Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 26 for “"Monte Carlo Tree Search"”.
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RESOURCE CONSTRAINT COOPERATIVE GAME WITH MONTE CARLO TREE SEARCH
A hybrid methodology of game theory and Monte Carlo Tree Search was developed and the hybrid methodology was tested with various case studies through the nurse scheduling problem to show that it was able to form Pareto front dominance solutions, finding feasible solutions that were optimal and …
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Monte Carlo Tree Search Applications to Neural Theorem Proving
… explores solutions to these problems, namely search and verification, following Yang’s recent contribution: LeanDojo: Theorem Proving with Retrieval-Augmented Language Models. In their work, Yang et al. introduce LeanDojo, an environment for programmatic interaction with the Lean theorem …
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Nested Monte Carlo Tree Search as Applied to Samurai Sudoku
… the game for interesting properties. The large search space presents a challenge for both generating and solving Sudoku puzzles without relying on techniques that simply permute a valid puzzle. These permutations result in puzzles that are essentially the same since they follow the same solution …
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Distributed Monte Carlo Tree Search With Applications To Chip Design
Monte Carlo Tree Search is a classic method in AI that builds up a search tree asymmetrically using random rollouts on a game tree. The work detailed in this thesis expands upon traditional implementations by allowing the capability of fully distributing each node onto different physical machines …
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An Intent-based Neural Monte Carlo Tree Search Framework for Synthesis of Printed Circuit Boards
… such problems. This paper proposes the usage of Monte Carlo Tree Search (MCTS) augmented to improve search in order to self-generate datasets, culminating in a process called LFS (Learning Feedback System). This process allows using past data to accelerate MCTS with deep RL models on new or …
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MONTE CARLO TREE SEARCH AND MINIMAX COMBINATION – APPLICATION OF SOLVING PROBLEMS IN THE GAME OF GO
Monte Carlo Tree Search (MCTS) has been successfully applied to a variety of games. Its best-first algorithm enables implementations without evaluation functions. Combined with Upper Confidence bounds applied to Trees (UCT), MCTS has an advantage over traditional depth-limited minimax search with …
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Artificial intelligence in co-operative games with partial observability
… an environment that an agent can observe. The research performed in this thesis examines the challenge of creating Artificial Intelligence for co-operative games that feature Partial Observability. The main contributions are that Monte-Carlo Tree Search outperforms Genetic Algorithm based agents …
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Winning at Pokémon Random Battles Using Reinforcement Learning
… We demonstrate an agent which employs a Monte Carlo Tree Search informed by a actor-critic network trained using Proximal Policy Optimization with experience collected through self-play. The agent peaked at rank 8 (1693 Elo) on the official Pokémon Showdown gen4randombattles ladder, which …
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Humanization of computational learning in strategy games
… strategy games: minimax, alpha-beta pruning, Monte Carlo tree search, and neural networks. I then explain why I do not believe that people use any of these techniques to play strategy games. I support this claim by creating a new strategy game, which I call Tarble, that people are able to play …
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Application of Retrograde Analysis to Fighting Games
… recent interest in two-player fighting games. Monte-Carlo Tree-Search approaches currently dominate the competition, but it is unclear if this is the best approach for all fighting games. In this thesis we study the design of two-player fighting games and the consequences of the game design on …
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Sidekick agents for sequential planning problems
… and communication actions. We also apply Monte Carlo tree search methods for partially observable domains to the problem and introduce an ensemble-based parallelization strategy. These planning techniques are demonstrated across a range of video game domains.
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From Tic-tac-toe to AlphaGo: a survey of algorithms used in various games
… the Bellman equations, rollouts and the Monte Carlo tree search. Finally, we introduct neural networks and provide the theory on how they can assist decision making both in general and in games. A simulation of the techniques neural networks used is provided. Particular attention is given …
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Simulation-Based General Game Playing
… whether for evaluating game positions or for search guidance. In this thesis we approach this challenge with Monte-Carlo Tree Search (MCTS), which in recent years has become a popular and effective search method in games. For competitive play such an approach requires an effective …
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Future of Personalized, Aligned Language Models
… harmful behavior is an important problem. Search-based methods, such as Best-of-N or Monte-Carlo Tree Search, are effective, but impractical for LLM adaptation due to their high inference cost. On the other hand, using Reinforcement Learning (RL) for adaptation is computationally efficient, …
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Towards Better Turn-Based Strategy Planning Agents: Turn-Based Evolutionary Tree Search
… state spaces where conventional algorithms like Monte Carlo Tree Search (MCTS) and Rolling Horizon Evolutionary Algorithms (RHEA) struggle to scale. First, we perform an investigation into state-of-the-art Deep Learning based models for game agents in TUBSTAP. Then, we propose Turn-Based …
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Robot Graph Grammars: Towards Custom Robots for Every Task
… the table. These difficulties have motivated research into automatic robot design tools. Early tools were often limited in the range of robot topologies they can explore, however. Current graph-based robot representations can expand the space of possible designs, but it is not always clear how …
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Query-driven adaptive sampling
… way that avoids information. We then describe a Monte Carlo tree search approach to plan for all queries in our language, using sample based objective estimators embedded within tree search. This approach outperforms methods that maximize information about all variables in hydrocarbon seep search …
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Solving General Game Playing with Incomplete Information Problem using Iterative Tree Search and Language Learning
… been provided to play games in GGP-II. This research is concerned with three main limitations of algorithms in the literature: valuing-information, generating mixed strategy and cooperating in games which require implicit communication. In this thesis, I theoretically and experimentally show …
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Query-Driven Adaptive Sampling
… way that avoids information. We then describe a Monte Carlo tree search approach to plan for all queries in our language, using sample based objective estimators embedded within tree search. This approach outperforms methods that maximize information about all variables in hydrocarbon seep search …
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Optimization Methods for Distribution Systems: Market Design and Resiliency Enhancement
… approach. On the resiliency side, this research proposed a system restoration approach, taking into account uncertain devices and associated asynchronous information, by means of a two-module optimization models based on binary programming and three phase unbalanced optimal power flow. …
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