Learn about the AI research shaping the future of video games

For decades, video games have immersed us in extraordinary environments and experiences, provided challenges for us to overcome, and captivated us with their unique mixture of art and entertainment.

Video games is also a domain that’s long been explored by AI researchers. With well-defined parameters and success metrics, games provide a testbed for AI development, and they are also a key application area for the technology. Working both within the sector and outside it, Amii researchers have advanced the state-of-the-art in games with implications across all aspects of design, development and delivery.

In this series of short talks, you'll hear from Fellows and Canada CIFAR AI Chairs at Amii as they discuss their areas of research and how each relates to the field of video games. Can computers help humans make more interesting puzzles? Will intelligent tutoring agents replace game tutorials? Can AI models assist level designers and still keep the designer’s creative style? Dive in now:

Computational Creativity with Matthew Guzdial

Fellow & Canada CIFAR AI Chair – Amii; Assistant Professor – University of Alberta

Matthew Guzdial discusses computational creativity and how AI and machine learning can be used to help human designers with creative tasks. He walks us through the level editor created by his team, which includes an active learning AI assistant that can support designers and help creative workers make better work, faster.

Learn More about Matthew's work.

Human in the Loop AI with Matthew E. Taylor

Fellow-in-residence & Canada CIFAR AI Chair – Amii; Associate Professor – University of Alberta

Matthew (Matt) E. Taylor discusses an area of AI called reinforcement learning, the next big wave in AI application. He goes into the importance of creating AI systems that keep humans in the loop and how reinforcement learning can help enable greater personalization and customization in AI-based products and services.

Learn more about Matt's work.

Planning, Pathfinding and Puzzles with Nathan Sturtevant

Fellow & Canada CIFAR AI Chair – Amii; Professor – University of Alberta

An expert in heuristic search, Nathan Sturtevant discusses the use of planning algorithms for in-game pathfinding. He also discusses how AI search techniques can be used to improve the development and design of puzzles and for generating new or improved puzzles based on previously-designed examples.

Learn more about Nathan's work.

Optimization in Reinforcement Learning with Csaba Szepesvári

Fellow & Canada CIFAR AI Chair – Amii; Professor – University of Alberta

Csaba Szepesvári talks reinforcement learning, an area of AI research and application that is rapidly growing in popularity. He highlights some of the areas where open problems still exist in the field and his work on developing algorithms that make efficient use of simulators in order to come up with good policies that can be executed in the real world.

Learn more about Csaba's work.

Computational Linguistics and Artificial Languages with Greg Kondrak

Fellow – Amii; Professor – University of Alberta

Greg Kondrak, a leading expert in natural language processing NLP), discusses his work in computational linguistics and enabling computer systems to better understand and process language. His work has important applications in machine translation, computational decipherment and in using NLP to create artificial languages that more accurately mimic the patterns of natural human language.

Learn more about Greg's work.

Dealing with Limited Rationality with James R. Wright

Fellow & Canada CIFAR AI Chair – Amii; Assistant Professor – University of Alberta

James R. Wright talks about his research interests in behavioural game theory and his specific focus on modelling multi-agent systems in which some or all of the agents are people. James is interested in understanding and predicting human behaviour – which is often not optimal or even fully rational – and applying these insights to better understand the actions and preferences of individuals.

Learn more about James' work.

Machine Learning for Optimization and Game Theory with Kevin Leyton-Brown

Canada CIFAR AI Chair – Amii; Professor – University of British Columbia

Kevin Leyton–Brown uses machine learning to think about game theory – or human strategic behaviour. In this talk, Kevin discusses potential applications of his area of interest such as multiplayer modelling to anticipate interactions and helping to inform changes in monetization strategies. He also highlights the importance of algorithmic optimization with applicability to market design.

Learn more about Kevin's work.

Computers Writing Programs with Levi Lelis

Fellow & Canada CIFAR AI Chair – Amii; Assistant Professor – University of Alberta

Levi Lelis focuses his research on developing intelligent systems that are able to augment human knowledge through teaching and collaboration. In this talk, he discusses program synthesis – the task of having a computer program write programs for us – and his work that seeks to unpack insights hidden within ‘black box’ systems such as a deep neural network. Levi works to extract strategies from information learned by computer systems.

Learn more about Levi's work.

Textual Understanding and Generation with Lili Mou

Fellow & Canada CIFAR AI Chair – Amii; Assistant Professor – University of Alberta

Lili Mou specializes in natural language processing (NLP) for text understanding and generation. Lili’s work focuses on creating systems that can better understand, interpret and use human language and has applications in paraphrasing, translation and human-machine interaction. He also works on unsupervised text generation – in other words, training a system to generate reasonable-sounding text, even without training data.

Learn more about Lili's work.

Social Network Analysis with Osmar Zaïane

Fellow & Canada CIFAR AI Chair – Amii; Professor – University of Alberta

Osmar Zaïane uses machine learning and data mining for pattern discovery and information extraction – focusing on problems that have direct real-world applications. In this short talk, he highlights his work on social network analysis, which helps to map and understand the relationship between entities interacting in a system. His work has important implications in discovering communities and the changes that occur over time through the interaction of individuals and communities.

Learn more about Osmar's work.

Learn how Amii advances world-leading artificial intelligence and machine learning research: visit our Research page.

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