Pub Date : 1900-01-01DOI: 10.4230/OASIcs.CMN.2013.277
Alan Tapscott, Joaquim Colás, A. Moghnieh, J. Blat
Multi-authoring is currently a common practice in the field of contemporary storytelling but producing consistent stories that share a common narrative space when multiple authors are involved is not a trivial task. Inconsistencies, which are not always well-received by readers are sometimes expensive to fix. In this work we attempt to improve the consistency of stories and narrative spaces by introducing a set of rules based on a formal model. Such a model takes into account the reader’s concept of consistency in storytelling, and acts as a framework for building tools to construct stories grounded in a common narrative space with a reinforced sense of consistency. We define a model (the Setting) and deploy it through a tool (CrossTale); both based on previous research, and discuss some user evaluation, with an in-depth analysis of the results and their implications.
{"title":"Writing Consistent Stories based on Structured Multi-Authored Narrative Spaces","authors":"Alan Tapscott, Joaquim Colás, A. Moghnieh, J. Blat","doi":"10.4230/OASIcs.CMN.2013.277","DOIUrl":"https://doi.org/10.4230/OASIcs.CMN.2013.277","url":null,"abstract":"Multi-authoring is currently a common practice in the field of contemporary storytelling but producing consistent stories that share a common narrative space when multiple authors are involved is not a trivial task. Inconsistencies, which are not always well-received by readers are sometimes expensive to fix. In this work we attempt to improve the consistency of stories and narrative spaces by introducing a set of rules based on a formal model. Such a model takes into account the reader’s concept of consistency in storytelling, and acts as a framework for building tools to construct stories grounded in a common narrative space with a reinforced sense of consistency. We define a model (the Setting) and deploy it through a tool (CrossTale); both based on previous research, and discuss some user evaluation, with an in-depth analysis of the results and their implications.","PeriodicalId":311534,"journal":{"name":"Workshop on Computational Models of Narrative","volume":"156 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130035306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4230/OASIcs.CMN.2015.152
David R. Winer, Adam Amos-Binks, Camille Barot, R. Young
The temporal order in which story events are presented in discourse can greatly impact how readers experience narrative; however, it remains unclear how narrative systems can leverage temporal order to affect comprehension and experience. We define structural properties of discourse which provide a basis for computational narratologists to reason about good timing, such as when readers learn about event relationships.
{"title":"Good Timing for Computational Models of Narrative Discourse","authors":"David R. Winer, Adam Amos-Binks, Camille Barot, R. Young","doi":"10.4230/OASIcs.CMN.2015.152","DOIUrl":"https://doi.org/10.4230/OASIcs.CMN.2015.152","url":null,"abstract":"The temporal order in which story events are presented in discourse can greatly impact how readers experience narrative; however, it remains unclear how narrative systems can leverage temporal order to affect comprehension and experience. We define structural properties of discourse which provide a basis for computational narratologists to reason about good timing, such as when readers learn about event relationships.","PeriodicalId":311534,"journal":{"name":"Workshop on Computational Models of Narrative","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132731676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4230/OASIcs.CMN.2014.2
Sohail A. Shaikh, R. Payne
To counter the threat posed by adversary information activities, the U.S. Army has developed a new warfighting function, "engagement" which will institutionalize lessons learned over the past decade of warfare. Like mission command, sustainment, intelligence, or other warfighting functions that are critical to the successful prosecution of warfare, the ability to engage a population in a way that is credible, logical and emotional to people is far more likely to compel them to the national will than lethal options. The military as a whole, and more specifically the strategic land forces (consisting of the Army, Marine Corps and U.S. Special Operations Command), are now in the process of determining the best way to implement engagement as a full-fledged function of strategic landpower. This paper will make the case that narrative is one of the key elements of engagement. The past ten years of conflict in Iraq and Afghanistan have taught the U.S. military that future wars of the 21st century will be characterized by low intensity conflicts in increasingly complex environments. In spite of the U.S. military's preponderance of power and overwhelming ability to dominate an adversary in traditional maneuver warfare, resilient insurgencies have demonstrated their potential to successfully conduct asymmetric warfare. This has proven successful, at least in the near term, when employed against U.S. and coalition forces. While the military has consistently fulfilled its responsibility to defeat the enemy's conventional forces and seize, occupy and defend land areas, it has not been as successful in the war of ideologies. We will outline how narrative should align to the military decision making process, and give an example of a successful narrative operation (Voices of Moderate Islam) that can serve as vignette for demonstrating how to conduct a narrative in U.S. led operations. We also make the case for greater academic focus on the topic of narrative in a military context: The acceptance of "engagement" as a function of warfare is still premature so a close cooperation is necessary between the military and academic disciplines that study narrative. Collaborative partnerships with academia will be critical. Finally, we argue that the doctrinal institutionalization of narrative as part of the military decision making process (MDMP) will enable military commanders to effectively achieve the desired goals of national policy.
{"title":"Narrative in the Operations Process (Invited Paper)","authors":"Sohail A. Shaikh, R. Payne","doi":"10.4230/OASIcs.CMN.2014.2","DOIUrl":"https://doi.org/10.4230/OASIcs.CMN.2014.2","url":null,"abstract":"To counter the threat posed by adversary information activities, the U.S. Army has developed a new warfighting function, \"engagement\" which will institutionalize lessons learned over the past decade of warfare. Like mission command, sustainment, intelligence, or other warfighting functions that are critical to the successful prosecution of warfare, the ability to engage a population in a way that is credible, logical and emotional to people is far more likely to compel them to the national will than lethal options. The military as a whole, and more specifically the strategic land forces (consisting of the Army, Marine Corps and U.S. Special Operations Command), are now in the process of determining the best way to implement engagement as a full-fledged function of strategic landpower. This paper will make the case that narrative is one of the key elements of engagement. \u0000 \u0000The past ten years of conflict in Iraq and Afghanistan have taught the U.S. military that future wars of the 21st century will be characterized by low intensity conflicts in increasingly complex environments. In spite of the U.S. military's preponderance of power and overwhelming ability to dominate an adversary in traditional maneuver warfare, resilient insurgencies have demonstrated their potential to successfully conduct asymmetric warfare. This has proven successful, at least in the near term, when employed against U.S. and coalition forces. While the military has consistently fulfilled its responsibility to defeat the enemy's conventional forces and seize, occupy and defend land areas, it has not been as successful in the war of ideologies. We will outline how narrative should align to the military decision making process, and give an example of a successful narrative operation (Voices of Moderate Islam) that can serve as vignette for demonstrating how to conduct a narrative in U.S. led operations. We also make the case for greater academic focus on the topic of narrative in a military context: The acceptance of \"engagement\" as a function of warfare is still premature so a close cooperation is necessary between the military and academic disciplines that study narrative. Collaborative partnerships with academia will be critical. Finally, we argue that the doctrinal institutionalization of narrative as part of the military decision making process (MDMP) will enable military commanders to effectively achieve the desired goals of national policy.","PeriodicalId":311534,"journal":{"name":"Workshop on Computational Models of Narrative","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134424866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4230/OASIcs.CMN.2014.88
S. Kiss, Z. Jakab
In this paper we first offer a task analysis of the false belief test including the bidirectional relationship between mindreading and language. Following this we present our theory concerning Quinian bootstrapping of the meaning of mental state terms and relate it to the task-analytic framework. Finally we present an experiment on ascribing privileged access through minimal narratives which is intended to serve as a test of our theory.
{"title":"Mindreading, Privileged Access and Understanding Narratives","authors":"S. Kiss, Z. Jakab","doi":"10.4230/OASIcs.CMN.2014.88","DOIUrl":"https://doi.org/10.4230/OASIcs.CMN.2014.88","url":null,"abstract":"In this paper we first offer a task analysis of the false belief test including the bidirectional relationship between mindreading and language. Following this we present our theory concerning Quinian bootstrapping of the meaning of mental state terms and relate it to the task-analytic framework. Finally we present an experiment on ascribing privileged access through minimal narratives which is intended to serve as a test of our theory.","PeriodicalId":311534,"journal":{"name":"Workshop on Computational Models of Narrative","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127982562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4230/OASIcs.CMN.2013.198
Kenji Sagae, A. Gordon, Morteza Dehghani, Michael Metke, Jackie S. Kim, Sarah I. Gimbel, C. Tipper, J. Kaplan, Mary Helen Immordino‐Yang
Personal narratives typically involve a narrator who participates in a sequence of events in the past. The narrator is therefore present at two narrative levels: (1) the extradiegetic level, where the act of narration takes place, with the narrator addressing an audience directly; and (2) the diegetic level, where the events in the story take place, with the narrator as a participant (usually the protagonist). Although story understanding is commonly associated with semantics of the diegetic level (i.e., understanding the events that take place within the story), personal narratives may also contain important information at the extradiegetic level that frames the narrated events and is crucial for capturing the narrator’s intent. We present a data-driven modeling approach that learns to identify subjective passages that express mental and emotional states of the narrator, placing them at either the diegetic or extradiegetic level. We describe an experiment where we used narratives from personal weblog posts to measure the effectiveness of our approach across various topics in this narrative genre.
{"title":"A Data-Driven Approach for Classification of Subjectivity in Personal Narratives","authors":"Kenji Sagae, A. Gordon, Morteza Dehghani, Michael Metke, Jackie S. Kim, Sarah I. Gimbel, C. Tipper, J. Kaplan, Mary Helen Immordino‐Yang","doi":"10.4230/OASIcs.CMN.2013.198","DOIUrl":"https://doi.org/10.4230/OASIcs.CMN.2013.198","url":null,"abstract":"Personal narratives typically involve a narrator who participates in a sequence of events in the past. The narrator is therefore present at two narrative levels: (1) the extradiegetic level, where the act of narration takes place, with the narrator addressing an audience directly; and (2) the diegetic level, where the events in the story take place, with the narrator as a participant (usually the protagonist). Although story understanding is commonly associated with semantics of the diegetic level (i.e., understanding the events that take place within the story), personal narratives may also contain important information at the extradiegetic level that frames the narrated events and is crucial for capturing the narrator’s intent. We present a data-driven modeling approach that learns to identify subjective passages that express mental and emotional states of the narrator, placing them at either the diegetic or extradiegetic level. We describe an experiment where we used narratives from personal weblog posts to measure the effectiveness of our approach across various topics in this narrative genre.","PeriodicalId":311534,"journal":{"name":"Workshop on Computational Models of Narrative","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115335291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4230/OASIcs.CMN.2016.1
J. Bateman
Models of narrative have been proposed from many perspectives and most of these nowadays promote further the notion that narrative is a transmedial phenomenon: i.e., stories can be told making use of distinct and multiple forms of expressions. This raises a range of theoretical and practical questions, as well as rendering the task of providing computational models of narrative both more interesting and more challenging. Central to this endeavour are issues concerned with the potential mutual conditioning of narrative forms and the media employed. Methods are required for isolating narrative properties and mechanisms that may be generalised across media, while at the same time appropriately respecting differences in medial affordances. In this discussion paper I set out a corresponding approach to characterising narrative that draws on a fine-grained formal characterisation of multimodal discourse developed on the basis of both functional and formal linguistic models of discourse, generalised to the multimodal case. After briefly setting out the theoretical principles on which the account builds, I position narrative with respect to the framework and give an example of how audiovisual narratives such as film are accounted for. It will be suggested that a common anchoring in a well specified notion of discourse as an intrinsically multimodal phenomenon offers beneficial new angles on how narratives can be modelled, as well as establishing bridges between humanistic understandings of narrative and complementary computational accounts of narratives involving communicative goal-based planning.
{"title":"From Narrative to Visual Narrative to Audiovisual Narrative: the Multimodal Discourse Theory Connection (Invited Talk)","authors":"J. Bateman","doi":"10.4230/OASIcs.CMN.2016.1","DOIUrl":"https://doi.org/10.4230/OASIcs.CMN.2016.1","url":null,"abstract":"Models of narrative have been proposed from many perspectives and most of these nowadays promote further the notion that narrative is a transmedial phenomenon: i.e., stories can be told making use of distinct and multiple forms of expressions. This raises a range of theoretical and practical questions, as well as rendering the task of providing computational models of narrative both more interesting and more challenging. Central to this endeavour are issues concerned with the potential mutual conditioning of narrative forms and the media employed. Methods are required for isolating narrative properties and mechanisms that may be generalised across media, while at the same time appropriately respecting differences in medial affordances. In this discussion paper I set out a corresponding approach to characterising narrative that draws on a fine-grained formal characterisation of multimodal discourse developed on the basis of both functional and formal linguistic models of discourse, generalised to the multimodal case. After briefly setting out the theoretical principles on which the account builds, I position narrative with respect to the framework and give an example of how audiovisual narratives such as film are accounted for. It will be suggested that a common anchoring in a well specified notion of discourse as an intrinsically multimodal phenomenon offers beneficial new angles on how narratives can be modelled, as well as establishing bridges between humanistic understandings of narrative and complementary computational accounts of narratives involving communicative goal-based planning.","PeriodicalId":311534,"journal":{"name":"Workshop on Computational Models of Narrative","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127375987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4230/OASIcs.CMN.2013.147
Gregory Lessard, M. Levison
We present a web-based environment - an Ethics Workbench - which allows a reader's ethical judgments to be solicited while reading a narrative. Preliminary results show generally consistent scores across subjects and test conditions, and suggest that it is possible to measure how individual readers respond to texts in terms of ethical judgments, how the linearity inherent in narrative plays a role in affecting ethical judgments, and how readers appear to synthesize judgments over the course of a text. Applications of the model include the empirical analysis of the ethical aspects of reading, the more detailed study of ethical issues, the potential for eliciting ethical discussions, and a means of dynamically planning texts to achieve maximum effect with respect to reader judgments.
{"title":"Testing Reader Ethical Judgments over the Course of a Narrative","authors":"Gregory Lessard, M. Levison","doi":"10.4230/OASIcs.CMN.2013.147","DOIUrl":"https://doi.org/10.4230/OASIcs.CMN.2013.147","url":null,"abstract":"We present a web-based environment - an Ethics Workbench - which \u0000allows a reader's ethical judgments to be solicited while reading a \u0000narrative. Preliminary results show generally consistent scores across \u0000subjects and test conditions, and suggest that it is possible to \u0000measure how individual readers respond to texts in terms of ethical \u0000judgments, how the linearity inherent in narrative plays a role in \u0000affecting ethical judgments, and how readers appear to synthesize \u0000judgments over the course of a text. Applications of the model include \u0000the empirical analysis of the ethical aspects of reading, the more \u0000detailed study of ethical issues, the potential for eliciting ethical \u0000discussions, and a means of dynamically planning texts to achieve \u0000maximum effect with respect to reader judgments.","PeriodicalId":311534,"journal":{"name":"Workshop on Computational Models of Narrative","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123471460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4230/OASIcs.CMN.2015.124
B. Miller, Ayush Shrestha, Jenn Olive, S. Gopavaram
Automated cross-document comparison of narrative facilitates co-reference and event similarity identification in the retellings of stories from different perspectives. With attention to these outcomes, we introduce a method for the unsupervised generation and comparison of graph representations of narrative texts. Composed of the entity-entity relations that appear in the events of a narrative, these graphs are represented by adjacency matrices populated with text extracted using various natural language processing tools. Graph similarity analysis techniques are then used to measure the similarity of events and the similarity of character function between stories. Designed as an automated process, our first application of this method is against a test corpus of 10 variations of the Aarne-Thompson type 333 story, "Little Red Riding Hood." Preliminary experiments correctly co-referenced differently named entities from story variations and indicated the relative similarity of events in different iterations of the tale despite their order differences. Though promising, this work in progress also indicated some incorrect correlations between dissimilar entities.
{"title":"Cross-Document Narrative Frame Alignment","authors":"B. Miller, Ayush Shrestha, Jenn Olive, S. Gopavaram","doi":"10.4230/OASIcs.CMN.2015.124","DOIUrl":"https://doi.org/10.4230/OASIcs.CMN.2015.124","url":null,"abstract":"Automated cross-document comparison of narrative facilitates co-reference and event similarity identification in the retellings of stories from different perspectives. With attention to these outcomes, we introduce a method for the unsupervised generation and comparison of graph representations of narrative texts. Composed of the entity-entity relations that appear in the events of a narrative, these graphs are represented by adjacency matrices populated with text extracted using various natural language processing tools. Graph similarity analysis techniques are then used to measure the similarity of events and the similarity of character function between stories. Designed as an automated process, our first application of this method is against a test corpus of 10 variations of the Aarne-Thompson type 333 story, \"Little Red Riding Hood.\" Preliminary experiments correctly co-referenced differently named entities from story variations and indicated the relative similarity of events in different iterations of the tale despite their order differences. Though promising, this work in progress also indicated some incorrect correlations between dissimilar entities.","PeriodicalId":311534,"journal":{"name":"Workshop on Computational Models of Narrative","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124748947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4230/OASIcs.CMN.2015.12
Fritz Breithaupt, E. Brower, Sarah Whaley
This study examines whether there is an optimal degree of eventfulness of short narratives. We ask whether there is a specific degree of eventfulness (unexpectedness) that makes them "stick" better than other stories so that they are maintained more faithfully in serial reproduction (telephone games). The result is: probably not. The finding is that there is an impressive correlation of eventfulness rankings of original stories and resulting retellings in serial reproduction, despite the change of many other story elements and almost regardless of low or high eventfulness. Put more simply, people remember and retell “eventfulness” accurately, even when the actual events and circumstances of a story are changed.
{"title":"Optimal Eventfulness of Narratives","authors":"Fritz Breithaupt, E. Brower, Sarah Whaley","doi":"10.4230/OASIcs.CMN.2015.12","DOIUrl":"https://doi.org/10.4230/OASIcs.CMN.2015.12","url":null,"abstract":"This study examines whether there is an optimal degree of eventfulness of short narratives. We ask whether there is a specific degree of eventfulness (unexpectedness) that makes them \"stick\" better than other stories so that they are maintained more faithfully in serial reproduction (telephone games). The result is: probably not. The finding is that there is an impressive correlation of eventfulness rankings of original stories and resulting retellings in serial reproduction, despite the change of many other story elements and almost regardless of low or high eventfulness. Put more simply, people remember and retell “eventfulness” accurately, even when the actual events and circumstances of a story are changed.","PeriodicalId":311534,"journal":{"name":"Workshop on Computational Models of Narrative","volume":"144 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131572436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4230/OASIcs.CMN.2015.108
M. McShane, S. Nirenburg, B. Jarrell, G. Fantry
The mental models of experts can be encoded in computational cognitive models that can support the functioning of intelligent agents. This paper compares human mental models to computational cognitive models, and explores the extent to which the latter can be acquired automatically from published sources via automatic learning by reading. It suggests that although model components can be automatically learned, published sources lack sufficient information for the compilation of fully specified models that can support sophisticated agent capabilities, such as physiological simulation and reasoning. Such models require hypotheses and educated guessing about unattested phenomena, which can be provided only by humans and are best recorded using knowledge engineering strategies. This work merges past work on cognitive modeling, agent simulation, learning by reading, and narrative structure, and draws examples from the domain of clinical medicine.
{"title":"Learning Components of Computational Models from Texts","authors":"M. McShane, S. Nirenburg, B. Jarrell, G. Fantry","doi":"10.4230/OASIcs.CMN.2015.108","DOIUrl":"https://doi.org/10.4230/OASIcs.CMN.2015.108","url":null,"abstract":"The mental models of experts can be encoded in computational cognitive models that can support the functioning of intelligent agents. This paper compares human mental models to computational cognitive models, and explores the extent to which the latter can be acquired automatically from published sources via automatic learning by reading. It suggests that although model components can be automatically learned, published sources lack sufficient information for the compilation of fully specified models that can support sophisticated agent capabilities, such as physiological simulation and reasoning. Such models require hypotheses and educated guessing about unattested phenomena, which can be provided only by humans and are best recorded using knowledge engineering strategies. This work merges past work on cognitive modeling, agent simulation, learning by reading, and narrative structure, and draws examples from the domain of clinical medicine.","PeriodicalId":311534,"journal":{"name":"Workshop on Computational Models of Narrative","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133047779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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