3 citations · 7 across the 4 of their papers we have counts for
5 papers
Towards Explainable Strategy Templates using NLP Transformers
Pallavi Bagga, Kostas Stathis
This paper bridges the gap between mathematical heuristic strategies learned from Deep Reinforcement Learning (DRL) in automated agent negotiation, and comprehensible, natural lang…
Learning to Identify Perceptual Bugs in 3D Video Games
Benedict Wilkins, Kostas Stathis
Automated Bug Detection (ABD) in video games is composed of two distinct but complementary problems: automated game exploration and bug identification. Automated game exploration h…
Deep Learnable Strategy Templates for Multi-Issue Bilateral Negotiation
Pallavi Bagga, Nicola Paoletti, Kostas Stathis
We study how to exploit the notion of strategy templates to learn strategies for multi-issue bilateral negotiation. Each strategy template consists of a set of interpretable parame…
A Metric Learning Approach to Anomaly Detection in Video Games
Benedict Wilkins, Chris Watkins, Kostas Stathis
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 (…
A Deep Reinforcement Learning Approach to Concurrent Bilateral Negotiation
Pallavi Bagga, Nicola Paoletti, Bedour Alrayes +1
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. The agent uses an act…