Search Results for author:
Found 32 papers, 2 papers with code
Learning opening books in partially observable games: using random seeds in Phantom Go
Many artificial intelligences (AIs) are randomized.
The αμ Search Algorithm for the Game of Bridge
{\alpha}{\mu} is an anytime heuristic search algorithm for incomplete information games that assumes perfect information for the opponents.
Monte Carlo Game Solver
We present a general algorithm to order moves so as to speedup exact game solvers.
Polygames: Improved Zero Learning
Since DeepMind's AlphaZero, Zero learning quickly became the state-of-the-art method for many board games.
Generalized Nested Rollout Policy Adaptation
Nested Rollout Policy Adaptation (NRPA) is a Monte Carlo search algorithm for single player games.
Mobile Networks for Computer Go
The architecture of the neural networks used in Deep Reinforcement Learning programs such as Alpha Zero or Polygames has been shown to have a great impact on the performances of the resulting playing engines.
Minimax Strikes Back
Deep Reinforcement Learning (DRL) reaches a superhuman level of play in many complete information games.
Stabilized Nested Rollout Policy Adaptation
Nested Rollout Policy Adaptation (NRPA) is a Monte Carlo search algorithm for single player games.
Optimizing $αμ$
$\alpha\mu$ is a search algorithm which repairs two defaults of Perfect Information Monte Carlo search: strategy fusion and non locality.
Improving Model and Search for Computer Go
The standard for Deep Reinforcement Learning in games, following Alpha Zero, is to use residual networks and to increase the depth of the network to get better results.
Batch Monte Carlo Tree Search
The transposition table contains the results of the inferences while the search tree contains the statistics of Monte Carlo Tree Search.
Time-based Dynamic Controllability of Disjunctive Temporal Networks with Uncertainty: A Tree Search Approach with Graph Neural Network Guidance
Scheduling in the presence of uncertainty is an area of interest in artificial intelligence due to the large number of applications.
Optimal Solving of Constrained Path-Planning Problems with Graph Convolutional Networks and Optimized Tree Search
More specifically, a graph neural network is used to assist the branch and bound algorithm in handling constraints associated with a desired solution path.
Constrained Shortest Path Search with Graph Convolutional Neural Networks
In this paper, we focus on shortest path search with mandatory nodes on a given connected graph.
Learning-based Preference Prediction for Constrained Multi-Criteria Path-Planning
The uncertain criterion represents the feasibility of driving through the path without requiring human intervention.
Generalized Nested Rollout Policy Adaptation with Dynamic Bias for Vehicle Routing
In this paper we present an extension of the Nested Rollout Policy Adaptation algorithm (NRPA), namely the Generalized Nested Rollout Policy Adaptation (GNRPA), as well as its use for solving some instances of the Vehicle Routing Problem.
Solving Disjunctive Temporal Networks with Uncertainty under Restricted Time-Based Controllability using Tree Search and Graph Neural Networks
Moreover, we leverage a graph neural network as a heuristic for tree search guidance.
Refutation of Spectral Graph Theory Conjectures with Monte Carlo Search
We demonstrate how Monte Carlo Search (MCS) algorithms, namely Nested Monte Carlo Search (NMCS) and Nested Rollout Policy Adaptation (NRPA), can be used to build graphs and find counter-examples to spectral graph theory conjectures in minutes.
Planning and Learning: Path-Planning for Autonomous Vehicles, a Review of the Literature
We describe briefly the concept of reinforcement learning algorithms and some approaches designed to date.
Nested Search versus Limited Discrepancy Search
Limited Discrepancy Search (LDS) is a popular algorithm to search a state space with a heuristic to order the possible actions.
Solving the HP model with Nested Monte Carlo Search
The algorithm presented in this paper does not beat state of the art algorithms, see PERM (Hsu and Grassberger 2011), REMC (Thachuk, Shmygelska, and Hoos 2007) or WLRE (W\"ust and Landau 2012) for better results.
Learning to Play Stochastic Two-player Perfect-Information Games without Knowledge
In this paper, we extend the Descent framework, which enables learning and planning in the context of two-player games with perfect information, to the framework of stochastic games.
Towards Tackling MaxSAT by Combining Nested Monte Carlo with Local Search
First, a nesting of the tree search inspired by the Nested Monte Carlo Search algorithm is effective on most instance types in the benchmark.
A Scalable Framework for Automatic Playlist Continuation on Music Streaming Services
Music streaming services often aim to recommend songs for users to extend the playlists they have created on these services.
On the Consistency of Average Embeddings for Item Recommendation
A prevalent practice in recommender systems consists in averaging item embeddings to represent users or higher-level concepts in the same embedding space.
Vision Transformers for Computer Go
Motivated by the success of transformers in various fields, such as language understanding and image analysis, this investigation explores their application in the context of the game of Go.
Generalized Nested Rollout Policy Adaptation with Limited Repetitions
Generalized Nested Rollout Policy Adaptation (GNRPA) is a Monte Carlo search algorithm for optimizing a sequence of choices.
Learning a Prior for Monte Carlo Search by Replaying Solutions to Combinatorial Problems
Monte Carlo Search gives excellent results in multiple difficult combinatorial problems.
Monte Carlo Search Algorithms Discovering Monte Carlo Tree Search Exploration Terms
We automatically design the PUCT and the SHUSS root exploration terms.
Dialogue avec Molière (Dialogue with Molière )
A l’occasion du quatre-centième anniversaire de la naissance de Molière (1622-1673), nous présentons un agent conversationnel qui parle comme un personnage du théâtre de Molière.
