Comparative Analysis of Computer Generated Forces' Artificial Intelligence
Abstract
Computer Generated Forces (CGFs) are a key component in constructive simulations and are being increasingly used to control multiple entities in Synthetic Environments (SEs). Being a cost-effective way to providing extra players in SEs, they are becoming a possible alternative in various activities, such as Concept, Development and Experimentation (CD&E), analysis, training, tactic development, and mission rehearsal. The predictable nature of many current CGFs behaviour is one of their biggest problems, making it easy for the trainee to distinguish between human-controlled and computer-controlled entities in the simulation environment. This can result in negative or ineffective training as the trainee quickly learns to predict the behaviour of the CGF entity and easily defeats it in a way that would not happen with a human opponent. This results in a requirement for humans to control synthetic entities, thus limiting simulation exercises by the availability of operators. If instead the Artificial Intelligence (AI) of these entities could be improved, the number of operators required will, thus, be reduced. The first step in such an effort is evaluating the AI capabilities commonly available in CGFs. Such an analysis was performed at the Defence Research & Development Canada (DRDC), revealing the common strengths and weaknesses of available CGFs, and suggesting which might be most useful as a platform for further AI research. This document presents the methods and results of this analysis.
Document Details
- Document Type
- Technical Report
- Publication Date
- Oct 01, 2009
- Accession Number
- ADA567877
Entities
People
- Adrian Taylor
- Glen Parkinson
- Nacer Abdellaoui
Organizations
- Defence Research and Development Canada