Gillian Smith, Levi Lelis
Proceedings of the 15th Conference on Artificial Intelligence and Interactive Digital Entertainment
AIIDE-2019, 2019.
Contents (38 items)
- AIIDE-2019-BehroozRJ #matter #quality #using #word
- Story Quality as a Matter of Perception: Using Word Embeddings to Estimate Cognitive Interest (MB, JR, AJ), pp. 3–9.
- AIIDE-2019-BontragerKASST #game studies #learning #network
- “Superstition” in the Network: Deep Reinforcement Learning Plays Deceptive Games (PB, AK, DA, MS, CS, JT), pp. 10–16.
- AIIDE-2019-DiamantiT #abstraction #adaptation #automation #refinement #simulation
- Automatic Abstraction and Refinement for Simulations with Adaptive Level of Detail (MD, DT), pp. 17–23.
- AIIDE-2019-GaoKHT #case study #learning #on the
- On Hard Exploration for Reinforcement Learning: A Case Study in Pommerman (CG, BK, PHL, MET), pp. 24–30.
- AIIDE-2019-Hernandez-LealK #learning #modelling
- Agent Modeling as Auxiliary Task for Deep Reinforcement Learning (PHL, BK, MET), pp. 31–37.
- AIIDE-2019-KartalHT #learning #predict
- Terminal Prediction as an Auxiliary Task for Deep Reinforcement Learning (BK, PHL, MET), pp. 38–44.
- AIIDE-2019-KreminskiSMW #game studies
- Evaluating AI-Based Games through Retellings (MK, BS, EM, NWF), pp. 45–51.
- AIIDE-2019-LinXR #learning #named #semantics
- GenerationMania: Learning to Semantically Choreograph (ZL, KX, MR), pp. 52–58.
- AIIDE-2019-OsbornDASDTWM #game studies #generative
- Is Your Game Generator Working? Evaluating Gemini, an Intentional Generator (JCO, MD, BRA, AS, JD, DT, NWF, MM), pp. 59–65.
- AIIDE-2019-LiebanaGDIBL #analysis #statistics
- Analysis of Statistical Forward Planning Methods in Pommerman (DPL, RDG, OD, EI, MB, SML), pp. 66–72.
- AIIDE-2019-Snodgrass #sketching
- Levels from Sketches with Example-Driven Binary Space Partition (SS), pp. 73–79.
- AIIDE-2019-SturtevantSTG #abstraction
- Pathfinding and Abstraction with Dynamic Terrain Costs (NRS, DS, BT, TG), pp. 80–86.
- AIIDE-2019-WareGSF #experience #graph #multi
- Multi-Agent Narrative Experience Management as Story Graph Pruning (SGW, ETG, AS, RF), pp. 87–93.
- AIIDE-2019-XuKZHLS #learning #metaprogramming
- Macro Action Selection with Deep Reinforcement Learning in StarCraft (SX, HK, ZZ, RH, YL, HS), pp. 94–99.
- AIIDE-2019-YangO #game studies #monte carlo #realtime
- Guiding Monte Carlo Tree Search by Scripts in Real-Time Strategy Games (ZY, SO), pp. 100–107.
- AIIDE-2019-AzadM #named #simulation
- Lyra: Simulating Believable Opinionated Virtual Characters (SA, CM), pp. 108–115.
- AIIDE-2019-BisbergC #named #parametricity
- SCOPE: Selective Cross-Validation over Parameters for Elo (AJB, RECR), pp. 116–122.
- AIIDE-2019-ChenM #social
- Augmenting Character Path Planning with Layered Social Influences (ZC, JM), pp. 123–129.
- AIIDE-2019-DemediukYDWB #analysis #identification
- Role Identification for Accurate Analysis in Dota 2 (SD, PY, AD, JAW, FB), pp. 130–138.
- AIIDE-2019-EgerM #case study
- A Study of AI Agent Commitment in One Night Ultimate Werewolf with Human Players (ME, CM), pp. 139–145.
- AIIDE-2019-FrazierR #learning
- Improving Deep Reinforcement Learning in Minecraft with Action Advice (SF, MR), pp. 146–152.
- AIIDE-2019-KartalHT19a #learning
- Action Guidance with MCTS for Deep Reinforcement Learning (BK, PHL, MET), pp. 153–159.
- AIIDE-2019-KimRTS #comprehension #natural language
- Cooperation and Codenames: Understanding Natural Language Processing via Codenames (AK, MR, AT, AS), pp. 160–166.
- AIIDE-2019-MachadoGWNNT #design #evaluation #game studies #recommendation
- Evaluation of a Recommender System for Assisting Novice Game Designers (TM, DG, AW, ON, AN, JT), pp. 167–173.
- AIIDE-2019-MoriTS #analysis #experience
- A Structured Analysis of Experience Management Techniques (GM, DT, SS), pp. 174–180.
- AIIDE-2019-PachecoM
- Alignment of Player and Non-Player Character Assertiveness Levels (ACP, CM), pp. 181–187.
- AIIDE-2019-ShirvaniW
- A Plan-Based Personality Model for Story Characters (AS, SGW), pp. 188–194.
- AIIDE-2019-WangSZ #behaviour #learning #modelling
- Beyond Winning and Losing: Modeling Human Motivations and Behaviors with Vector-Valued Inverse Reinforcement Learning (BW, TS, XSZ), pp. 195–201.
- AIIDE-2019-ZhangSFK #behaviour #semantics
- Knowledge-Powered Inference of Crowd Behaviors in Semantically Rich Environments (XZ, DS, PF, MK), pp. 202–209.
- AIIDE-2019-Kreminski #case study #experience #game studies
- Creativity Support for Story Construction Play Experiences (MK), pp. 210–212.
- AIIDE-2019-Lang #towards
- Towards Usable Level PCG (EL), pp. 213–215.
- AIIDE-2019-Makhmutov #adaptation #game studies #generative #music
- Adaptive Game Soundtrack Generation Based on Music Transcription (MM), pp. 216–218.
- AIIDE-2019-Marino #game studies #learning #programming #realtime #search-based
- Learning Strategies for Real-Time Strategy Games with Genetic Programming (JRHM), pp. 219–220.
- AIIDE-2019-Mori #case study #evaluation #experience #interactive #standard
- Standardizing the Evaluation of Digital Managers for Better Interactive Experiences (GM), pp. 221–223.
- AIIDE-2019-Sanghrajka #authoring #interactive #modelling #using
- Interactive Narrative Authoring Using Cognitive Models in Narrative Planning (RS), pp. 224–226.
- AIIDE-2019-Schiffer #game studies #how #performance
- How Actors Can Animate Game Characters: Integrating Performance Theory in the Emotion Model of a Game Character (SS), pp. 227–229.
- AIIDE-2019-Shirvani #towards #using
- Towards More Believable Characters Using Personality and Emotion (AS), pp. 230–232.
- AIIDE-2019-LiuCMOMMWF #case study #experience #interactive
- Playable Experiences at the 15th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (RL, CC, CM, SO, PM, KWM, TW, SF), pp. 234–238.
