Papers Game of Chess

R2C: Mapping Room to Chessboard to Unlock LLM As Low-Level Action Planner

This paper explores using large language models (LLMs) as low-level action planners for embodied tasks. While LLMs excel as the robot's "brain" for high-level planning, they face challenges in directly controlling th…

Bridging the Human-AI Knowledge Gap: Concept Discovery and Transfer in AlphaZero

Artificial Intelligence (AI) systems have made remarkable progress, attaining super-human performance across various domains. This presents us with an opportunity to further human knowledge and improve human expert perfo…

Diversifying AI: Towards Creative Chess with AlphaZero

In recent years, Artificial Intelligence (AI) systems have surpassed human intelligence in a variety of computational tasks. However, AI systems, like humans, make mistakes, have blind spots, hallucinate, and struggle to…

The Value of Chess Squares

We propose a neural network-based approach to calculate the value of a chess square-piece combination. Our model takes a triplet (Color, Piece, Square) as an input and calculates a value that measures the advantage/disad…

Representation Matters for Mastering Chess: Improved Feature Representation in AlphaZero Outperforms Switching to Transformers

While transformers have gained recognition as a versatile tool for artificial intelligence (AI), an unexplored challenge arises in the context of chess - a classical AI benchmark. Here, incorporating Vision Transformers …

Human and Machine Intelligence in n-Person Games with Partial Knowledge: Theory and Computation

In this paper, I formalize intelligence measurement in games by introducing mechanisms that assign a real number -- interpreted as an intelligence score -- to each player in a game. This score quantifies the ex-post stra…

Hybrid cuckoo search algorithm for the minimum dominating set problem

The notions of dominating sets of graphs began almost 400 years ago with the game of chess, which sparked the analysis of dominating sets of graphs, at first relatively loosely until the beginnings of the 1960s, when the…

Acquisition of Chess Knowledge in AlphaZero

What is learned by sophisticated neural network agents such as AlphaZero? This question is of both scientific and practical interest. If the representations of strong neural networks bear no resemblance to human concepts…

Modeling the Mistakes of Boundedly Rational Agents Within a Bayesian Theory of Mind

When inferring the goals that others are trying to achieve, people intuitively understand that others might make mistakes along the way. This is crucial for activities such as teaching, offering assistance, and deciding …

Chess as a Testbed for Language Model State Tracking

Transformer language models have made tremendous strides in natural language understanding tasks. However, the complexity of natural language makes it challenging to ascertain how accurately these models are tracking the…

Learning Chess Blindfolded

Transformer language models have made tremendous strides in natural language understanding. However, the complexity of natural language makes it challenging to ascertain how accurately these models are tracking the world…

Playing Chess with Limited Look Ahead

We have seen numerous machine learning methods tackle the game of chess over the years. However, one common element in these works is the necessity of a finely optimized look ahead algorithm. The particular interest of t…

Leveraging Rationales to Improve Human Task Performance

Machine learning (ML) systems across many application areas are increasingly demonstrating performance that is beyond that of humans. In response to the proliferation of such models, the field of Explainable AI (XAI) has…

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

Constructing agents with planning capabilities has long been one of the main challenges in the pursuit of artificial intelligence. Tree-based planning methods have enjoyed huge success in challenging domains, such as che…

Content Determination for Chess as a Source for Suspenseful Narratives

Learning to Generate Move-by-Move Commentary for Chess Games from Large-Scale Social Forum Data

This paper examines the problem of generating natural language descriptions of chess games. We introduce a new large-scale chess commentary dataset and propose methods to generate commentary for individual moves in a che…

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

The game of chess is the most widely-studied domain in the history of artificial intelligence. The strongest programs are based on a combination of sophisticated search techniques, domain-specific adaptations, and handcr…

DeepChess: End-to-End Deep Neural Network for Automatic Learning in Chess

We present an end-to-end learning method for chess, relying on deep neural networks. Without any a priori knowledge, in particular without any knowledge regarding the rules of chess, a deep neural network is trained usin…

A Popperian Falsification of Artificial Intelligence -- Lighthill Defended

The area of computation called artificial intelligence (AI) is falsified by describing a previous 1972 falsification of AI by British mathematical physicist James Lighthill. How Lighthill's arguments continue to apply to…

Phoenix: A Self-Optimizing Chess Engine

Since the advent of computers, many tasks which required humans to spend a lot of time and energy have been trivialized by the computers' ability to perform repetitive tasks extremely quickly. Playing chess is one such t…