Intelligent Narrative Technologies: Papers from the 2013 AIIDE Workshop (WS-13-21) A Conceptual Blending Approach to the Generation of Cognitive Scripts for Interactive Narrative Justin Permar and Brian Magerko fjpermar, [email protected] Georgia Institute of Technology 225 North Ave NW Atlanta, GA 30332 USA Abstract prehension, here we focus on an explicitly temporal repre- sentation of activities. Our method of modeling a process This paper presents a computational approach to the that produces script blends from input scripts is based on the generation of cognitive scripts employed in freeform theory of conceptual blending. activities such as pretend play. Pretend play activities involve a high degree of improvisational narrative con- Conceptual blending (also known as “conceptual integra- struction using cognitive scripts acquired from everyday tion theory”) has been proposed as a basic cognitive op- experience, cultural experiences, and previous play ex- eration underlying our capacity for creativity as well as periences. Our computational model of cognitive script mundane meaning-making (Fauconnier and Turner 2003). generation, based upon conceptual integration theory, Pretend play is a narrative domain where numerous cre- applies operations to familiar scripts to generate new ative feats regularly appear, including the use of real ob- blended scripts. jects as pretend objects, diegetic and extra-diegetic con- text switches, and script blending, such as playing cars and 1 Introduction Godzilla wrecking the city. However, script blending is cer- tainly not confined to pretend play, but instead occurs perva- People regularly engage in social activities that conform to sively throughout creative works. Music composition, film cognitive scripts, which are temporally ordered sequences pastiche and narrative sub-genres are all examples of art of actions and events that represent a narrative experience works created from multiple sources. (Schank and Abelson 1977). Our everyday experiences of- Conceptual blending is a theoretical abstraction encom- ten involve modifications to cognitive scripts, where features passing a group of related cognitive processes, including from multiple scripts are meshed together to help us make analogical reasoning, mental modeling, and similarity (Fau- sense of the world or create an interesting new narrative ex- connier and Turner 1998). Fauconnier and Turner’s net- perience. For example, children pretend play as heroes or work model of conceptual blending involves four concep- villains based upon situations seen in superhero films. Al- tual spaces: a generic space, two inputs spaces, and a blend though this narrative process is ubiquitous, formally under- (output) space. In addition to the overall structure of these standing how to combine narrative scripts to elicit new, cre- spaces, conceptual blending also proposes standard pro- ative scripts is an unsolved problem (Magerko et al. 2009). cesses that occur for the purposes of “on-line, dynamical By better understanding how to accomplish this, we can in- cognitive work” (Fauconnier and Turner 1998). This net- form approaches to computational creativity that deal with work model is a generalization of many cognitive processes narrative understanding and generation. that produce an output derived from two inputs, such as a Our work addresses the generation of unique scripts given process of script generation using two familiar input scripts. two input scripts from an agent’s background knowledge in Conceptual blending processes that are relevant to script the narrative domain of pretend play. Playing pretend, as a generation include 1) the cross-space mapping of counter- creative act, frequently necessitates the generation of scripts part connections between input spaces and 2) selective pro- that are blended from cultural references, past play experi- jection from the input spaces to the blend space. A key result ences, and common cultural scripts (Moran, John-Steiner, of blending is that the composition of elements (in the blend and Sawyer 2003). In this paper, we present an algorithm space) from the input spaces results in relations not present that generates new “script blends”, represented as scripts. in either input space. This capability can be used, for example, by an AI agent to execute scripts that are not defined a priori. Similarly, To date, research related to conceptual blending, which an agent could use this capability to aid script recognition we review in section 2, has mostly focused on metaphor and during pretend play activities. Although scripts consist of analogy. Some research has successfully utilized concep- causal and temporal links, both of which are critical to com- tual blending for other purposes, including interactive po- etry generation with Harrell’s GRIOT system and the work Copyright c 2013, Association for the Advancement of Artificial by Li et al. on the generation of fantastical and imaginary Intelligence (www.aaai.org). All rights reserved. pretend play objects (Harrell 2005; Li et al. 2012). How- 44 ever, no work has yet applied conceptual blending to cogni- that treats mappings as exclusively one-to-one. Our algo- tive structures that explicitly represent temporality, such as rithm mirrors later work that only uses one-to-one mappings. scripts. Our primary contribution is an initial algorithm that modifies two familiar scripts to produce a blended script, a Computational Models of Generative Processes capability that can inform generalized approaches to com- Holyoak and Thagard built ACME to produce analogies, putational generation of creative narratives. which at its core is a problem of determining suitable cor- This paper is outlined as follows. In section 2, we re- respondences between two inputs (1989). Holyoak and view related work in conceptual blending and computational Thagard describe categories of constraints that must be re- models of generative processes. In section 3, we present our spected in order to derive analogies, including 1) structural, script generation algorithm alongside a motivating example 2) semantic, and 3) pragmatic constraints. Structural con- in order to ground the remainder of the paper. In section 4, straints specify that an isomorphism between inputs is valid we present two additional examples illustrating details of our if, and only if, the mapping is one-to-one for any objects algorithm and approach. Lastly, in section 5 we conclude and relations (see (Holyoak and Thagard 1989) for formal with a critical discussion of our current approach, highlight definitions). They use a matching process that can estab- limitations of our algorithm, and present future work. lish mappings between relations when only the number of arguments matches, regardless of order. Our approach uses 2 Related Work a matching process that uses both the number and order of Conceptual Blending arguments, but slightly relaxes the requirements from exact matches for all arguments to exact matches on all arguments Zook et al. propose a formal model of pretend object play except one. based on a two-space model of conceptual blending (Zook, Falkenhainer et al. developed SME as an implementation Magerko, and Riedl 2011). The model contains a concep- of analogical reasoning based upon Gentner’s structure map- tual space comprised of real objects in a real domain and ping theory (Falkenhainer, Forbus, and Gentner 1989). SME a second conceptual space comprised of imaginary objects is an implementation of analogy based on structural similar- in a pretend domain. They propose a process in which the ity between two representations (of situations). Structural last step blends attributes of real and pretend objects to pro- similarity is achieved by aligning elements of two (simi- duce a blended object, such as a pretend sword. Both our lar) representations using two primary constraints: 1) one- work and the work by Zook et al. employ a process involv- to-one mapping, and 2) parallel connectivity (retaining the ing substitution operations during blending, but their work order of arguments for a predicate). SME, as pointed out does not involve inputs with a temporal dimension, such as by (Holyoak and Thagard 1989) requires an exact match of scripts, which necessitates a significantly different approach predicates (part of the predicate calculus representation fa- to counterpart mapping. vored by Forbus et al.) during mapping. Closely related to Veale et al. provide a computational model of concep- SME is MAC/FAC, where the MAC stage uses a computa- tual blending called Sapper that uses a semantic network to tionally inexpensive non-structural filter to reduce compu- model concepts and relations between concepts and a pro- tational complexity (Forbus, Gentner, and Law 1995). Our cess of spreading activation to explore the conceptual do- approach is closely related to SME, as we use the notions of main (Veale, O’Donoghue, and Keane 2000). Their goal predicate matching and parallel connectivity to assert equiv- during counterpart mapping is to establish a sub-graph iso- alence. A key difference is our use of path representations morphism between the two input spaces, which allows “pro- during structural alignment. jection” of mapped properties between objects. Veale et al. base the means for establishing the sub-graph isomor- phism on a structural mapping that is derived from Gentner’s 3 Script Blending Algorithm structure-mapping theory of analogy and the property of sys- In order to guide this section, we present an