Performance Comparison of AI Algorithms – Anytime Algorithms

Commercial computer gaming is a large growing industry that already has its major contributions in the entertainment industry of the world. One of the most important among different types of computer games are Real Time Strategy (RTS) based games. RTS games are considered being the major research subject for Artificial Intelligence (AI). But still the performance of AI in these games is poor by human standards due to some fundamental AI problems those require more research to be better solved for the RTS games. There also exist some AI algorithms those can help us solve these AI problems. Anytime- Algorithms (AA) are algorithms those can optimize their memory and time resources and are considered best for the RTS games. We believe that by making AI algorithms anytime we can optimize their behavior to better solve the AI problems. Although many anytime algorithms are available to solve various kinds of AI problems


Chapter 1: Introduction
1.1: Background
1.2: Purpose & Objectives
1.3: Research Questions
1.4: Research Methodology
1.5: Outline
Chapter 2: Theoretical Study
2.1: AI Importance and Performance in RTS Games
2.1.1: AI Importance in RTS Games
2.1.2: AI Performance in RTS Games
2.2: AI Problems and Algorithms in RTS Games
2.2.1: AI Problems in RTS Games
2.2.2: AI Algorithms in RTS Games
2.3: Anytime Algorithms
2.3.1: AA Properties
2.3.2: Making AI Algorithms, Anytime- A Possible Solution?
Chapter 3: A – Star Search (A*)
3.1: Basic Concepts about A*
3.1.1: Evaluation Function ( ) nf
3.2: Our Implementation of A*
3.2.1: Local Minima Problem
3.3: Our Implementation of Anytime A*
Chapter 4: Recursive Best First Search (RBFS)
4.1: Basic Concepts about RBFS
4.2: Our Implementation of RBFS
4.2.1: Local Minima Problem
4.3: Our Implementation of Anytime RBFS
Chapter 5: Potential Fields (PF)
5.1: Basic Concepts about PF
5.1.1: Representing Behaviors as Potential Fields
5.1.2: Combining Potential Fields
5.2: Types of Potential Fields
5.2.1: Uniform Potential Field
5.2.2: Perpendicular Potential Field
5.2.3: Tangential Potential Field
5.2.4: Random Potential Field
5.3: The Grid
5.4: Our Implementation of Potential Fields
5.4.1: Creating Potential Field for seekGoal Behavior
5.4.2: Creating Potential Field for avoidObstacle Behavior
5.4.3: Local Minima Problem
5.5: Our Implementation of Anytime PF
Chapter 6: PFPC – Our Own Build Platform
6.1: Path Finding Performance Comparison (PFPC) Platform…

Source: Blekinge Institute of Technology

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