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Found 11 papers, 3 papers with code
Towards Explainable Strategy Templates using NLP Transformers
This paper bridges the gap between mathematical heuristic strategies learned from Deep Reinforcement Learning (DRL) in automated agent negotiation, and comprehensible, natural language explanations.
Black-Box Analysis: GPTs Across Time in Legal Textual Entailment Task
The evolution of Generative Pre-trained Transformer (GPT) models has led to significant advancements in various natural language processing applications, particularly in legal textual entailment.
A negation detection assessment of GPTs: analysis with the xNot360 dataset
Negation is a fundamental aspect of natural language, playing a critical role in communication and comprehension.
World of Bugs: A Platform for Automated Bug Detection in 3D Video Games
We present World of Bugs (WOB), an open platform that aims to support Automated Bug Detection (ABD) research in video games.
Learning to Identify Perceptual Bugs in 3D Video Games
Automated Bug Detection (ABD) in video games is composed of two distinct but complementary problems: automated game exploration and bug identification.
Supporting Complaints Investigation for Nursing and Midwifery Regulatory Agencies
Health professional regulators aim to protect the health and well-being of patients and the public by setting standards for scrutinising and overseeing the training and conduct of health and care professionals.
Learnable Strategies for Bilateral Agent Negotiation over Multiple Issues
We present a novel bilateral negotiation model that allows a self-interested agent to learn how to negotiate over multiple issues in the presence of user preference uncertainty.
A Metric Learning Approach to Anomaly Detection in Video Games
With the aim of designing automated tools that assist in the video game quality assurance process, we frame the problem of identifying bugs in video games as an anomaly detection (AD) problem.
A Deep Reinforcement Learning Approach to Concurrent Bilateral Negotiation
We present a novel negotiation model that allows an agent to learn how to negotiate during concurrent bilateral negotiations in unknown and dynamic e-markets.
Probabilistic Programming with Gaussian Process Memoization
Gaussian Processes (GPs) are widely used tools in statistics, machine learning, robotics, computer vision, and scientific computation.
Computational Logic Foundations of KGP Agents
This paper presents the computational logic foundations of a model of agency called the KGP (Knowledge, Goals and Plan model.
