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Physical Sciences and Mathematics Commons™
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- Artificial Intelligence (3)
- Games (3)
- Artificial intelligence (2)
- Education (2)
- Amazons (1)
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- Birds of a Feather (1)
- Deep learning (1)
- Deep neutral network learning (1)
- Game design (1)
- Hey! That’s My Fish! (1)
- Machine learning (1)
- Neural networks (1)
- Open access (1)
- Open educational resources (1)
- Reinforcement learning (1)
- Supervised learning (1)
- Teaching (1)
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Articles 1 - 6 of 6
Full-Text Articles in Physical Sciences and Mathematics
The Birds Of A Feather Research Challenge, Todd W. Neller
The Birds Of A Feather Research Challenge, Todd W. Neller
Computer Science Faculty Publications
Neller presented a set of research challenges for undergraduates that allow an excellent formative experience of research, writing, peer review, and potential presentation and publication through a top-tier conference. The focus problem is the analysis of a newly-designed solitaire card game, Birds of a Feather, so potentials for discovery abound. Open access talk slides, research code, solvability data sets, research tutorial videos, and more are also available at http://cs.gettysburg.edu/~tneller/puzzles/boaf .
Amazons, Penguins, And Amazon Penguins, Todd W. Neller
Amazons, Penguins, And Amazon Penguins, Todd W. Neller
Computer Science Faculty Publications
This talk discussed a family of games based on Amazons (1988), a distant relative of Go (area control) and Chess (queen-like movement), innovated with the introduction of move obstacles. Hey! That’s My Fish! (2003) restricted the addition of obstacles and added varying points for position visits. Introducing original related game designs (e.g. Amazon Penguins (2009) and Paper Pen-guins (2009)), we demonstrated how game mechanics are like genes that mutate, crossover, and invite evolution of new games.
Playful Ai Education, Todd W. Neller
Playful Ai Education, Todd W. Neller
Computer Science Faculty Publications
In this talk, Neller shared how games can serve as a fun means of teaching not only game-tree search in Artificial Intelligence (AI), but also such diverse topics as constraint satisfaction, logical reasoning, planning, uncertain reasoning, machine learning, and robotics. He observed that teachers teach best when they enjoy what they share and encouraged AI educators present to teach to their unique strengths and enthusiasms.
Ai Education: Open-Access Educational Resources On Ai, Todd W. Neller
Ai Education: Open-Access Educational Resources On Ai, Todd W. Neller
Computer Science Faculty Publications
Open-access AI educational resources are vital to the quality of the AI education we offer. Avoiding the reinvention of wheels is especially important to us because of the special challenges of AI Education. AI could be said to be “the really interesting miscellaneous pile of Computer Science”. While “artificial” is well-understood to encompass engineered artifacts, “intelligence” could be said to encompass any sufficiently difficult problem as would require an intelligent approach and yet does not fall neatly into established Computer Science subdisciplines. Thus AI consists of so many diverse topics that we would be hard-pressed to individually create quality learning …
Ai Education: Deep Neural Network Learning Resources, Todd W. Neller
Ai Education: Deep Neural Network Learning Resources, Todd W. Neller
Computer Science Faculty Publications
In this column, we focus on resources for learning and teaching deep neural network learning. Many exciting advances have been made in this area of late, and so many resources have become available online that the flood of relevant concepts and techniques can be overwhelming. Here, we hope to provide a sampling of high-quality resources to guide the newcomer into this booming field. [excerpt]
Ai Education: Machine Learning Resources, Todd W. Neller
Ai Education: Machine Learning Resources, Todd W. Neller
Computer Science Faculty Publications
In this column, we focus on resources for learning and teaching three broad categories of machine learning (ML): supervised, unsupervised, and reinforcement learning. In ournext column, we will focus specifically on deep neural network learning resources, so if you have any resource recommendations, please email them to the address above. [excerpt]