109 citations · 173 across the 32 of their papers we have counts for
18 papers · 1 filter
A Systematic Approach to Design Real-World Human-in-the-Loop Deep Reinforcement Learning: Salient Features, Challenges and Trade-offs
Jalal Arabneydi, Saiful Islam, Srijita Das +7
With the growing popularity of deep reinforcement learning (DRL), human-in-the-loop (HITL) approach has the potential to revolutionize the way we approach decision-making problems…
A Framework for Predicting the Impact of Game Balance Changes through Meta Discovery
Akash Saravanan, Matthew Guzdial
A metagame is a collection of knowledge that goes beyond the rules of a game. In competitive, team-based games like Pokémon or League of Legends, it refers to the set of current do…
Human-AI Collaboration in Real-World Complex Environment with Reinforcement Learning
Md Saiful Islam, Srijita Das, Sai Krishna Gottipati +6
Recent advances in reinforcement learning (RL) and Human-in-the-Loop (HitL) learning have made human-AI collaboration easier for humans to team with AI agents. Leveraging human exp…
Tree-Based Reconstructive Partitioning: A Novel Low-Data Level Generation Approach
Emily Halina, Matthew Guzdial
Procedural Content Generation (PCG) is the algorithmic generation of content, often applied to games. PCG and PCG via Machine Learning (PCGML) have appeared in published games. How…
Procedural Content Generation via Knowledge Transformation (PCG-KT)
Anurag Sarkar, Matthew Guzdial, Sam Snodgrass +3
We introduce the concept of Procedural Content Generation via Knowledge Transformation (PCG-KT), a new lens and framework for characterizing PCG methods and approaches in which con…
Generating Real-Time Strategy Game Units Using Search-Based Procedural Content Generation and Monte Carlo Tree Search
Kynan Sorochan, Matthew Guzdial
Real-Time Strategy (RTS) game unit generation is an unexplored area of Procedural Content Generation (PCG) research, which leaves the question of how to automatically generate inte…