Within the realm of computational story generation sits Minstrel, a decades old system which was once used to explore the idea that, under the correct conditions, novel stories can be generated by taking an existing story and replacing some of its elements with similar ones found in a different story. This concept would eventually fall within the bounds of a strategy known as Case-Based Reasoning (CBR), in which problems are solved by recalling solutions to past problems (the cases), and mutating the recalled cases in order to create an appropriate solution to the current problem. This dissertation uses a rational reconstruction of Minstrel called Minstrel Remixed, a handful of upgraded variants of Minstrel Remixed, and a pair of similar but unrelated storytelling systems, to explore various characteristics of Minstrel-style storytelling systems.
In the first part of this dissertation I define the class of storytelling systems that are similar to Minstrel. This definition allows me to compare the features of these systems and discuss the various strengths and weaknesses of the variants. Furthermore, I briefly describe the rational reconstruction of Minstrel and then provide a detailed overview of the inner workings of the resulting system, Minstrel Remixed.
Once Minstrel Remixed was complete, I chose to upgrade it in order to explore the set of stories that it could produced and ways to alter or reconfigure the system with the goal of intentionally influencing the set of possible outputs. This investigation resulted in two new storytelling systems called Conspiracy Forever and Problem Planets. The second portion of this dissertation discusses these systems as well as a number of discoveries about the strengths and weaknesses of Minstrel Style Storytelling Systems in general. More specifically, I discuss that, 1) a human reader's capacity for creating patterns out of an assortment of statements is incredibly useful and output should be crafted to use this potential, 2) Minstrel-Style Storytelling tends to be amnesiac and do a poor job of creating long stories that remain cohesive, and 3) the domain that a storytelling system is working from is incredibly important and must be well engineered. I continue by discussing the methods that I discovered for cleaning up and maintaining a domain and conclude with a section covering interviews with other storytelling system creators about the strengths and weaknesses of their systems in light of my findings about Minstrel Remixed.
In the final portion of this document I create a framework of six interrelated attributes of stories (length, coherence, creativity, complexity, contextuality, and consolidation,) and use this along with the learning discussed in the first two portions of the dissertation to discuss the strengths and weaknesses of this class of CBR systems when applied to both static story generation and interactive storytelling. I discuss the finding that these systems seem to have some amount of power and although they can be tweaked to produce for example, longer or more consolidated stories, these improvements always come along with a reduction in complexity, coherence, or one of the other attributes. Further discussion of the output power of this class of storytelling systems revolves around the primary limiting factor to their potential, namely the fact that they have no understanding of the symbols and patterns that they are manipulating. Finally, I introduce a number of strategies that I found to be fruitful for increasing the 'output power' of the system and getting around the lack of commonsense reasoning, chiefly improving the domain and adding new subsystems.
|Commitee:||Ferguson, William, Mateas, Michael|
|School:||University of California, Santa Cruz|
|School Location:||United States -- California|
|Source:||DAI-B 80/06(E), Dissertation Abstracts International|
|Subjects:||Artificial intelligence, Computer science|
|Keywords:||Artificial intelligence, Case-based reasoning, Computational creativity, Interactive storytelling, Story generation|
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