The Vision

A trilogy, expressing the vision behind the work of the Genesis Enterprise, including

• The original vision statement, including a larger discussion of the Genesis System and a discussion of the role of Rao's vision system.
• The vision, with an emphasis on five methodological steps: identify the competence to be understood; formulate computational problems; propose computational solutions; develop an exploratory implementation; and crystalize emergent principles
• The vision, with a review of history, discussion of Turing, and a focus on asking the right questions about how we are different from and the same as other primates.

We describe the current status of the Genesis story understanding system in a white paper on story understanding. Additional information is in a paper on story summarization.

Genesis and Genesis related theses

• Extract a Generalizable Lesson from a Fable Through Emotional Explanation and Alignment with a Past Experience, Alice Jin, MEng, 2019.
• Interpreting Author Intentions by Analyzing Story Modulation, Suri Bandler, MEng, 2019.
• Vision by Alignment, Adam Kraft, PhD, 2018.
• Using Near Misses to Teach Concepts to a Human Intelligence System, Jake Barnwell, MEng, 2018.
• A Computational Characterization of Domain-Based Causal Reasoning Development in Children, Caroline Aronoff, MEng, 2018.
• Computational Recognition and Comprehension of Humor in the Context of a General Error Investigation System, Ada Taylor, MEng, 2018.
• The Unified Plausibility Parser: how story-understanding systems can read stories written for humans, Emanuele Ceccarelli, MEng, 2017.
• A Commonsense Approach to Story Understanding, Bryan Michael Williams, Meng, 2017.
• Story-enabled Hypothetical Reasoning, Dylan X. Holmes, SM, 2017.
• NeverMind: An Interface for Human Memory Augmentation, Oscar Rosello, MS etc., 2017.
• Who Knows What? Perspective-Enabled Story Understanding, Jessica Marie Noss, MEng, 2017.
• Artificial Emphathy: Using Vector Space Modeling and Mixed Scope Alignment to Infer Emotional States of Characters in Stories, Ryan Cherian Alexander, MEng, 2016.
• Driving Story Generation with Learnable Character Models, Matthew Paul Fay, PhD, 2014.
• Audience Aware Computational Discourse Generation for Instruction and Persuasion, Eren Sila Sayan, MEng, 2014.
• Recognizing Actions using Embodiment & Empathy, Robert Louis McIntyre, MEng, 2014.
• Old Man Coyote Stories: Cross-Cultural Story Understanding in the Genesis Story Understanding System, Wolfgang Victor Hayden Yarlott, MEng, 2014.
• A Study in Human Attention to Guide Computational Action Recognition, Sam Sinai, MEng, 2014.
• Integrating Bottom-up and Top-Down Information, Giuliano Pezzolo Giacaglia, MEng, 2014.
• Culturally Based Story Understanding, Hiba Awad, MEng, 2013.
• Recognizing Simple Human Actions by Exploiting Regularities in Pose Sequences, Nikola Otasevic, MEng, 2013.
• Of Intent and Action: Implementing Personality Traits for Storytelling Through Concept Patterns, Susan S. Song, MEng, 2012.
• Story Retrieval and Comparison Using Concept Patterns, Caryn E. Krakauer, MEng, 2012.
• PUPIL Constructing the Space of Visual Attention, Moritz Philipp Kassner and William Rhoades Patera, MEng, 2012.
• Detecting High Level Story Elements from Low Level Data, Erik R. Speed, MEng, 2012.
• Enabling Imagination through Story Alignment, Matthew Paul Fay, MEng, 2012.
• Learning Narrative Structure from Annotated Folktales, Mark Alan Finlayson, PhD, 2011.
• A Model for Transition-Based Visiospatial Pattern Recognition, Telmo Luis Correa, Jr, MEng, 2011.
• Story Understanding in Genesis: Exploring Automatic PLot Construction through Commonsense Reasoning, Harold William Capen Low, IV, MEng, 2011.
• Disambiguating Words with Self-Organizing Maps, Martin Marcel Couturier, MEng, 2011.
• Word Sense Disambiguation Through Lattice Learning, Elyi Stickgold, MEng, 2011.
• Story Analogies with Structure Mapping and Plot Units, Jesse Dunietz, Advanced Undergraduate Project, 2011.
• Text to Text: Plot Unit Searches Generated from English, David Douglas Nackoul, MEng, 2010.
• Learning Commonsense Knowledge from the Interpretation of Individual Experiences, Glidden, MEng, 2009.
• Learning Meaning in Genesis, Cooper, MEng, 2009.
• Investigation of Cultural Biases in Human Moral Recall: A Computationally Grounded Study, Kim, MEng, 2009.
• Novelty in Goal-Oriented Machines Using a Thread Memory Structure, Gul, MEng, 2009.
• Understanding English with Lattice-Learning, Klein, MEng, 2008.
• Learning, Using Examples, to Translate Phrases and Sentences to Meanings, Kraft, MEng, 2007.
• Building Representations from Natural Language, Seifter, MEng, 2007.
• Modeling Trust in Human Conversation, Miller, MEng, 2006.
• Container Trajectories, Dominik Rabiej, Advanced Undergraduate Project, 2006.
• Learning Commonsense Categorical Knowledge in a Thread Memory System, Stamatoiu, MEng, 2004.
• Self-Organizing Event Maps, Seth Ronald Tardiff, MEng, 2004.
• Representing and Analyzing Temporal Complexity in Children's Story Books, Madleina Scheidegger, MEng, 2004.
• Intrinsic Representation: Bootstrapping Symbols from Experience, Stephen David Larson, MEng, 2003.
• Barnyard Politics: A Decision Rationale Representation for the Analysis of Simple Political Situations, Arian Shadadi, MEng, 2003.
• Bidirectional Natural Language Parsing using Streams and Counterstreams, Keith A Bonawitz, MEng, 2003.
• Connecting Language and Vision Using a Conceptual Semantics, John R. Bender, MEng, 2001
• Generalize and Sift as a Model of Inflection Acquisition, Ramond A. Molnar, MEng, 2001

Theses on Unrelated topics

• On Boosting and Noisy Labels, Chan, MEng, 2014.
• Assessing the Benefits of DCT Compressive Sensing for Computational Electromagnetics, D'Ambrosio, MEng, 2011.
• Propagation Networks, A Flexible and Expressive Substrate for Computation, Alexey Radul, PhD, 2009.
• Authorship Attribution using Lexical Semantics, Corey Gerritsen, MEng, 2003.
• Using Analogy to Aquire Commonsense Knowledge from Human Contributors, Timothy Chklovski, PhD, 2003.
• A decision rationale management system: capturing, reusing, and managing the reasons for decisions, Jintae Lee, PhD, 1992.