@conference {3636, title = {Character-building stories}, booktitle = {Advances in Cognitive Systems}, year = {2017}, month = {05/2017}, address = {Troy, NY}, abstract = {

We argue that story understanding mechanisms provide a foundation for modeling aspects of our ability to reason hypothetically. We first note that story understanding mechanisms enable us to answer what-if questions about what would happen if an event did or did not occur, and we note that story understanding enables us to answer what-if questions about how a story would be interpreted from a different cultural perspective. We then advance a theory of how humans use hypothetical reasoning to think about personality traits. Our theory and implementation describe how humans use past behavior and untapped alternatives to build a model of characters{\textquoteright} motives and constraints.
We focus on how generalizations of existing story understanding methods and concepts enable us to model this competence efficiently. In a sample story, our theory and implementation perform a complex reasoning process to decide what a character will do next based on whether the character is more like a Conformist, Thief, Opportunist, or Robin Hood archetype.

}, author = {Patrick Henry Winston and Dylan Holmes} } @article {2067, title = {Marvin L. Minsky (1927{\textendash}2016) Scientist and inventor was a visionary founder of AI}, year = {2016}, month = {03/2016}, abstract = {

In 1950, Alan Turing argued that there was no convincing reason to doubt that computers could be intelligent. Then, in the late 1950s, Marvin Minsky, John McCarthy, Herbert Simon, and Allen Newell started the work that led everyone to think of them as the founders of the field of Artificial Intelligence. Marvin Minsky, the last of the founders, died on January 24, 2016.
Minsky championed the idea that computers would someday think like we think and surpass us. He had no patience with those who doubted that computers could be intelligent at a human level or beyond. In the early days, there were many doubters, and were it not for Minsky{\textquoteright}s determined advocacy, the field of AI might have foundered...

}, author = {Patrick Henry Winston} } @conference {1870, title = {Model-based Story Summary}, booktitle = {6th Workshop on Computational Models of Narrative}, year = {2015}, month = {05/2015}, address = {Atlanta, Georgia}, abstract = {
A story summarizer benefits greatly from a reader model because a reader model enables the story summarizer to focus on delivering useful knowledge in minimal time with minimal effort. Such a summarizer can, in particular, eliminate disconnected story elements, deliver only story elements connected to conceptual content, focus on particular concepts of interest, such as revenge, and make use of our human tendency to see causal connection in adjacent sentences. Experiments with a summarizer, built on the Genesis story understanding system, demonstrate considerable compression of an 85-element pr{\'e}cis of the plot of Shakespeare{\textquoteright}s Macbeth, reducing it, for example, to the 14 elements that make it a concise summary about Pyrrhic victory. Refocusing the summarizer on regicide reduces the element count to 7, or 8\% of the original.
}, doi = {10.4230/OASIcs.CMN.2015.157}, url = {http://dx.doi.org/10.4230/OASIcs.CMN.2015.157}, author = {Patrick Henry Winston} } @article {448, title = {The Genesis Story Understanding and Story Telling System A 21st Century Step toward Artificial Intelligence.}, number = {019}, year = {2014}, month = {06/2014}, abstract = {

Story understanding is an important differentiator of human intelligence, perhaps the most important differentiator. The Genesis system was built to model and explore aspects of story understanding using simply expressed, 20-100 sentence stories drawn from sources ranging from fairy tales to Shakespeare{\textquoteright}s plays. I describe Genesis at work as it reflects on its reading, searching for concepts, reads stories with controllable allegiances and cultural biases, models personality traits, answers basic questions about why and when, notes concept onsets, anticipating trouble, calculates similarity using concepts, models question-driven interpretation, aligns similar stories for analogical reasoning, develops summaries, and tells and persuades using a reader model. I conclude with thoughts on how Genesis would describe people in pictures and video, thus engaging with the CBMM challenge problem.

}, keywords = {Visual Intelligence}, author = {Patrick Henry Winston} }