Zobrazeno 1 - 10
of 111
pro vyhledávání: '"Lucas, Simon M."'
Autor:
Ovalle, Alvaro, Lucas, Simon M.
A large part of the interest in model-based reinforcement learning derives from the potential utility to acquire a forward model capable of strategic long term decision making. Assuming that an agent succeeds in learning a useful predictive model, it
Externí odkaz:
http://arxiv.org/abs/2106.13911
Autor:
Lucas, Simon M.
This demo paper describes a simple and practical approach to writing cross-platform casual games using the Kotlin programming language. A key aim is to make it much easier for researchers to demonstrate their AI playing a range of games. Pure Kotlin
Externí odkaz:
http://arxiv.org/abs/2008.04446
Autor:
Ovalle, Alvaro, Lucas, Simon M.
We revisit the role of instrumental value as a driver of adaptive behavior. In active inference, instrumental or extrinsic value is quantified by the information-theoretic surprisal of a set of observations measuring the extent to which those observa
Externí odkaz:
http://arxiv.org/abs/2007.09297
The General Video Game Artificial Intelligence (GVGAI) competition has been running for several years with various tracks. This paper focuses on the challenge of the GVGAI learning track in which 3 games are selected and 2 levels are given for traini
Externí odkaz:
http://arxiv.org/abs/2005.11247
Autor:
Ovalle, Alvaro, Lucas, Simon M.
Having access to a forward model enables the use of planning algorithms such as Monte Carlo Tree Search and Rolling Horizon Evolution. Where a model is unavailable, a natural aim is to learn a model that reflects accurately the dynamics of the enviro
Externí odkaz:
http://arxiv.org/abs/2004.07155
The Fighting Game AI Competition (FTGAIC) provides a challenging benchmark for 2-player video game AI. The challenge arises from the large action space, diverse styles of characters and abilities, and the real-time nature of the game. In this paper,
Externí odkaz:
http://arxiv.org/abs/2003.13949
Game-playing Evolutionary Algorithms, specifically Rolling Horizon Evolutionary Algorithms, have recently managed to beat the state of the art in win rate across many video games. However, the best results in a game are highly dependent on the specif
Externí odkaz:
http://arxiv.org/abs/2003.12331
Autor:
Dockhorn, Alexander, Lucas, Simon M., Volz, Vanessa, Bravi, Ivan, Gaina, Raluca D., Perez-Liebana, Diego
This paper examines learning approaches for forward models based on local cell transition functions. We provide a formal definition of local forward models for which we propose two basic learning approaches. Our analysis is based on the game Sokoban,
Externí odkaz:
http://arxiv.org/abs/1909.00442
The space of Artificial Intelligence entities is dominated by conversational bots. Some of them fit in our pockets and we take them everywhere we go, or allow them to be a part of human homes. Siri, Alexa, they are recognised as present in our world.
Externí odkaz:
http://arxiv.org/abs/1906.04023
Autor:
Browne, Cameron, Soemers, Dennis J. N. J., Piette, Éric, Stephenson, Matthew, Conrad, Michael, Crist, Walter, Depaulis, Thierry, Duggan, Eddie, Horn, Fred, Kelk, Steven, Lucas, Simon M., Neto, João Pedro, Parlett, David, Saffidine, Abdallah, Schädler, Ulrich, Silva, Jorge Nuno, de Voogt, Alex, Winands, Mark H. M.
Digital Archaeoludology (DAL) is a new field of study involving the analysis and reconstruction of ancient games from incomplete descriptions and archaeological evidence using modern computational techniques. The aim is to provide digital tools and m
Externí odkaz:
http://arxiv.org/abs/1905.13516