Gamification of Problem Solving Process Based on Logical Rules

Novikov, F. A.; Katsman, Viktor

doi:10.1007/978-3-030-02750-6_28

Cited by 2 publications

(1 citation statement)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Solving combinatorial problems through gamification has been broadly studied in the literature. For instance, Novikov and Katsman (2018) propose an approach to verify the solution of logicalcombinatorial problems. They interpret the application of the logic rules as moves in an intellectual game.…”

Section: Related Workmentioning

confidence: 99%

Learning self-play agents for combinatorial optimization problems

Xu¹,

Lieberherr²

2020

The Knowledge Engineering Review

View full text Add to dashboard Cite

Recent progress in reinforcement learning (RL) using self-gameplay has shown remarkable performance on several board games (e.g., Chess and Go) as well as video games (e.g., Atari games and Dota2). It is plausible to consider that RL, starting from zero knowledge, might be able to gradually approximate a winning strategy after a certain amount of training. In this paper, we explore neural Monte-Carlo-Tree-Search (neural MCTS), an RL algorithm which has been applied successfully by DeepMind to play Go and Chess at a super-human level. We try to leverage the computational power of neural MCTS to solve a class of combinatorial optimization problems. Following the idea of Hintikka's Game-Theoretical Semantics, we propose the Zermelo Gamification (ZG) to transform specific combinatorial optimization problems into Zermelo games whose winning strategies correspond to the solutions of the original optimization problem. A specially designed neural MCTS algorithm is also provided to train Zermelo game play agents. We use a prototype problem for which the ground-truth policy is efficiently computable to demonstrate that ZG is promising.

show abstract

Section: Related Workmentioning

confidence: 99%

Learning self-play agents for combinatorial optimization problems

Xu¹,

Lieberherr²

2020

The Knowledge Engineering Review

View full text Add to dashboard Cite

show abstract

Игрофикация Процесса Решения Типовых Учебных Задач На Основе Выбора Правил Преобразования

Katsman¹,

Козлов²,

Novikov³

2020

Естественные И Технические Науки

View full text Add to dashboard Cite

Одним из основных элементов современного образовательного процесса в области технических наук является решение задач на преобразование символьных выражений. При решении таких задач учащимся требуется преобразовать исходное выражение так, чтобы оно удовлетворяло целевым критериям, например, являлось конъюнктивной нормальной формой или совпадало с требуемым целевым выражением. Для совершения преобразований над выражением или его подвыражениями учащийся может использовать уже изученные правила преобразования.

show abstract

Gamification of Problem Solving Process Based on Logical Rules

Cited by 2 publications

References 4 publications

Learning self-play agents for combinatorial optimization problems

Learning self-play agents for combinatorial optimization problems

Игрофикация Процесса Решения Типовых Учебных Задач На Основе Выбора Правил Преобразования

Contact Info

Product

Resources

About