Goal-Driven Conceptual Blending: A Computational Approach for

Creativity

Boyang Li, Alexander Zook, Nicholas Davis and Mark O. Riedl

School of Interactive Computing, Georgia Institute of Technology, Atlanta GA, 30308 USA

{boyangli, a.zook, ndavis35, riedl}@gatech.edu

Abstract

Conceptual blending has been proposed as a creative

cognitive process, but most theories focus on the

analysis of existing blends rather than mechanisms for

the efficient construction of novel blends. While

conceptual blending is a powerful model for creativity,

there are many challenges related to the computational

application of blending. Inspired by recent theoretical

research, we argue that contexts and context-induced

goals provide insights into algorithm design for creative

systems using conceptual blending. We present two

case studies of creative systems that use goals and

contexts to efficiently produce novel, creative artifacts

in the domains of story generation and virtual

characters engaged in pretend play respectively.

Introduction

Conceptual blending has been proposed as a fundamental

cognitive process, responsible for the creation of a broad

range of creative artifacts. (Fauconnier and Turner 1998,

2002; Grady 2000; Hutchins 2005). Fauconnier and Turner

(1998, 2002) proposed that conceptual blending involves

the merger of two or more input spaces into a blended

space. A griffin, for example, may be considered as a blend

of an eagle and a lion. Each of the input spaces contains a

number of concepts and their inter-connections. These

concepts are selectively merged and projected into the

blend space. After that, additional structures may emerge

in the blend by pattern completion and further elaboration.

Among artifacts created by conceptual blending, we

distinguish two types of blends, namely semiotic

expressions and standalone concepts. Semiotic expressions

are used in communication to highlight certain aspects of

or shed light on one of the input spaces. An often discussed

semiotic expression is "this surgeon is a butcher" (cf.

Grady, Oakley, and Coulson 1999; Brandt and Brandt

2002; Veale and O'Donoghue 2000). The input spaces of

the expression are the conceptual spaces of the surgeon and

the butcher respectively. The surgeon in the blend

possesses the brutal attitude of the butcher, forming a

criticism of the surgeon.

This paper focuses on the construction of the second

type of blend, which we call standalone concepts. An

example is the lightsaber from Star Wars: a lightsaber

blends together a sword and a laser emitter, but it is an

independent concept that does not inform hearers about the

properties of swords or laser emitters. During blend

creation, contents are still projected from the input spaces

into the blend, but the blend is not meant to convey

information about the input spaces.

We share the belief that theories of creativity should be

computable (Johnson-Laird 2002), yet most accounts of

blending focus on analyses of existing blends and have not

fully described how novel blends are constructed

cognitively or algorithmically. In particular, three key

procedures required for blending lack sufficient details

necessary for efficient computation: (1) the selection of

input spaces, (2) the selective projection of elements of

input spaces into the blend, and (3) the stopping criteria for

blend elaboration. Inefficiencies in these procedures can

lead to significant difficulties in finding appropriate blends

and elaborations. For example, a simplistic algorithm may

produce all possible combinations of elements from all

input spaces, resulting in a combinatorial explosion of

possible blends.

We argue that these three main procedures must

algorithmically make use of the context and goals of the

blend being constructed. Brandt and Brandt (2002)

proposed communication contexts and goals as the driving

force behind the three key procedures, but their analysis is

limited to semiotic expressions. We extend their theory to

the construction of novel standalone concepts and provide

computational justifications through two case studies of

working computational systems in the domains of story

generation and pretend play. Our systems construct blends

in a goal-driven and context-driven manner. As integral

aspects of the conceptual blending process, contexts and

goals provide concrete computational benefits by pruning

search spaces and improving average-case performance.

The Theories of Conceptual Blending

This section reviews the theories of conceptual blending

described by Fauconnier and Turner (1998, 2002) and

Brandt and Brandt (2002). We compare these two accounts

side by side and identify some underspecified parts in the

theories, which a working system must address.

In the original blending theory (BT) by Fauconnier and

Turner (1998, 2002), conceptual blending takes two or

more mental spaces as inputs. Mental spaces are

dynamically constructed during a discourse (e.g.

conversation) to contain relevant concepts. The input space

of the surgeon, for example, includes the surgeon and

relevant entities, such as his scalpel, patient and so on.

Elements in one input space are then mapped to their

counterparts in another input space, using mapping rules

such as identity or analogy. In the surgeon-as-butcher

example, the cleaver of the butcher is mapped to the

scalpel of the surgeon; the dead animal is mapped to the

patient, and so forth. Elements from input spaces are

selectively projected into a blend space. The generic space

captures the structural similarities between input spaces. In

addition to elements projected from input spaces, the blend

space can also contain emergent structures created by

pattern completion or elaboration. This four-space

formulation is shown in Figure 1, where big circles denote

mental spaces, black dots represent elements in the spaces,

solid lines are the mappings between inputs, and dashed

lines denote correspondences among the elements in the

four spaces. Hollow dots denote emergent structures in the

blend.

Fauconnier and Turner, however, did not specify how

elements from these input spaces could be chosen during

the selective projection. Although eight optimality

principles—human scale, topology, pattern completion,

integration, vital relations, unpacking, web, and

relevance—were proposed as quality measures, they can

only evaluate the quality of a complete blend after it is

constructed. This suggests a computational approach where

all possible blends have to be generated and tested

individually, called a neo-Darwinian algorithm by

Johnson-Laird (2002). A neo-Darwinian algorithm could

lead to a combinational explosion of options and is

infeasible for large input spaces. In contrast, what Johnson-

Laird calls a neo-Lamarckian approach generates only

valuable products by applying quality constraints on the

search space. To do so in blending requires a mechanism

that selects elements from input spaces effectively during

the generation process. Note that projection occurs after

inter-space mappings are built, so the complexity of

analogy making should not be conflated with the

complexity of projection.

Moreover, BT does not provide detailed procedures for

the effective retrieval of input spaces, nor for the

elaboration of blends. A full computational implementation

of the blending theory should select input spaces by itself,

rather than assume them as given. To create a powerful

criticism of the surgeon, a system should decide to blend it

with a butcher, rather than a driver or a school teacher. A

creative system may possess a huge amount of knowledge,

so an efficient selection procedure for input spaces is

necessary for it to operate within reasonable time limits.

The same argument goes for elaboration. Neither a human

nor a computational system should elaborate a blend

endlessly. We usually do not wish to simulate the entire

world’s reaction to an irresponsible surgeon, which will

require excessive computational power or time.

In summary, the original blending theory left much

ambiguity in three key procedures: (1) the selection of

input spaces, (2) the selection of elements for projection,

and (3) the stopping criterion for blend elaboration.

However, a complete working implementation of BT must

contain these procedures.

Context-Dependent Blending

Brandt and Brandt (2002) pointed out that blends used in

communication do not have fixed meanings. Rather, they

are real-world phenomena that can only be analyzed in the

context of the discourse during which they were uttered.

Under different circumstances, the same utterance “this

surgeon is a butcher” can mean different things. For

example, if a soldier referred to a battlefield medic as a

butcher, he may be highlighting the fact that he has to

perform an astonishing number of amputations. This

Figure 1. The four-space blending theory, adapted from

Fauconnier & Turner (2002).

Figure 2. The context-dependent blending theory, adapted from

Brandt and Brandt (2002).

interpretation is vastly different than the ethical judgment

about a surgeon’s skills after a failed surgery.

To account for this plurality of meanings, blend

construction must not be based solely on the attributes of

the input domains, but on the context. Hence, Brandt and

Brandt abandoned the context-free generic space and

proposed a context-driven blend construction process. A

simplified version of Brandt and Brandt’s theory is shown

in Figure 2, where the dashed arrow indicates that the

situational relevance prompts the retrieval of the two input

spaces in order and solid arrows represent selective

projection. First, the communicative situation (context)

retrieves the input spaces in order. The situation implies a

first input space, the reference space based in the actual

real-world situation. For example, if we are talking about a

particular surgery, the input space of the surgeon becomes

available. Based on the goal of communication (e.g.

conveying that the surgeon is irresponsible), a presentation

space including the figurative entities (e.g. a butcher) is

then retrieved and mapped to the reference space. Second,

the goal of communication, captured through the

situational relevance, determines what elements from the

two spaces are projected into the blend. If we want to

accuse the surgeon of being irresponsible, we should

project the careless attitude of the butcher into the blend.

Third, elaboration of blends also depends on the context

and the goal, fleshing out a blended space until sufficient

detail is achieved for the guiding goal. In summary, the

sequential retrieval of input spaces, the selection of

projected elements, and the stopping criteria are all driven

by contexts and goals.

Blending Novel Concepts

In addition to metaphorical expressions, blending also

yields novel concepts independent of the input spaces, such

as the lightsaber. We believe the two cases differ in the

relationship between the inputs spaces and the blend.

Blends used in communication are usually meant to

underscore certain aspects of, or to attach new properties to

an input space. In our example, the surgeon who possesses

a butcher’s attitude in the blend space becomes a criticism

of the surgeon in the input. The linkage between the blend

and the input spaces is essential for understanding.

In contrast, standalone concepts are blends that are not

meant to convey meaning about their input spaces. For

example, the birth stork is a blend of the metaphor birth-is-

arrival, air travel, and the stork (Fauconnier and Turner

2002, ch. 14), but it does not tell us much about air travel

or storks in general. As another example, the lightsaber in

Star Wars is clearly a blend of a sword and a laser emitter,

but it is not meant to tell us anything about swords or laser,

even though understanding laser and swords can help us

understand lightsabers. There are links going from the

input spaces to the blend spaces, but not vice versa.

Standalone concepts created from blending are

commonly seen in stories and story-related activities.

Dragons, for example, possess features of snakes, large

cats and birds of prey, and the instinctive fear of the three

is postulated to be its origin (Jones 2002). Many other

mythical creatures are combinations of common animals.

Novel concepts created from blending also appear in

modern science fiction. The popular Japanese sci-fi manga

Doraemon (Fujio 1974-1996) contains several gadgets

made this way, such as a toy telephone that can transmit flu

instead of voice.

The idea of an independent concept does not contradict

the notion of contextualized meaning. The meaning of a

novel concept, when used as a semiotic sign, can still

change depending on the context. One may say “my new

kitchen knife is a lightsaber” to emphasize its sharpness. In

Brandt and Brandt’s framework, this meaning is created

from the blend of the kitchen knife and the lightsaber,

which is a different blend than the lightsaber itself. The

concept lightsaber, on the other hand, is as independent as

the concept butcher. For another example, the birth stork is

now often a humorous symbol for baby births rather than

used to explain births. The contexts and meanings vary, but

the concepts remain relatively constant.

We extend Brandt and Brandt’s contextualized blending

theory to the construction of novel concepts. Below we

present two computational systems that generate novel

concepts in fictions and pretend play. While Brandt and

Brandt’s (2002) theory initially was not meant to explain

such blends, we show that a goal and context driven

approach can account for their generation and bring

computational benefits.

Previous Implementations

Most previous work on computational algorithms for

conceptual blending are based on the theories of

Fauconnier and Turner and thus do not fully account for

input space selection, selective projection, or blend

elaboration. It is common for computational blending

algorithms to ignore one or more of these stages. The

Alloy blending engine, as part of the Griot poem generator

(Goguen and Harrell 2004), is the earliest implementation

of BT that we are aware of. Input spaces are manually

coded as symbolic expressions. Projection into the blend is

based on a structural mapping between input spaces.

Without the guidance of goals, any element from the input

spaces may be projected. The authors found the number of

possible blends is exponential to the number of relations in

the input spaces, but did not discuss methods to prune

these spaces. Hervás et al. (2006) proposed a process that

enriches texts of stories which can be considered as

conceptual blends. They proposed that readers’ familarity

with input spaces, as part of the communication context, is

important in selecting input spaces and elements for

projection without specifying an algorithm.

Martinez et al. (2011) proposed a blending algorithm

where input spaces are sets of axioms. Compatible axioms

are selected for projection. The system does not consider

goals to directly build blends that meet specific purposes.

Rather, it "enumerates alternatives ranked by the

complexity of the underlying mappings". Yamada et al.

(2011) generate motion of dancing characters by

representing motion as wavelet equations and blending is a

weighted summation of wavelet coefficients. The process

does not involve any of the three key procedures

mentioned earlier. Thagard and Steward (2011) proposed

an implementation of blending at the neural level, which

also does not consider goals.

The Divago system (Pereira 2007) is noteworthy in that

it considers goals, but only uses them indirectly during

construction of standalone concepts. Note that the input

spaces are given to—rather than selected by—Divago. In

Divago, goals do not directly participate in the selection of

elements being projected. If an element a is mapped to b,

one of 4 things can be projected into the blend: a, b, a/b, or

nothing. This leads to a combinatorial explosion of

possible blends regardless of the complexity of finding an

inter-space analogical mapping. To effectively search an

exponentially growing space, Divago utilizes a genetic

algorithm (GA) that stochastically samples the space of

possible blends. From a population of blends, Divago first

selects those with high scores as computed by an

evaluation function, consisting of the weighted sum of the

eight optimality criteria introduced earlier. One of the

criteria, relevance, is interpreted as goal satisfaction. After

evaluation, highly ranked blends are randomly modified to

create the next population. Imitating biological evolution,

this process repeats in the hope of finding a near-optimal

blend after sufficient number of iterations. Note however

that a high score does not guarantee goal satisfaction

because the evaluation function contains several, possibly

competing criteria.

To elaborate a blend, Divago fires any production rules

whose premises are true to add content into the blend.

Divago also supplements details to the blend based on its

similarity with other frames. For example, if a blend is

similar enough to the bird frame, Divago will grant it the

ability to fly. However, given enough rules and frames,

rule firing and pattern matching can potentially go on

indefinitely, as it does not specify any explicit stopping

criteria for elaboration based on the notions of

meaningfulness or necessity.

In the light of the above analysis, it is clear that goals

and context can guide a computational conceptual blending

process for the purpose of creating novel, standalone

concepts. However, context and goals must be used

directly to ensure successful blends and to focus search

efficiently. In the next section, we study two creative

systems that utilize contexts and goals to effectively realize

the three procedures in conceptual blending.

Two Case Studies

This section presents two systems that implement blending

theory in a goal and context driven manner. We describe

these systems through the lens of the context-driven

blending theory. The first system builds fictional gadgets

in computer-generated stories. The second system

constructs objects used in pretend play that combine

features of a desired fantasy-world object with a real-world

object at hand. The two systems address the three

aforementioned problems—input space selection, selective

projection, and elaboration—in a neo-Lamarckian manner

by employing constraints introduced by the domain of

application and the specific goal to be achieved. The first

case study focuses on selective projection and elaboration.

The second case study focuses on input space selection.

Generating Gadgets in Fictions

As a vibrant research field, artificial intelligence (AI) story

generation aspires to create intelligent systems that can

create and tell novel stories. Most current approaches to

story generation are restricted to generating stories for

static, hand-authored micro-worlds, manipulating given

characters and objects to produce stories (e.g. Cavazza,

Charles, and Mead 2002; Gervás et al. 2005; Riedl and

Young 2010; Ontañón and Zhu 2010). These systems can

be likened to jigsaw puzzle solvers who play only with

given pieces and never dream of inventing a new piece.

These story generation systems are not able to tell stories

with novel objects or gadgets; they cannot tell Star Wars if

the idea of lightsaber is not supplied ahead of time.

The aim of the gadget generation algorithm (Li and

Riedl 2011a, 2011b) is to break out of these static world

configurations and create new types of objects previously

unknown to the system. Our approach was initially

presented as a combination of partial-order planning (Weld

1994) and analogical reasoning. Here we point out its

connection to conceptual blending. The algorithm blends

existing concepts to generate novel standalone concepts as

unforeseen gadgets in support of a goal derived from a

story context.

To generate a gadget, the algorithm reasons about how

the gadget should be used in the context of a story,

including events that happen immediately before and after

its usage. These events are captured as the behavior of the

gadget, represented as a temporally ordered sequence of

actions. Given a goal derived from a story (e.g., a character

must become infected by a flu virus), the algorithm

iteratively constructs the gadget’s behavior by working

backward from the goal using actions and entities from

various input spaces. Goals are first-order logic predicates

such as

infected‐by(bob, virus). An action is an

operator that requires certain predicates as preconditions

and asserts some predicates as effects. The gadget

generator works with a conventional story generator, which

supplies goals considered appropriate for a gadget to

achieve. The final behavior of the gadget must achieve

these goals.

Goals are first used to identify the input spaces. The

reference space includes objects in the goal predicates and

relevant concepts. For example, the reference space

implied by the goal

infected‐by(bob,virus)includes

concepts such as flu viruses, a character named Bob, and

actions such as coughing and curing. In fact, the reference

space exists only conceptually and is not separated from

the rest of the knowledge in the system. It highlights that

knowledge structures closely related to the goal play more

important roles in projection than the rest. The system

creates the second input space by retrieving an object from

its knowledge base of many known objects that achieve

predicates analogous to the specified goals. This object is

called the prototype for the gadget. At this time, the list of

known potential prototypes is small, and we go down the

list from the best guess. In the long term, a robust

mechanism to find the best prototype is needed (e.g.

Wolverton 1994). The second input space, the prototype

space, includes the prototype object, its behavior, as well

as actions used and entities referred to in the behavior. For

example, the behavior of a toy phone object refers to the

entity

voice, which becomes part of the space.

The behavior of the gadget, or the blend space, is built

by projecting actions from the two input spaces selectively.

This projection is driven by goals in a backward-chaining,

iterative process. Following the partial-order planning

representation, actions have causal requirements—

predicates that must be true in the micro-world for the

action to be performed (also called preconditions). When

an action is brought in the blend space to solve one goal,

its own causal requirements are added to the set of goals.

Actions continue to be projected to satisfy goals until all

goals are satisfied or determined to be fundamental

properties of the gadget itself. Note that when there are

multiple possible ways to achieve a goal, the algorithm

tries the best first and backtracks when mistakes are made.

The selective projection process is thus completely goal-

driven. Due to space constraints, we refer interested reader

to (Li and Riedl 2011b) for details of the algorithm.

There are several methods to project actions from the

input spaces into the blend space. First, the algorithm can

project an action from the prototype space without any

changes. Second, an action from either space can be

projected with arguments from both spaces, constituting a

form of blending. Figure 3(a) shows the action

Cough‐

Into being projected with arguments from both input

spaces. This action is mapped with the action

Speak‐Into

in the prototype space because of identified analogical

similarities. The analogical reasoning engine Sapper

(Veale and O'Donoghue 2000) is used to establish

analogical mapping across input spaces. Third, a special

case of blending occurs when an action from the prototype

space is combined with illegal arguments from the

reference space to create a new action previously not

allowed. As illustrated in Figure 3(b), the Transmit action

of a toy phone is now allowed to transmit

virus instead of

voice, which otherwise would not have been a legal

assignment of parameters. The algorithm ignores the rules

of the micro-world to achieve goals of an imaginary gadget

phone that can transmit flu virus from one person to

another. This allows a generated gadget to achieve the

impossible, producing an object not conceived before.

Figure 4 shows the behavior of a gadget phone

generated by the algorithm, which one can use to give her

flu to someone else and free herself from it. The goal

predicates it achieves are

infected‐by(bob,virus) and

not(infected‐by (alice, virus)). Each box

represents one action. Solid arrows denote temporal

precedence and dotted arrows denote closure actions. This

gadget appeared in the Doraemon manga. Li and Riedl

(2011a) also describes an example not from the manga.

Elaboration in the blend space is simulated with the use

of closure actions. Closure actions are not necessary for the

gadget’s goal, but restore some goal-irrelevant aspects of

the story world to an ordinary state. For example, in Figure

4, the actions where people put down the phone after use

(the

Let‐Gos) are closure actions. Closure actions are

manually labeled in prototype behaviors and projected into

the blend space. Their use is motivated by the desire to

restore an ordinary state after using the gadget, which

creates a sense of denouement in the story. Although the

elaboration is not directly driven by goals, it is motivated

by the domain of storytelling.

Finally, the gadget is verified by incorporating the

gadget behaviors into the story from which the goals were

originally derived. If any necessary conditions of the

gadget’s behavior cannot be achieved in the story, the

gadget does not work and has to be modified further by

going through additional rounds of blending. When this

happens, a second prototype is retrieved as yet another

input space and blended with the current gadget to make it

even more powerful. The algorithm cannot create two

gadgets simultaneously.

Figure 4. The behavior of the flu-transmitting gadget phone

Figure 3. Two projections involved in the generation of the flu-

transmitting gadget phone.

By using means-ends search guided by goals, the search

space of possible actions to be added to the behavior of the

gadget is pruned, resulting in improved average-case

efficiency of the algorithm. Each iteration of the algorithm

tries to satisfy a goal in the blend space, and will thus only

consider actions that can achieve that goal. While the total

number of actions in both input spaces may be large,

usually only a small fraction of them can be projected to

achieve any given goal. In contrast, a naïve selective

projection algorithm will attempt to project all actions in

any input space using all possible projection methods.

Although the goal-driven best-first search in the worst case

has to consider all possibilities in the search space, in an

average case it only considers a small portion of the total

possibilities (Weld 1994). A goal-driven blending

algorithm also has the added benefit of ensuring that any

result of the algorithm is guaranteed to meet all of the

acceptability requirements.

In summary, the gadget generation system implements a

goal-driven model of blending, including relatively

efficient selective projection (due to pruning of the search

space) and elaboration procedures. These procedures are

driven by the gadget’s goal, the particular story that serves

as the context, and the general domain of storytelling.

However, this system has not fully investigated the

selection of input spaces, which will be illustrated in the

next case study.

A Virtual Character for Pretend Play

Our second case study illustrates the use of goals in

blending with a specific focus on the selection of

appropriate input spaces. In this case, a real-world object is

selected to represent an object from a fantasy world, as

required in children’s pretend play. A goal specifies means

to appropriately prune potential input spaces and select one

option based on contextual constraints of similarity. Below

we describe how our pretend play system can be viewed

through the lens of conceptual blending; full details on the

system are presented in (Zook, Riedl, and Magerko 2011).

In pretend play, children construct and enact story

scripts and roles with real-life objects (Nourot 1998).

Examples of pretend play include lightsaber duels with

cardboard tubes, holding pretend tea parties with stuffed

animal guests and acting as a group of pirates sailing on a

couch. When enacting these scripts, pretenders have goals

of using particular objects from a fictional world, but are

limited to using the real-world objects that are ready at

hand. Pretend players imaginatively overlay the fictional

object onto the real-world object to create a blend, i.e. a

pretend object existing in both the fictional world and the

real world. Pretend play research has found children

project traits of the fictional object on the real object. As an

example, children engaged in a lightsaber duel with

cardboard tubes may make buzzing noises when they

swing the tube. In general, the pretending process involves

identifying real-world objects as stand-ins for the fictional

object, and selectively projecting traits of the fictional

object onto the real object. The objects are input spaces to

the blend. The construction process must account for how

input spaces relate to a larger context of a target pretend

play activity, pruning the options considered.

Building computational systems that can engage in

pretend requires the capacity to construct the objects used

in these scripts (Zook, Riedl, and Magerko 2011). To

formalize the problem, the play activity (lightsaber duel)

provides a structuring situation such that a pretender—

human or agent—selects a real world object (cardboard

tube) as a presentation space for a given reference space of

a fictional object (lightsaber). That is, the goal is to find a

presentation input space that most closely matches the

reference input space. Once the input spaces are selected,

the blending process takes the most relevant aspects of the

fictional object for the activity (buzzing), which are

imposed onto the real object in the blend space for use in

play (swinging a cardboard tube while buzzing). This

process starts with a situation in the fictional world and a

specific fictional object (the lightsaber I am using in the

duel), and seeks a presentation object in the real world to

effectively manifest the fictional object.

To reason about numerous objects in the fictional world

and the real world, we need a computational representation

of objects and their attributes. Lakoff and Johnson (1980)

proposed that the salient perceptual, motor-activity, and

purposive features of objects affect how humans interact

with them. We model objects in both the fictional and real

domains using selected attributes in these categories.

Following prototype theory (Rosch 1978), these attributes

are assigned fuzzy values to represent a real-valued ([0, 1])

range of degree of membership (DOM). As an example, a

lightsaber may have a 0.8 DOM value for the perceptual

feature of being blue (very blue, except for the handle), 0.9

DOM value for the motor-activity feature of ease of

handling (very easy to hold and swing), and 0.1 DOM

value for the purpose of supporting weights (unsuitable for

propping up heavy objects).

Iconic attributes are salient attributes of an object that

distinguish it from similar objects within the same

category. These attributes help to resolve the potential

ambiguity of which fictional object is being represented by

a given real world object. For example, if a pretender grabs

an object and begins making buzzing noises, it may be

unclear if they are signaling that they are holding a buzzing

lightsaber or shooting a laser pistol. An iconic posture of

handling, however, makes this difference clear. Iconic

attributes help participants in pretend play interpret other

players’ behaviors and intentions.

The computational play system algorithm has three steps

for context and goal driven blending: (1) select a real-

world object based on the pretending goal and context;

(2) select the set of fictional object attributes to project;

and (3) project these attribute values into the blend. The

first step is the selection of the input space—the real-world

object—to be blended with the fictional object.

Selecting an input space uses the pretending goal to first

prune impossible input spaces and then search for the

optimal input space among those that remain. Conceptually

this process is similar to the surface-level filtering process

used in the MAC/FAC system (Forbus, Gentner and Law

1995) with the modification of using fuzzy attributes rather

than predicates. The filtering quickly removes from

consideration all real-world objects that differ too

significantly from the desired fictional object on a single

attribute. The goal specifies attributes that are important to

the pretend play object and the extent to which they must

be preserved. Thus, when seeking a lightsaber, all real

objects that are too difficult to handle would be ignored, as

they cannot serve as useful lightsabers during the play.

Computationally, all real objects are compared to the

desired fictional object on the set of relevant attributes, and

those that differ by a specified threshold are pruned. For

example, when considering ease of handling, a cardboard

tube would be kept as a candidate real object for a

lightsaber, while a wooden log would be discarded.

After filtering, the remaining real-world objects are

searched for the single real-world object with minimum

difference from the desired fictional object.

Computationally, the pretend play system exhaustively

searches all available real-world objects and calculates the

Euclidean distances between the attributes of each

candidate real object and the fictional object. The real-

world object with the minimal distance from the fictional

object is then selected. In this domain, pruning appears to

sufficiently constrain the set of potential input spaces to

enable subsequent exhaustive search, although alternative

search techniques are likely applicable.

Once the input spaces are selected, the second step is to

bridge the remaining distance between the real-world

object and the fictional object by determining the set of

iconic attributes that capture characteristic attributes of the

fictional object. These will then be mapped back to the

real-world object so that the agent can play with the real-

world object as a placeholder for the fictional object.

Iconic attributes of an object are those attributes that are

most different from other objects under consideration; they

capture which features are relevant to the pretend goal. The

level of iconicity of an attribute for the desired pretend

object is calculated as the sum of Euclidean distances

between that attribute and the same attribute of all other

objects in the fictional world. Iconicity values are

normalized within categories of objects and an attribute is

considered iconic for an object if it falls in the proximity of

the maximum value.

In the third stage, blending occurs by projecting iconic

values of the desired fictional object onto the selected real

object. By default, the blend space contains all attributes of

the real object. All iconic attributes of the pretend object

are projected into the real object, replacing the original

values. This process captures the notion that most action

and reasoning should treat the real world as the base with

the pretend domain layered onto this base.

In the pretend play algorithm, context and goals are

utilized to filter the set of possible input spaces to only

those that are most crucial for the use of a real-world

object for pretend play. By pruning the set of possible

objects according to their relevance to a goal, the process

avoids the naïve consideration of all possible combinations

of spaces to use for blending. Selective projection of

attributes is achieved by searching for the most iconic

attributes of the presentation input space (the fictional

object) to be blended with attributes of the reference input

space (the real-world object). While this search is

performed in a brute-force manner, the number of

attributes that cross the acceptability threshold for iconicity

are relatively limited.

Discussion and Conclusions

As a powerful mechanism for creativity, conceptual

blending is capable of synthesizing known concepts into

new concepts. Much existing theoretical work focuses on

the blending phenomenon and identifying the input spaces

and the blend without a mechanism for blending. This

paper presents our first efforts at building a complete

computational outline for conceptual blending systems. We

identify three major procedures in conceptual blending: (1)

the selection of input spaces, (2) the mechanism for

selective projection of input space attributes into the blend

space, and (3) the sufficiency condition for pattern

completion and elaboration. These components play vital

roles in conceptual blending and have significant

implications for the efficiency of computation. In our

analysis of computational implementations of blending

theory, we found few systems fully account for all three

processes.

Brandt and Brandt (2002) argued that the construction of

semiotic expressions as blends are cued by communication

contexts and guided by the specific communicative goal.

We argue that context and goals can provide the basis for

rigorous and efficient computational algorithms for the

three main processes described above. We present two

computational systems that utilize goals and context to

guide generation of standalone conceptual blends. The

gadget generation algorithm mainly demonstrates

procedures (2) and (3) by utilizing goals to select concepts

from the input spaces and elaborating them. The pretend

play work likewise uses context to determine which input

spaces to select and which concepts from the input spaces

to project into the blend, illustrating procedures (1) and (2).

These two case studies suggest the three main

procedures can be implemented efficiently by employing

constraints introduced by their respective domain of

application, the contexts of the solutions, and the specific

goals the solutions must achieve. Our analysis shows that

goals can be used to prune the search space and improve

average-case performance. Although our implementations

are deterministic, we believe determinism and goals are not

a bundled package. A goal-driven procedure may not be

completely deterministic or even optimal. Future work is

needed to reduce the effort required to author the

knowledge representations used by our systems (e.g. with

crowdsourcing (Li et al. 2012)).

Boden (2004) raised the questions of whether computers

can appear to be creative, and whether computational

systems can help us understand creativity. By

implementing theories of creativity, we are forced to

consider procedural details which theories sometimes do

not cover. We believe a computational approach can help

expose underspecified components or flaws in existing

theories, hint at their solution, or even lead to their remedy.

A computational approach to creativity will strengthen our

confidence in answering yes to both of Boden’s questions.

Acknowledgement

This work was supported by the National Science

Foundation under Grant No. IIS-1002748. We thank Per

Aage Brandt and the anonymous reviewers for valuable

inputs.

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