MEXICA: A computer model of a cognitive account of creative writing

By R. P. Y. Pérez, Mike Sharples. Journal of experimental and theoretical artificial intelligence (Print), 2001

301 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Developmental and Educational Psychology

View Paper: https://www.semanticscholar.org/paper/ea275de9890489706bc21b533b44843b134bad4d

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In Developmental and Educational Psychology

Citations in the corpus

Narrative Planning: Balancing Plot and Character (2010, background): “Méxica (Pérez y Pérez & Sharples, 2001) implements a model of creative writing (cf., Sharples, 1999) that conceptualizes writing as a cycle of cognitive engagement and reflection.”
Event Representations for Automated Story Generation with Deep Neural Nets (2017, methodology): “Early attempts relied on symbolic planning (Meehan 1976; Lebowitz 1987; P´erez y P´erez and Sharples 2001; Riedl and Young 2010) or case-based reasoning using ontologies (Gerv´as et al. 2005).”
Story Generation from Sequence of Independent Short Descriptions (2017, background): “In an aempt to add engagement, reections and make stories user centric, more sensitive models modifying stories with human feedback were proposed [3, 27].”
Toward Automated Story Generation with Markov Chain Monte Carlo Methods and Deep Neural Networks (2021, background): “…generation techniques include symbolic and logical planning (Meehan 1977; Lebowitz 1987; Cavazza, Charles, and Mead 2002; Porteous and Cavazza 2009; P´erez y P´erez and Sharples 2001; Riedl and Young 2010; Martens et al. 2014; Farrell and Ware 2016) and case-based reasoning (Turner 1994; Gerv´as…”
Towards Controllable Story Generation (2018, methodology): “Early attempts in this ﬁeld relied on symbolic planning (Meehan, 1977; Lebowitz, 1987; Turner, 1993; Bringsjord and Ferrucci, 1999; Perez and Sharples, 2001; Riedl and Young, 2010), case-based reasoning (Gervas et al., 2005), or generalizing knowledge from existing stories to assemble new ones…”
Controllable Neural Story Generation via Reinforcement Learning (2018, background): “C L ] 2 7 Se p 20 18 Ware and Young 2011) or case-based reasoning (Pérez y Pérez and Sharples 2001; Gervás et al. 2005).”
Toward Vignette-Based Story Generation for Drama Management Systems (2007, background): “Minstrel [22], ProtoPropp [5], and Méxica [13] use forms of case-based reasoning approaches to”
Controllable Neural Story Plot Generation via Reinforcement Learning (2018, background): “, 2002; Porteous and Cavazza, 2009; Riedl and Young, 2010; Ware and Young, 2011] or case-based reasoning [Pérez y Pérez and Sharples, 2001; Gervás et al., 2005].”
Goal-Directed Story Generation: Augmenting Generative Language Models with Reinforcement Learning (2021, background): “Early work on story generation used planning (Meehan, 1976; Lebowitz, 1987; Cavazza et al., 2003; Porteous and Cavazza, 2009; Riedl and Young, 2010; Ware and Young, 2010) or case-based reasoning (Pérez y Pérez and Sharples, 2001; Peinado and Gervás, 2005; Turner, 2014).”
Plan-And-Write: Towards Better Automatic Storytelling (2018, unknown): “Prior research on story generation mostly focused on automatically composing a sequence of events that can be told as a story by plot planning (Lebowitz 1987; Perez and Sharples 2001; Porteous and Cavazza 2009; Riedl and Young 2010; Li et al. 2013) or case-based reasoning (Turner 1994; Gervas et al.”