Node.js is a popular non-browser JavaScript platform that provides useful but sometimes also vulnerable packages. On one hand, prior works have proposed many program analysis-based approaches to detect Node.js vulnerabilities, such as command …
Cooperation between AI agents and humans is of ever greater importance. In this paper we present an AI agent for the game Pandemic that was specifically designed to play cooperatively with a human player. Our agent utilizes planning to determine …
Explainable AI is an emerging field that studies how to explain the rationality behind the decisions of intelligent computer-based systems in human-understandable terms. The research-focus so far has though almost exclusively been on model …
This work introduces World-GAN, the first method to perform data-driven Procedural Content Generation via Machine Learning in Minecraft from a single example. Based on a 3D Generative Adversarial Network (GAN) architecture, we are able to create …
Probabilistic classification has shown success in detecting known types of software bugs. However, the works following this approach tend to require a large amount of specimens to train their models. We present a new machine learning-based bug …
The generalizability of programs synthesized by genetic programming (GP) to unseen test cases is one of the main challenges of GP-based program synthesis. Recent work showed that increasing the amount of training data improves the generalizability of …
We put forward a novel learning methodology for ensembles of decision trees based on a genetic algorithm that is able to train a decision tree for maximizing both its accuracy and its robustness to adversarial perturbations. This learning algorithm …
Data is a prevalent part of every business and scientific domain, but its explosive volume and increasing complexity make data querying challenging even for experts. For this reason, numerous text-to-SQL systems have been developed that enable …
In this article, we present an interpretable artificial intelligence, and its associated machine learning algorithm, that is capable of automatically learning the rules of a game whenever the rules—the relationship between a player’s current state …
Estimation of distribution genetic programming (EDA-GP) algorithms are metaheuristics where sampling new solutions from a learned probabilistic model replaces the standard mutation and recombination operators of genetic programming (GP). This paper …