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Articles 1 - 5 of 5
Full-Text Articles in Theory and Algorithms
A Bridge Between Graph Neural Networks And Transformers: Positional Encodings As Node Embeddings, Bright Kwaku Manu
A Bridge Between Graph Neural Networks And Transformers: Positional Encodings As Node Embeddings, Bright Kwaku Manu
Electronic Theses and Dissertations
Graph Neural Networks and Transformers are very powerful frameworks for learning machine learning tasks. While they were evolved separately in diverse fields, current research has revealed some similarities and links between them. This work focuses on bridging the gap between GNNs and Transformers by offering a uniform framework that highlights their similarities and distinctions. We perform positional encodings and identify key properties that make the positional encodings node embeddings. We found that the properties of expressiveness, efficiency and interpretability were achieved in the process. We saw that it is possible to use positional encodings as node embeddings, which can be …
Simulating Polistes Dominulus Nest-Building Heuristics With Deterministic And Markovian Properties, Benjamin Pottinger
Simulating Polistes Dominulus Nest-Building Heuristics With Deterministic And Markovian Properties, Benjamin Pottinger
Undergraduate Honors Theses
European Paper Wasps (Polistes dominula) are social insects that build round, symmetrical nests. Current models indicate that these wasps develop colonies by following simple heuristics based on nest stimuli. Computer simulations can model wasp behavior to imitate natural nest building. This research investigated various building heuristics through a novel Markov-based simulation. The simulation used a hexagonal grid to build cells based on the building rule supplied to the agent. Nest data was compared with natural data and through visual inspection. Larger nests were found to be less compact for the rules simulated.
Risk Gameplay Analysis Using Stochastic Beam Search, Jacob Gillenwater
Risk Gameplay Analysis Using Stochastic Beam Search, Jacob Gillenwater
Electronic Theses and Dissertations
Hasbro’s RISK, first published in 1959, is a complex multiplayer strategy game that has received little attention from the scientific community. Training artificial intelligence (AI) agents using stochastic beam search gives insight into effective strategy when playing RISK. A comprehensive analysis of the systems of play challenges preconceptions about good strategy in some areas of the game while reinforcing those preconceptions in others. This study applies stochastic beam search to discover optimal strategies in RISK. Results of the search show both support for and challenges to traditionally held positions about RISK gameplay. While stochastic beam search competently investigates gameplay on …
Machine Learning Approaches To Dribble Hand-Off Action Classification With Sportvu Nba Player Coordinate Data, Dembe Stephanos
Machine Learning Approaches To Dribble Hand-Off Action Classification With Sportvu Nba Player Coordinate Data, Dembe Stephanos
Electronic Theses and Dissertations
Recently, strategies of National Basketball Association teams have evolved with the skillsets of players and the emergence of advanced analytics. One of the most effective actions in dynamic offensive strategies in basketball is the dribble hand-off (DHO). This thesis proposes an architecture for a classification pipeline for detecting DHOs in an accurate and automated manner. This pipeline consists of a combination of player tracking data and event labels, a rule set to identify candidate actions, manually reviewing game recordings to label the candidates, and embedding player trajectories into hexbin cell paths before passing the completed training set to the classification …
Investigating Genetic Algorithm Optimization Techniques In Video Games, Nathan Ambuehl
Investigating Genetic Algorithm Optimization Techniques In Video Games, Nathan Ambuehl
Undergraduate Honors Theses
Immersion is essential for player experience in video games. Artificial Intelligence serves as an agent that can generate human-like responses and intelligence to reinforce a player’s immersion into their environment. The most common strategy involved in video game AI is using decision trees to guide chosen actions. However, decision trees result in repetitive and robotic actions that reflect an unrealistic interaction. This experiment applies a genetic algorithm that explores selection, crossover, and mutation functions for genetic algorithm implementation in an isolated Super Mario Bros. pathfinding environment. An optimized pathfinding AI can be created by combining an elitist selection strategy with …