and implementing RTS games and the reluctance of game companies to incorporate AI APIs in their products are big obstacles to AI competition in RTS games.

AI Areas needing more research Adversarial real–time planning. In fine–grained realistic

simulations, agents cannot afford to think in terms of micro actions such as “move one step North”. Instead, abstractions of the world state have to be found that allow AI programs to conduct forward searches in a manageable abstract space and to translate found solutions back into action sequences in the original state space. Because the environment is also dynamic, hostile, and smart — adversarial real–time planning approaches need to be investigated.

Decision making under uncertainty. Initially, players are not aware of the enemies’ base locations and intentions. It is necessary to gather intelligence by sending out scouts and to draw conclusions to adapt. If no data about opponent locations and actions is available yet, plausible hypotheses have to be formed and acted upon.

Opponent modeling, learning. One of the biggest shortcomings of current (RTS) game AI systems is their inability to learn quickly. Human players only need a couple of games to spot opponents’ weaknesses and to exploit them in future games. New efficient machine learning techniques have to be developed to tackle these important problems.

AI Areas needing more research (2) Spatial and temporal reasoning. Static and dynamic

terrain analysis as well as understanding temporal relations of actions is of utmost importance in RTS games — and yet, current game AI programs largely ignore these issues and fall victim to simple common–sense reasoning .

Resource management. Players start the game by gathering local resources to build up defenses and attack forces, to upgrade weaponry, and to climb up the technology tree. At any given time the players have to balance the resources they spend in each category. For instance, a player who chooses to invest too many resources into upgrades, will become prone to attacks because of an insufficient number of units. Proper resource management is therefore a vital part of any successful strategy

AI Areas needing more research (3) Collaboration. In RTS games groups of

players can join forces and intelligence. How to coordinate actions effectively by communication among the parties is a challenging research problem. For instance, in case of mixed human/AI teams, the AI player often behaves awkwardly because it does not monitor the human’s actions, cannot infer the human’s intentions, and fails to synchronize attacks.

Latest Research

Latest Research : Intro. Current Implementation of RTS Games applies

extensive usage of FSM that makes them highly predictable.

Adaptation is achieved either through Learning or planning or a mixture of both

Planning is beginning to appear in commercial games such as DemiGod and Latest Total War Game.

Learning has limited success so far. Developers are experimenting on replacing the

ordinary decision making systems (FSM, FUSM, Scripting, Decision Trees, and Markov Systems) with Learning Techniques

Latest Research : The Papers

More than 30 papers/theses talk about Planning and Learning in RTS Games

The Major 3 approaches to AI Research in RTS-GAMES concerning Learning and Planning are Case-Based Planning, Reinforcement Learning with its different techniques and Genetic Algorithms. Some Papers use a Hybrid approach of these techniques. Others use other planning algorithms like PDDL or opponent modeling techniques and other misc. techniques.

3 papers encourage the research in this field. 9 papers use Case-Based Planning Approach from 2003-2009,1

uses a Hybrid CBR/GA approach in 2008,1 uses a Hybrid CBR/RL approach in 2007

10 papers use Reinforcement Learning with its different forms (Monte-Carlo, Dynamic Scripting and TD-Learning),1 uses TD-Learning with GA,1 uses Dynamic Scripting with GA

3 Papers use Genetic Algorithms. 3 Papers apply opponent modeling techniques.

Encouraging Research : Papers RTS Games and Real–Time AI

Research – 2003 RTS Gaines A New AI Research

Challenge – 2003 Call for AI Research in RTS Games -

2004

Case-Based Planning

Case-based planning is the reuse of past successful plans in order to solve new planning problems.

It’s an application of Case-Based Reasoning in planning.

Case-Based Planning : Papers

The David Aha Research Thread : On the Role of Explanation for Hierarchical Case-Based Planning

in RTS Games - after 2004 Learning to Win - Case-Based Plan Selection in a RTS Game-

2005 Defeating Novel Opponents in a Real-Time Strategy Game –

2005 The Santiago Ontanon Research Thread :

Case-Based Planning and Execution for RTS Games – 2007 Learning from Human Demonstrations for Real-Time Case-

Based Planning – 2008 On-Line Case-Based Plan Adaptation for RTS Games- 2008 Situation Assessment for Plan Retrieval in RTS Games – 2009

Other Papers Case-based plan recognition for RTS games - after 2003 Mining Replays of RTS Games to learn player strategies – 2007

Reinforcement Learning

It is a sub-area of machine learning concerned with how an agent ought to take actions in an environment so as to maximize some notion of long-term reward.

Reinforcement learning differs from the supervised learning problem in that correct input/output pairs are never presented, nor sub-optimal actions explicitly corrected.

Further, there is a focus on on-line performance, which involves finding a balance between exploration and exploitation.

Reinforcement Learning : Papers Dynamic Scripting :

Goal-Directed Hierarchical Dynamic Scripting for RTS Games – 2006 Automatically Acquiring Domain Knowledge For Adaptive Game AI

Using Evolutionary Learning – 2008 Monte-Carlo Planning :

UCT(Monte-Carlo) for Tactical Assault Battles in Real-Time Strategy Games. – 2003

Monte Carlo Planning in RTS Games - After 2004 Temporal-Difference Learning :

Learning Unit Values in Wargus Using Temporal Differences – 2005 Establishing an Evaluation Function for RTS games - After 2005

Dynamic Scripting VS Monte-Carlo Planning: Adaptive reinforcement learning agents in RTS games – 2008

Hierarchical Reinforcement Learning Hierarchical Reinforcement Learning in Computer Games - After 2006 Hierarchical Reinforcement Learning with Deictic repr. in a computer

game- After 2006

Genetic Algorithms

Genetic algorithms are a particular class of evolutionary algorithms (EA) that use techniques inspired by evolutionary biology such as inheritance, mutation, selection, and crossover.

Genetic Algorithms : Papers

Human-like Behavior in RTS Games – 2003

Co-evolving Real-Time Strategy Game Playing Influence Map Trees with genetic algorithms

Co-Evolution in Hierarchical AI for Strategy Games - after 2004

Hybrid Approaches : Papers

Genetic Algorithms + Dynamic Scripting : Improving Adaptive Game AI With Evolutionary

Learning – 2004 Automatically Acquiring Domain Knowledge For

Adaptive Game AI using Evolutionary Learning – 2005 Genetic Algorithms + TD-Learning :

Neural Networks in RTS AI – 2001 Genetic Algorithms + Case-Based Planning :

Stochastic Plan Optimization in Real-Time Strategy Games – 2008

Case-Based Reasoning + Reinforcement Learning : Transfer Learning in Real-Time Strategy Games Using

Hybrid CBR-RL - 2007

Opponent Modeling : Papers

Hierarchical Opponent Models for Real-Time Strategy Games – 2007

Opponent modeling in real-time strategy games - after 2007

Design of Autonomous Systems - Learning Adaptive playing a RTS game - 2009

Misc. Approaches : Papers

Supervised Learning : Player Adaptive Cooperative Artificial Intelligence

for RTS Games – 2007 PDDL :

A First Look at Build-Order Optimization in RTS games - after 2006

Finite-State Machines : SORTS - A Human-Level Approach to Real-Time

Strategy AI – 2007 Others :

Real-time challenge balance in an RTS game using rtNEAT – 2008

AI Techniques in RTS Games -September 2006

The End

Thank You !

References

Books : AI Game Engine Programming -2009 Artificial Intelligence for Games – 2009

Papers: RTS Games and Real–Time AI Research –Michael Buro &

Timothy M. Furtak - 2003 Call for AI Research in RTS Games - Michael Buro – 2004

Web Resources : AIGameDev Forums. GameDev.Net Forums. Wikipedia. Others