[Module 0] [Module 1] [Module 2] [Module 3] [Module 4] [Module 5]

Module 0: What is Neurosymbolic AI?

Tuesday, August 25, 2026 to Tuesday, August 25, 2026

Welcome to the class! In this introductory module, you will become acquainted with the concept of neurosymbolic AI and how it can be used in text generation.

No homework for this module.

Lesson 1: Intro to the Class & Neurosymbolic AI [Slides]

Academic Papers:

• Amit Sheth, Kaushik Roy, Manas Gaur, Neurosymbolic Artificial Intelligence (Why, What, and How)

• Bikram Pratim Bhuyan, Amar Ramdane-Cherif, Ravi Tomar, T. P. Singh, Neuro-symbolic artificial intelligence: a survey

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Module 1: Language Modeling

Thursday, August 27, 2026 to Thursday, September 10, 2026

When large language models were created, researchers realized how much easier it is to generate text. Here, you’ll learn about neural language models, particularly the transformer, and how they are used to generate text.

Homework

• Homework 1: Prompting and Fine-tuning, due Thursday, September 17, 2026 at 11:59PM EST (10% of final grade)

Lesson 1: Neural Language Models + Attention [Slides]

Academic Papers:

• Ilya Sutskever, Oriol Vinyals, Quoc V. Le, Sequence to Sequence Learning with Neural Networks

• Alex Graves, Generating Sequences With Recurrent Neural Networks

Supplemental Media:

• Elle Hunt (The Guardian), Tay, Microsoft’s AI chatbot, gets a crash course in racism from Twitter

• Tom Simonite (Wired), The AI Text Generator That’s Too Dangerous to Make Public

Lesson 2: Transformers [Slides]

Academic Papers:

• Emily M. Bender, Timnit Gebru, Angelina McMillan-Major, Margaret Mitchell, On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?

• Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, Illia Polosukhin, Attention is All You Need

• Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, Language Models are Unsupervised Multitask Learners

• Roy Schwartz, Jesse Dodge, Noah A. Smith, Oren Etzioni, Green AI

Supplemental Media:

• Jay Alammar, The Illustrated Transformer

Lesson 3: Sampling Output

Academic Papers:

• Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, Yejin Choi, The Curious Case of Neural Text Degeneration

• Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, et al., Language Models are Few-Shot Learners

• Sander Schulhoff, Michael Ilie, Nishant Balepur, Konstantine Kahadze, Amanda Liu, Chenglei Si, Yinheng Li, Aayush Gupta, HyoJung Han, Sevien Schulhoff, Pranav Sandeep Dulepet, Saurav Vidyadhara, et al., The Prompt Report: A Systematic Survey of Prompt Engineering Techniques

• Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed Chi, Quoc Le, Denny Zhou, Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

Supplemental Media:

• Daniel Jurafsky, James H. Martin, Speech and Language Processing, Chapter 9: Post-training: Instruction Tuning, Alignment, and Test-Time Compute

• Kory Mathewson, What is Prompting, Really?

• Janelle Shane, AI Weirdness (Newsletter)

Lesson 4: Types of Foundation Models & Word Embeddings

Academic Papers:

• Tomas Mikolov, Kai Chen, Greg Corrado, Jeffrey Dean, Efficient Estimation of Word Representations in Vector Space

• Piotr Bojanowski, Edouard Grave, Armand Joulin, Tomas Mikolov, Enriching Word Vectors with Subword Information

• Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova, BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

• Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu, Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

Supplemental Media:

• Daniel Jurafsky, James H. Martin, Speech and Language Processing, Chapter 6: Vector Semantics and Embeddings

Lesson 5: Module 1 Paper Presentations

Paper Presentations:

• (). . [paper] - presented by

Academic Papers:

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Module 2: Guided Generation

Tuesday, September 15, 2026 to Tuesday, September 29, 2026

Without any sort of scaffolding, neural networks can be difficult, if not impossible to control. This Module will teach you about guiding generation with scripts, causal chains, and events. We’ll also look at how people have been using these techniques in the age of the neural network.

Homework

• Homework 2: Guided Generation, due Tuesday, October 6, 2026 at 11:59PM EST (10% of final grade)

Lesson 1: Scripts and Events

Academic Papers:

• Roger Schank, Robert Abelson, Scripts Plans Goals and Understanding: An Inquiry Into Human Knowledge Structures (Chapter 3: Scripts)

• Karl Pichotta, Raymond Mooney, Learning Statistical Scripts with LSTM Recurrent Neural Networks

• Lara J. Martin, Prithviraj Ammanabrolu, Xinyu Wang, William Hancock, Shruti Singh, Brent Harrison, Mark O. Riedl, Event Representations for Automated Story Generation with Deep Neural Nets

• Nathanael Chambers and Dan Jurafsky, Unsupervised Learning of Narrative Schemas and their Participants

• Keisuke Sakaguchi, Chandra Bhagavatula, Ronan Le Bras, Niket Tandon, Peter Clark, Yejin Choi, proScript: Partially Ordered Scripts Generation via Pre-trained Language Models

• Abhilasha Sancheti, Rachel Rudinger, What do Large Language Models Learn about Scripts?

Lesson 2: Understanding Procedures

Academic Papers:

• Belinda Z. Li, Maxwell Nye, Jacob Andreas, Implicit Representations of Meaning in Neural Language Models

• Qing Lyu, Li Zhang, Chris Callison-Burch, Goal-Oriented Script Construction

• Li Zhang, Qing Lyu, Chris Callison-Burch, Reasoning about Goals, Steps, and Temporal Ordering with WikiHow

• Li Zhang, Qing Lyu, Chris Callison-Burch, Intent Detection with WikiHow

• Rowan Zellers, Yonatan Bisk, Roy Schwartz, Yejin Choi, SWAG: A Large-Scale Adversarial Dataset for Grounded Commonsense Inference

Lesson 3: Guided Generation

Academic Papers:

• Lili Yao, Nanyun Peng, Ralph Weischedel, Kevin Knight, Dongyan Zhao, Rui Yan, Plan-And-Write: Towards Better Automatic Storytelling

• Hannah Rashkin, Asli Celikyilmaz, Yejin Choi, Jianfeng Gao, PlotMachines: Outline-Conditioned Generation with Dynamic Plot State Tracking

• Angela Fan, Mike Lewis, Yann Dauphin, Hierarchical Neural Story Generation

• Hongqiu Wu, Weiqi Wu, Tianyang Xu, Jiameng Zhang, Hai Zhao, Towards Enhanced Immersion and Agency for LLM-based Interactive Drama

Lesson 4: Retrieval-Augmented Generation

Academic Papers:

• Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, Douwe Kiela, Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

• Rachel Chambers, Naomi Tack, Eliot Pearson, Lara J. Martin, Francis Ferraro, BERALL: Generating Retrieval-augmented State-based Interactive Fiction Games

• Zhihua Wen, Zhiliang Tian, Wei Wu, Yuxin Yang, Yanqi Shi, Zhen Huang, Dongsheng Li, GROVE: A Retrieval-augmented Complex Story Generation Framework with A Forest of Evidence

Supplemental Media:

• Daniel Jurafsky and James H. Martin, Speech and Language Processing, Chapter 11. Information Retrieval and Retrieval-Augmented Generation

Lesson 5: Module 2 Paper Presentations

Paper Presentations:

Academic Papers:

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Module 3: Search and Planning

Thursday, October 1, 2026 to Tuesday, October 20, 2026

Homework

• Homework 3: Creating Sabre Problems, due Tuesday, October 27, 2026 at 11:59PM EST (10% of final grade)

Lesson 1: Search

Academic Papers:

Supplemental Media:

• Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, Chapter 2: Intelligent Agents

• Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, Chapter 3: Solving Problems by Searching

• Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, Chapter 11: Automated Planning

• Wikipedia, Stanford Research Institute Problem Solver

• Kory Becker, Artificial Intelligence Planning with STRIPS, A Gentle Introduction

Lesson 2: Classical Planning

Academic Papers:

• Rogelio Cardona-Rivera, Arnav Jhala, Julie Porteous, R. Michael Young, The Story So Far on Narrative Planning

• Michael Lebowitz, Story-Telling as Planning and Learning

• Michael Lebowitz, Planning Stories

• Stephen G. Ware, R. Michael Young, CPOCL: A Narrative Planner Supporting Conflict

• Mark O. Riedl, R. Michael Young, Open-World Planning for Story Generation

• R. Michael Young, Stephen Ware, Brad Cassell, Justus Robertson, Plans and planning in narrative generation: a review of plan-based approaches to the generation of story, discourse and interactivity in narratives

• Julie Porteous, Marc Cavazza, Controlling narrative generation with planning trajectories: The role of constraints

• Stephen G. Ware, Cory Siler, Sabre: A Narrative Planner Supporting Intention and Deep Theory of Mind

• Stephen G. Ware, R. Michael Young, Glaive: A State-Space Narrative Planner Supporting Intentionality and Conflict

• Michael Mateas, Andrew Stern, Integrating Plot, Character and Natural Language Processing in the Interactive Drama Façade

• Manu Sharma, Santiago Ontañón, Manish Mehta, Ashwin Ram, Drama Management and Player Modeling for Interactive Fiction Games

• Stephen G. Ware, Edward T. Garcia, Alireza Shirvani, Rachelyn Farrell, Multi-Agent Narrative Experience Management as Story Graph Pruning

• Julie Porteous, João F. Ferreira, Alan Lindsay, Marc Cavazza, Automated narrative planning model extension

Supplemental Media:

• Narrative Intelligence Lab at the University of Kentucky, Camelot Virtual Environment

Lesson 3: LLM-based Planning

Academic Papers:

• Mihai Polceanu, Julie Porteous, Alan Lindsay, Marc Cavazza, Narrative Plan Generation with Self-Supervised Learning

• Yi Wang, Qian Zhou, David Ledo, StoryVerse: Towards Co-authoring Dynamic Plot with LLM-based Character Simulation via Narrative Planning

• Evgeniia Razumovskaia, Joshua Maynez, Annie Louis, Mirella Lapata, Shashi Narayan, Little Red Riding Hood Goes Around the Globe: Crosslingual Story Planning and Generation with Large Language Models

• Anbang Ye, Christopher Cui, Taiwei Shi, Mark O. Riedl, Neural Story Planning

• Li Zhang, Peter Jansen, Tianyi Zhang, Peter Clark, Chris Callison-Burch, Niket Tandon, PDDLEGO: Iterative Planning in Textual Environments

• Cassie Huang, Li Zhang, On the Limit of Language Models as Planning Formalizers

Lesson 4: Reinforcement Learning

Academic Papers:

• Pradyumna Tambwekar, Murtaza Dhuliawala, Lara J. Martin, Animesh Mehta, Brent Harrison, Mark O. Riedl, Controllable Neural Story Plot Generation via Reward Shaping

• Amal Alabdulkarim, Winston Li, Lara J. Martin, Mark O. Riedl, Goal-Directed Story Generation: Augmenting Generative Language Models with Reinforcement Learning

• Prithviraj Ammanabrolu, Mark O. Riedl, Playing Text-Adventure Games with Graph-Based Deep Reinforcement Learning

• Prithviraj Ammanabrolu, Jack Urbanek, Margaret Li, Arthur Szlam, Tim Rocktäschel, Jason Weston, How to Motivate Your Dragon: Teaching Goal-Driven Agents to Speak and Act in Fantasy Worlds

• Anjie Zhu, Hongcai He, Yongjun Yang, Zetao Zheng, Jie Shao, Hands-Free: Action Abstraction With Hierarchical Reinforcement Learning in Text-Based Games

Lesson 5: Module 3 Paper Presentations

Paper Presentations:

Academic Papers:

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Module 4: Commonsense Reasoning and Schemas

Thursday, October 22, 2026 to Thursday, November 5, 2026

Homework

• Homework 4: COMET-ATOMIC Schema, due Thursday, November 12, 2026 at 11:59PM EST (10% of final grade)

Lesson 1: Commonsense Reasoning

Academic Papers:

• Robyn Speer, Joshua Chin, Catherine Havasi, ConceptNet 5.5: An Open Multilingual Graph of General Knowledge

• Christiane Fellbaum, WordNet

• Maarten Sap, Ronan Le Bras, Emily Allaway, Chandra Bhagavatula, Nicholas Lourie, Hannah Rashkin, Brendan Roof, Noah A. Smith, Yejin Choi, ATOMIC: An Atlas of Machine Commonsense for If-Then Reasoning

• Kathy Panton, Cynthia Matuszek, Douglas Lenat, Dave Schneider, Michael Witbrock, Nick Siegel, and Blake Shepard, Common Sense Reasoning – From Cyc to Intelligent Assistant

• Shane Storks, Qiaozi Gao, Joyce Y. Chai, Commonsense Reasoning for Natural Language Understanding: A Survey of Benchmarks, Resources, and Approaches

• Gabor Angeli & Chris Manning, NaturalLI: Natural Logic Inference for Common Sense Reasoning

• Yidan Sun, Qin Chao, Boyang Li, Event Causality Is Key to Computational Story Understanding

Supplemental Media:

• Allen Institute for Artificial Intelligence, AI2 Common Sense leaderboards

• Yejin Choi, Vered Shwartz, Maarten Sap, Antoine Bosselut, Dan Roth, ACL 2020 Commonsense Tutorial

• David Gunning, Machine Common Sense Concept Paper

Lesson 2: Neurosymbolic Knowledge Base Systems

Academic Papers:

• Jena D. Hwang, Chandra Bhagavatula, Ronan Le Bras, Jeff Da, Keisuke Sakaguchi, Antoine Bosselut, Yejin Choi, (COMET-)ATOMIC2020: On Symbolic and Neural Commonsense Knowledge Graphs

• Peter West, Ronan Bras, Taylor Sorensen, Bill Lin, Liwei Jiang, Ximing Lu, Khyathi Chandu, Jack Hessel, Ashutosh Baheti, Chandra Bhagavatula, Yejin Choi, NovaCOMET: Open Commonsense Foundation Models with Symbolic Knowledge Distillation

• Nasrin Mostafazadeh, Aditya Kalyanpur, Lori Moon, David Buchanan, Lauren Berkowitz, Or Biran, Jennifer Chu-Carroll, GLUCOSE: GeneraLized and COntextualized Story Explanations

Lesson 3: Generating with Ontologies

Academic Papers:

• Martha Palmer, Claire Bonial, Jena Hwang, VerbNet: Capturing English verb behavior, meaning and usage

• Josef Ruppenhofer, Michael Ellsworth, Miriam R. L. Petruck, Christopher R. Johnson, Collin F. Baker, Jan Scheffczyk, FrameNet II: Extended Theory and Practice

• Zhongyang Li, Xiao Ding, Ting Liu, J. Edward Hu, Benjamin Van Durme, Guided Generation of Cause and Effect

• Peter Clark, Bhavana Dalvi, Niket Tandon, What Happened? Leveraging VerbNet to Predict the Effects of Actions in Procedural Text

Lesson 4: Generating Ontologies / World Tracking

Academic Papers:

• Yu Zhu, Rong Pan, Simple Contrastive Learning with Knowledge Graphs for Story Generation

• Xiangyu Peng, Kaige Xie, Amal Alabdulkarim, Harshith Kayam, Samihan Dani, Mark Riedl, Guiding Neural Story Generation with Reader Models

• Chenkai Sun, Tie Xu, ChengXiang Zhai, Heng Ji, Incorporating Task-specific Concept Knowledge into Script Learning

• Lara J. Martin, Brent Harrison, Mark O. Riedl, Improvisational Computational Storytelling in Open Worlds

• László von Kacsoh, Robin Horst, Ralf Dörner, From Stat Block to Tabletop: A First-Order Logic Framework for Authoring Immersive RPG Combat Encounters

• Shanshan Huang, Kenny Q. Zhu, Qianzi Liao, Libin Shen, Yinggong Zhao, Enhanced Story Representation by ConceptNet for Predicting Story Endings

• Peter A. Jansen, A Systematic Survey of Text Worlds as Embodied Natural Language Environments

• Marc-Alexandre Côté, Ákos Kádár, Xingdi Yuan, Ben Kybartas, Tavian Barnes, Emery Fine, James Moore, Matthew Hausknecht, Ruo Yu Tao, Layla El Asri, Mahmoud Adada, Wendy Tay, and Adam Trischler, TextWorld: A Learning Environment for Text-based Games

• Matthew Hausknecht, Prithviraj Ammanabrolu, Marc-Alexandre Côté, Xingdi Yuan, Interactive Fiction Games: A Colossal Adventure

• Eric Zhou, Shreyas Basavatia, Moontashir Siam, Zexin Chen, Mark O. Riedl, STORY2GAME: Generating (Almost) Everything in an Interactive Fiction Game

Lesson 5: Module 4 Paper Presentations

Paper Presentations:

Academic Papers:

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Module 5: Dialog and Agents

Tuesday, November 10, 2026 to Tuesday, November 24, 2026

Homework

• Homework 5: Consistent Personas, due Thursday, December 3, 2026 at 11:59PM EST (10% of final grade)

Lesson 1: Putting a Human in the Loop

Academic Papers:

Lesson 2: Dialog Systems

Academic Papers:

• Alex Rudnicky, Xu Wei, An agenda-based dialog management architecture for spoken language systems

• Heng-Da Xu, Xian-Ling Mao, Puhai Yang, Fanshu Sun, Heyan Huang, Rethinking Task-Oriented Dialogue Systems: From Complex Modularity to Zero-Shot Autonomous Agent

• Pei Zhou, Andrew Zhu, Jennifer Hu, Jay Pujara, Xiang Ren, Chris Callison-Burch, Yejin Choi, Prithviraj Ammanabrolu, I Cast Detect Thoughts: Learning to Converse and Guide with Intents and Theory-of-Mind in Dungeons and Dragons

Supplemental Media:

• Jacob Eisenstein, NLP Chapter 19.3: Dialogue

• Dan Jurafsky and James H. Martin, Speech and Language Processing, Chapter 25: Conversation and its Structure

Lesson 3: Agent-based AI

Academic Papers:

• Jack Urbanek, Angela Fan, Siddharth Karamcheti, Saachi Jain, Samuel Humeau, Emily Dinan, Tim Rocktäschel, Douwe Kiela, Arthur Szlam, Jason Weston, Learning to Speak and Act in a Fantasy Text Adventure Game

• Wai Man Si, Prithviraj Ammanabrolu, Mark O. Riedl, Telling Stories through Multi-User Dialogue by Modeling Character Relations

• Shrimai Prabhumoye, Margaret Li, Jack Urbanek, Emily Dinan, Douwe Kiela, Jason Weston, Arthur Szlam, I love your chain mail! Making knights smile in a fantasy game world: Open-domain goal-oriented dialogue agents

• Chris Callison-Burch, Gaurav Singh Tomar, Lara J. Martin, Daphne Ippolito, Suma Bailis, David Reitter, Dungeons and Dragons as a Dialogue Challenge for Artificial Intelligence

• Anvesh Rao Vijjini, Faeze Brahman, Snigdha Chaturvedi, Towards Inter-character Relationship-driven Story Generation

Lesson 4: Personas and Roleplaying

Academic Papers:

• Ke Ji, Yixin Lian, Linxu Li, Jingsheng Gao, Weiyuan Li, Bin Dai, Enhancing Persona Consistency for LLMs’ Role-Playing using Persona-Aware Contrastive Learning

• Seungwon Lim, Seungbeen Lee, Dongjun Min, Youngjae Yu, Persona Dynamics: Unveiling the Impact of Persona Traits on Agents in Text-Based Games

• Silin Gao, Beatriz Borges, Soyoung Oh, Deniz Bayazit, Saya Kanno, Hiromi Wakaki, Yuki Mitsufuji, Antoine Bosselut, PeaCoK: Persona Commonsense Knowledge for Consistent and Engaging Narratives

• Keming Lu, Bowen Yu, Chang Zhou, Jingren Zhou, Large Language Models are Superpositions of All Characters: Attaining Arbitrary Role-play via Self-Alignment

• Xuhui Zhou, Zhe Su, Tiwalayo Eisape, Hyunwoo Kim, Maarten Sap, Is this the real life? Is this just fantasy? The Misleading Success of Simulating Social Interactions With LLMs

• Lara J. Martin, Brent Harrison, Mark O. Riedl, Improvisational Computational Storytelling in Open Worlds

• Andrew Zhu, Evan Osgood, Chris Callison-Burch, First Steps Towards Overhearing LLM Agents: A Case Study With Dungeons & Dragons Gameplay

• Andrew Zhu, Lara J. Martin, Andrew Head, Chris Callison-Burch, CALYPSO: LLMs as Dungeon Masters’ Assistants

• Piotr Mirowski, Kory W. Mathewson, Jaylen Pittman, Richard Evans, Co-Writing Screenplays and Theatre Scripts with Language Models: Evaluation by Industry Professionals

• Ann Yuan, Andy Coenen, Emily Reif, Daphne Ippolito, Wordcraft: story writing with large language models

• Ben Samuel, James Ryan, Adam J. Summerville, Michael Mateas, Noah Wardrip-Fruin, Bad News: An Experiment in Computationally Assisted Performance

• Michael Mateas and Andrew Stern, Façade: An Experiment in Building a Fully-Realized Interactive Drama

Lesson 5: Module 5 Paper Presentations

Paper Presentations:

Academic Papers:

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