I like what you’ve written, you give a lot of examples, run through a history, even point out different techniques, it seems good for something on narrative, and for someone like me who knows bits of this but isn’t a specialist it’s a useful summary, and I’d probably send my students to read it too. Perhaps move up the reference to Weizenbaum (in brackets) so it’s after the first mention of ELIZA, and also make the sentences in the second paragraph flow more? The first two sentences in the paragraph are fine, but after that they read as too staccato to me, OK separately but they don’t quite fit together. I had the same problem in my blog definition: it’s really hard to compress lots of information like this. The rest of the text flows well, I reckon. There’s an “are” that is probably meant to be an “as” towards the end but as you can tell once I start picking at nits that small I’ve no larger bones to pick. Or something.

Except is there no way you can include a cross reference to the blogs entry?

;)

The Works Cited list has Eliza as 1996 not 1966.

Here’s the body:

This field attempts to understand intelligence via systems that reason, choose, and learn. While computer science has had a central role in artificial intelligence (AI), philosophy, linguistics, and psychology are all involved. *Narrative has featured strongly in AI almost from the beginning, because the production and understanding of narrative are complex examples of intelligent behaviour, and because organizing stories into narrative may also be essential to human cognition (Schank 1990). Conversational characters, typified by ELIZA (Weizenbaum 1966), are one intersection of AI and narrative. AI can also generate narratives by simulating (at varying degrees of resolution, modularity and flexibility) an environment and/or characters.[I wouldn't mention interactive drama until after mentioning games.]

Although the term “artificial intelligence” dates from a 1955 proposal [Citation?], in 19XX Arthur Samuel’s checkers program learned to play at the championship level; *computer games have remained important in the field. In the mathematical domain, one early and perhaps archetypal AI system was the theorem-proving General Problem Solver (GPS) developed by Alan Newell and Herbert Simon in the late 1950s. The standard introductory text on AI (Russel and Norvig 2003) provides good historical notes and the mathematical and computational essentials .

ELIZA (Weizenbaum 1966) was the first conversational character. Although it simply matched patterns in the user’s typed input and printed out responses emulating those of a Rogerian psychotherapist, the system created the sense of a character and often elicited narratives from users; some found it compelling for psychotherapeutic, literary, or dramatic reasons (Murray 1997:214-247). Its successors included the 1971 simulated schizophrenic PARRY, an academic project by Ken Colby, and the 1984 commercial poetry-generating program Racter by William Chamberlain. Racter was used to create the first book attributed to a computer author, The Policeman’s Beard is Half Constructed.

SHRDLU (Winograd 1972) used text and graphics to simulate a robot that could rearrange blocks. The system could answer questions about what had happened and could narrate what actions had resulted in the current configuration; it was an important ancestor of interactive fiction such as the 1975-76 Adventure and the 1977-78 Zork. Several later systems had the generation and narration of stories as their main goal. The first of these was TALE-SPIN (Meehan 1976), which used planning to generate fables about animals with simple drives and goals. The system’s amusing and memorable errors illustrate the difficulty of automatically generating interesting stories. Michael Lebowitz’s 1984 UNIVERSE refined the approach and enhanced the representation of characters (embellishing certain stereotypes) in a soap-opera scenario. MINSTREL (Turner 1994) was a similar attempt to generate Arthurian tales, with the innovative ability to get “bored” and move on to other topics. A recent automatic storyteller is BRUTUS (Bringsjord and Ferrucci 2000), a system that uses a formal model of betrayal and sophisticated abilities are a narrator to generate short stories. None of these story-generating systems respond to user input as they run, but they are examples of AI deeply involved with *digital narrative.

Important work on interactive systems that use AI techniques in narrative took place in Carnegie Mellon University’s Oz Project in the early 1990s. Graphical and all-text projects examined parsing natural language, generating surface texts, and representing the emotional states of characters in a narrative or dramatic framework. One thread of this project continued in the work of Joe Bates’s company Zoesis, while Michael Mateas continued the Oz Project’s work at CMU, completing a graphical, Aristotelaian, interactive drama, Facade, in collaboration with Andrew Stern (Mateas 2002). Facade does not have an all-powerful “director” or completely autonomous characters; it allows the characters in the drama to cooperate (even when they are fighting) to attain common goals (e.g., “portray a fight and make the user uncomfortable.”)

The systems above almost all represent story elements and ways of narrating explicitly, using techniques associated with rule-based or symbolic AI, also called “good old-fashioned AI” (GOFAI). Beginning in the 1990s much work in AI has been done in a different framework, successfully employing statistical methods and connectionist principles. Such approaches, although effective, do not include an explicit, human-understandable representation of the system’s knowledge, which some see as a disadvantage for work with narrative. Statistical systems have been used to generate poetry; Jon Trowbridge’s Gnoetry is one free system for doing this. Whether GOFAI will continue to be favoured for narrative work or whether more recent AI techniques will be used in these endeavours as well remains to be seen.

A follow up reaction appears in a new top-level post.

Some of the suggestions remind me of editorial situations that I found myself in as a journalist. I originally wrote:

“This field attempts to understand intelligence and to implement computer systems that can learn, reason, and make intelligent decisions.”

The suggested edit:

“This field attempts to understand intelligence via systems that reason, choose, and learn.”

The edited statement excludes pretty much every researcher at my university from the field of artificial intelligence. It asserts that you are only in the field of AI if you build your systems for the purpose of understanding intelligence. The original statement allows both attempts to understand intelligence (e.g., Minsky’s society of mind theory) and attempts to build systems that act intelligently (e.g., classifiers, theorem-provers, parsers), whether or not these systems are supposed to shed any light on the nature of intelligence.

In this case the suggestion was part of a free (and much-appreciated) peer review, but I mention it since this is the type of innocent-looking edit I’ve often seen inflicted on my articles when I have no chance to see or approve the change before publication. [*] When it’s something like the definition of a whole field of research, subtleties can matter a great deal.

Hopefully my article is now throughly Britished, with proper punctuation and spelling. I didn’t change the structure of the opening paragraph due to specific instructions about how it should outline all the main points of the article, and other instructions guided me in other places.

Amusingly, the one change I didn’t make was to correct 1996 to 1966 for the Eliza article. I’ll send off an email with that correction.

I’ll have to wait to see if this was actually the take on the topic that the volume editors wanted! I hope so…

[*] Of course, my editors made my articles better at times, too. But what I remember are the times they maimed my writing.

I did that edit late at night, and now I can see where I distorted your meaning. But home my word-whacking did leave room for some other good stuff — beyond just repairing the damage I did!

My 200-level journalism class will read “It Ain’t Necessarily So,” a good book of case-studies that show how the media either gulp down and spit back biased press releases, or hype tiny details in otherwise unremarkable studies. (The recent scare about yet another big space rock hitting the Earth is a case in point.)

Sheesh, I just noticed that I misspelled ‘Aristotelian,’ also …

The suggested edit:

“This field attempts to understand intelligence via systems that reason, choose, and learn.”

The edited statement excludes pretty much every researcher at my university from the field of artificial intelligence. It asserts that you are only in the field of AI if you build your systems for the purpose of understanding intelligence. The original statement allows both attempts to understand intelligence (e.g., Minsky’s society of mind theory) and attempts to build systems that act intelligently (e.g., classifiers, theorem-provers, parsers), whether or not these systems are supposed to shed any light on the nature of intelligence.

Yes, AI from the beginning has had these two camps; those that build systems to understand human intelligence (cognitive science – Alan Newell and Herb Simon are paradigmatic examples) and those who build systems to do things that, if done by a human, would require intelligence (that is, artificial intelligences that may or may not have anything to do with human intelligence). Interestingly, I would place Marvin Minsky in the later camp. In Semantic Information Processing (MIT Press, 1968) Minsky defines AI as “…the science of making machines do things that would require intelligence if done by men.” The Society of Mind is not a theory of human cognition (like Soar or Act* are), but a sketch of a decentralized, heterogenous architecture that exhibits behavior that would require intelligence if done by people. For Minksy human brains are one element of the collection of physical systems that exhibit intelligence.