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Found 12 papers, 0 papers with code
A Multi-class Approach -- Building a Visual Classifier based on Textual Descriptions using Zero-Shot Learning
Machine Learning (ML) techniques for image classification routinely require many labelled images for training the model and while testing, we ought to use images belonging to the same domain as those used for training.
Robust Classification of High-Dimensional Spectroscopy Data Using Deep Learning and Data Synthesis
This paper presents a new approach to classification of high dimensional spectroscopy data and demonstrates that it outperforms other current state-of-the art approaches.
A Virtual Testbed for Critical Incident Investigation with Autonomous Remote Aerial Vehicle Surveying, Artificial Intelligence, and Decision Support
Autonomous robotics and artificial intelligence techniques can be used to support human personnel in the event of critical incidents.
Using a Game Engine to Simulate Critical Incidents and Data Collection by Autonomous Drones
Using a game engine, we have developed a virtual environment which models important aspects of critical incident scenarios.
Skilled Experience Catalogue: A Skill-Balancing Mechanism for Non-Player Characters using Reinforcement Learning
The objective of this mechanism is to approximately match the skill level of an NPC to an opponent in real-time.
Adaptive Shooting for Bots in First Person Shooter Games Using Reinforcement Learning
In current state-of-the-art commercial first person shooter games, computer controlled bots, also known as non player characters, can often be easily distinguishable from those controlled by humans.
Analysis of the Effect of Unexpected Outliers in the Classification of Spectroscopy Data
Multi-class classification algorithms are very widely used, but we argue that they are not always ideal from a theoretical perspective, because they assume all classes are characterized by the data, whereas in many applications, training data for some classes may be entirely absent, rare, or statistically unrepresentative.
DRE-Bot: A Hierarchical First Person Shooter Bot Using Multiple Sarsa(λ) Reinforcement Learners
This paper describes an architecture for controlling non-player characters (NPC) in the First Person Shooter (FPS) game Unreal Tournament 2004.
Learning to Shoot in First Person Shooter Games by Stabilizing Actions and Clustering Rewards for Reinforcement Learning
While reinforcement learning (RL) has been applied to turn-based board games for many years, more complex games involving decision-making in real-time are beginning to receive more attention.
A Virtual Environment with Multi-Robot Navigation, Analytics, and Decision Support for Critical Incident Investigation
Because of the rare and high-risk nature of these events, realistic simulations can support the development and evaluation of AI-based tools.
Colwell's Castle Defence: A Custom Game Using Dynamic Difficulty Adjustment to Increase Player Enjoyment
Dynamic Difficulty Adjustment (DDA) is a mechanism used in video games that automatically tailors the individual gaming experience to match an appropriate difficulty setting.
A One-Sided Classification Toolkit with Applications in the Analysis of Spectroscopy Data
It can often be impossible to fully characterise outlier examples given the fact that they can represent the immeasurable quantity of "everything else" that is not a target.
