General forward model

Reasearch Context

Artificial intelligence (AI) algorithms such as deep learning has been used successfully in many fields such as Computer Vision, Natural Language Processing and Decision making systems.
AI has been particularly successful in learning how to play games such as the ancient chinese game of Go, Atari games such as Breakout and Pong, and more recently complex games such as Starcraft and DOTA2.

Many successful algorithms require an ability to effectively see-forward in time, and plan next moves based on the outcome of these internal experiments.
This usually requires access to a perfect underlying model of the sytem, or a very accurate hand-crafted replica of the underlying system. There have been several attempts recently to actually build models to predict
the possible future outcomes and plan accordingly without access to the underlying model.

Aims and Objectives

The aim of this research is to create an efficient and genric way for simulated environments to be accurately predicted. There are several questions that will be answered by this research:
Can predictive models of the simulated environment improve the results of training?
Are accurate models of the environment necessary to train AI algorithms to perform complex tasks? Or can models that effectively "filter" unecessary information be used?
In games with several players which amy or may not have predictable actions, can predictive models be used to create better adversaries?
Can predictive models of simulated environments be used to understand risk?

Potential Applications and benefits

Using games as a test-bed for research has advantages in safety, cost and control of time. For example, when training self-driving cars to avoid collisions. In a simulated environment,
the cars can make mistakes without endangering the public; they can exist entirely in software and no physical components need to be maintained or replaced; simulations can be 'sped up'
meaning that several hours of training time can be compressed into minutes.

If there are ways to predict outcomes in real-world environments, then the applications of this research are widespread. Being able to accurately predict the outcomes of actions several seconds
into the future would provide a large advantage for safetly-critical applications. Additionally in the context of environments with hard to predict parameters,
AI algorithms that can plan ahead for best and worse case scenarios would be able to understand underlying risk in it's actions.