Zobrazeno 1 - 10
of 119
pro vyhledávání: '"Gow, Jeremy"'
Exploration is a key part of many video games. We investigate the using an exploratory agent to provide feedback on the design of procedurally generated game levels, 5 engaging levels and 5 unengaging levels. We expand upon a framework introduced in
Externí odkaz:
http://arxiv.org/abs/2409.02632
Large Language Models (LLMs) have shown great success as high-level planners for zero-shot game-playing agents. However, these agents are primarily evaluated on Minecraft, where long-term planning is relatively straightforward. In contrast, agents te
Externí odkaz:
http://arxiv.org/abs/2403.00690
Action advising is a peer-to-peer knowledge exchange technique built on the teacher-student paradigm to alleviate the sample inefficiency problem in deep reinforcement learning. Recently proposed student-initiated approaches have obtained promising r
Externí odkaz:
http://arxiv.org/abs/2104.08441
Deep Reinforcement Learning (RL) techniques can benefit greatly from leveraging prior experience, which can be either self-generated or acquired from other entities. Action advising is a framework that provides a flexible way to transfer such knowled
Externí odkaz:
http://arxiv.org/abs/2104.08440
Action advising is a budget-constrained knowledge exchange mechanism between teacher-student peers that can help tackle exploration and sample inefficiency problems in deep reinforcement learning (RL). Most recently, student-initiated techniques that
Externí odkaz:
http://arxiv.org/abs/2010.00381
Deep Reinforcement Learning (RL) algorithms can solve complex sequential decision tasks successfully. However, they have a major drawback of having poor sample efficiency which can often be tackled by knowledge reuse. In Multi-Agent Reinforcement Lea
Externí odkaz:
http://arxiv.org/abs/1905.01357
Autor:
Grov, Gudmund, Ireland, Andrew, Llano, Maria Teresa, Kovacs, Peter, Colton, Simon, Gow, Jeremy
Refinement based formal methods allow the modelling of systems through incremental steps via abstraction. Discovering the right levels of abstraction, formulating correct and meaningful invariants, and analysing faulty models are some of the challeng
Externí odkaz:
http://arxiv.org/abs/1603.00636
This project aims to gather player perspectives on what is helpful or productive about learning to play or improve from videos and streams. Specifically focusing on team-based esport games. We will be sending a survey asking questions about their vie
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fd7f5e0c766d6d9e7ffb0e2debb223a1
OSF project for a qualitative enquiry into learning the team-based esports games Counter-Strike: Global Offensive and Dota 2.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e3cd4d5eaef87bf11ec69d2b38a63ade
Autor:
Gow, Jeremy, Corneli, Joseph
Publikováno v:
2nd International Workshop on Experimental AI in Games
Gow, J & Corneli, J 2015, Towards Generating Novel Games Using Conceptual Blending . in Experimental AI in Games: Papers from the AIIDE 2015 Workshop . pp. 15-21 .
Gow, J & Corneli, J 2015, Towards Generating Novel Games Using Conceptual Blending . in Experimental AI in Games: Papers from the AIIDE 2015 Workshop . pp. 15-21 .
We sketch the process of creating a novel video game by blending two video games specified in the Video Game Description Language (VGDL), following the COINVENT computational model of conceptual blending. We highlight the choices that need to be made
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ff05f8c6d03775d251efdc3cc3f3afb2
https://doi.org/10.1609/aiide.v11i3.12824
https://doi.org/10.1609/aiide.v11i3.12824
