the fifty-year history of Artificial Intelligence has witnessed
much work on representing and reasoning with the foundations of
cognition such as the modeling of beliefs and goals, relatively
little work has been focused on higher-level cognitive phenomena
such as socialisation, creativity and writing. Researching in the
script-based story understanding traditions of
Roger Schank and Mike Dyer, Scott Turner, in The Creative Process,
presents a computational model of creativity and storytelling. The
problem space is very interesting and fruitful, and there is still
a paucity of research at these cognitive granularities.
motivates the computational modeling of creativity with the goal
of producing short 200-word-long computer-generated stories within
the King Arthur domain. This program, called MINSTREL,
was the topic of Turner's dissertation work with Mike Dyer at UCLA
in the late 1980s. Turner recounts previous story generation programs,
such as, prominently, Meehan's TALESPIN system,
which treated stories as problem solving simulations. Turner's criticism
of TALESPIN is that the stories it produced were wholly uninteresting
and unmotivated. In writing MINSTREL, Turner wanted to model several
layers of storytelling planning tasks, such as the application
of story themes, i.e. morals; drama techniques
and devices (e.g. suspense, foreshadowing, pacing, characterisation,
dialogue, description, tragedy); and the introduction of creativity.
One of the canonical stories generated by MINSTREL, called "The
Vengeful Princess," illustrates the complexity of the task
a descendant of Schank and Dyer, Turner's view of the modeling world
is understandably dominated by the paradigms of scripts, planning,
problem solving, and case-based reasoning. In fact, it is Turner's
thesis that every cognitive task (including creativity) can be viewed
as problem solving. One of the well-known
traps of case-based reasoning is that the discretisation
of "cases" seems to encourage rote learning
and rote application of knowledge. In my opinion, this is rather
problematical to the modeling of creativity which my intuition tells
me is somehow more fluid, well-connected, and parallel (perhaps
an associationist/k-line layer would help?) than
CBR is good at. To his credit, Turner recognises the rote learning
trap of CBR and tries to address it with some success through the
introduction of "mutations."
is set up as a generic case-based problem solver
instantiated several times to solve different granularities
of the creative authoring problem. From highest to lowest granularity,
these subgoals are: theme-selection, consistency-maintainence, dramatic-writing-techniques,
and linguistic-presentation. Turner seems quite proud that there
is a single generic mechanism that handles all of these subgoals.
At each granularity, creativity is interjected in the form of mutations
of the problem description. Let me illustrate this idea with an
example at the theme-selection granularity. Suppose I have an episodic
memory of stories about business situations, and I wanted to adapt
one of these stories to the domain of King Arthur. Each story can
be represented as a network of frames or a script, with slots
like: action, actor, instrument, etc. These features are used to
identify the story and provide a way to index stories.
(Memory research Tulving has previously argued for the great diversity
and specificity of encoded features for retrieving memories in human
recall.) In MINSTREL, the case-based reasoner takes a problem description
with features from King Arthur, and tries first to look for a rote
match in episodic memory. If none exists, usually CBR systems
would fail, but Turner's suggestion is to recursively mutate
and massage the problem description until a match can be
found. For example, change the character "Sir Galahad"
to "boss" and "princess" to "employee"
or other mutations. What's unclear from the book is the extent of
rigour applied to deciding on mutations, but my guess is that it
can only be as rigourous as the amount of external knowledge we
have about how to make cross-domain analogical mappings (on this
point, cf. Fauconnier and (Mark) Turner's work on Conceptual
Blending, which specifies metaphorical binding conditions).
Turner calls the recursive failure-driven mutation
of problem descriptions "creativity." I
am skeptical that people are creative in the same way, because at
the very least, people are fit at creating sound cross-domain analogical
mappings between discourses like business and King Arthur. Turner
recognises that some mutations are nonsensical, but instead of proposing
more knowledge to assess the soundness of mutations (e.g. Gentner
and Forbus' structure-mapping idea), he hand-wavishly excuses these
errors by arguing that people too make creativity errors
(my position is that while people err, they don't err in such a
fundamentally nonsensical way because people are good at things
like context and analogy).
prominent variation on the creativity-as-mutation idea is Turner's
suggestion for an imaginative memory. An imaginative
memory is created by applying mutations to the individual episodic
memories rather than to the problem description. Again, my complaint
is that I feel it's unsatisfactory to argue that imagination
is simply a mutation operation over episodes without specifying
any principles for mutation. A mutation can be a random change,
or a complicated context-sensitive inference. My belief is that
it is far more complex and I wish there was more dedicated to its
specification in Turner's book. My storytelling program MAKEBELIEVE
(Liu & Singh, 2002) introduced variation at the lexical level
by substitutions using verb alternation classes and semantic synsets
-- a rather naive thing to do -- but MINSTREL seems to also use
naive mutation for its model of creativity.
for the rest of the book, there are some fruits to be had. Several
interesting thematic goals were implemented, i.e. Planning-Advice-Themes
(PATs), and four transformations are randomly applied to each theme
to "invent" new themes, namely, generalisation, specialisation,
mutation, and recombination. The major role of PAT transformations
seems to be to help MINSTREL appear to be less deterministic
and hand-crafted. Like creative mutation, there is no way to critique
or assess the soundness or goodness of each transformation, because
perhaps very broad commonsense knowledge would be required for such
a task. MINSTREL implements only one real critic, the boredom
assessment, which measures boredom as the ratio of recalled
solutions to creative solutions. Perhaps this is the only critic
which can be implemented without external knowledge.
rest of the book is dedicated to discussion of the other story planning
tasks such as the introduction of explanatory text to segue from
one vignette to another and so on. MINSTREL's evaluation amounts
to a quantitative re-enunciation of the various inventories of goals
and themes and such, and there is also a limited human evaluation
of story quality. My sense is that evaluating these sorts of creative
pursuits seems quite difficult, especially with systems like MINSTREL
that are so brittle and narrow in their storytelling abilities.
conclusion, The Creative Process is interesting insofar
as it attempts to model two interesting high-level cognitive phenomena
of storytelling and creativity. MINSTREL is novel relative to the
prior work on story generation because for the first time, there
is serious treatment given to author-level goals.
I would hesitate to call the proposed model of creativity as anything
close to profound because it is implemented as small random variations
in a very brittle, narrow, hand-crafted language of scripts and
schemas. And really, without broad knowledge of the world to draw
from, it is impossible to really emulate the human creative process.
I am slightly erked that instead of fessing up to this, Turner back-justifies
MINSTREL's creative mutation mechanisms with pseudo-truisms like
'creativity amounts to many small changes' and 'MINSTREL makes creative
errors but so do people.' This is reminiscent of the academic context
of early AI when researchers thought that humans could be emulated
with one or two simple structures and not very much knowledge at
all. Only now are we beginning to admit the importance of broad
knowledge in all cognitive pursuits.
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