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The Yale Artificial Intelligence Project: a Brief Historv J Stephen Slade AI Magazine Volume 8 Number 4 (1987) (© AAAI) The Yale Artificial Intelligence Project: A Brief Historv J Stephen Slade n AI lab is like a greenhouse. have considered a range of topics This overview of the Yale Artificial Intelli- A Researchers develop new ideas related to reasoning including non- gence Project serves as an introduction to and plant them in programs. The pro- monotonic logic, planning with Scientific Datalink’s microfiche publica- grams are cultivated, hybridized, nur- incomplete knowledge, and reasoning tion of Yale AI Technical Reports tured. The weaker ideas die out. The about space and time. stronger ideas are grafted onto new l Cognition and Programming. Elliot stock and serve as the basis of hearty Soloway, and David Barstow before new strains. him, have undertaken a scientific At Yale, there has been a traditional inquiry focused on the task of pro- summer seminar series at which grad- gramming itself. How could a com- uate students present their unprepos- puter automatically write programs? sessing theories to the vocal and criti- How do people learn to program? cal review of their colleagues. The How can a computer program help tenor of these discussions is conveyed people learn to write better programs? by their title: “The Friday Fights.” l Cognitive Science All the work at Such occasions provide the Yale the Yale AI Project could be termed researcher opportunities for both part of cognitive science. We have had pruning and growth. Cultivation by a long association with members of candor is the standard. This level of the Yale Psychology Department, peer review has also been experienced especially Robert Abelson and John by colloquium speakers. Many visi- Black. Psychologists-faculty, visi- tors to the lab were unprepared for the tors, graduate students-have actively onslaught. By now though, Yale’s rep- contributed to the AI research efforts, utation for open debate has led many testing and refining theories of human speakers to agree to disagree. cognitive processing. Ideas and theories grow through this process of natural selection. The Cognitive Modelling best are represented here, in this col- lection of technical reports from the The Yale AI Project began in 1974 past dozen years. These reports are when Roger Schank and Chris Ries- our harvest-the results of twelve beck came from the Stanford AI Lab, intense years of work by a large crop via the Istituto per gli Studi Semantici of researchers. e Cognitivi in Castagnola, Switzer- In the present article, we survey land, to join the Yale Computer Sci- this collection, which is now made ence Department. available on microfiche through Sci- The faculty at Yale, especially Alan entific Datalink. The work falls into Perlis and Martin Schultz, were very several areas. supportive of the establishment of an AI lab. Robert Abelson, in the Psy- l Cognitive Modelling This category is quite broad, and is used here to chology Department, had a long- describe the work of Roger Schank standing interest in AI and had and his students. It includes natural already begun collaboration with language processing, models of human Schank Graduate students were memory organization, learning, and quickly drawn into the work. Jim explanation. Meehan, Wendy Lehnert, Rich Cullingford, and Gerry DeJong were l Spatial and Temporal Reasoning. among the first students involved. Drew McDermott and his students WINTER 1987 67 The main research tool was a DEC In the latter case, even the most Inference.CD included a process PDP-10, equipped with custom-built objective researcher may find himself model for determining the implicit Sugarman CRT’s and Yale’s own full- tailoring the examples to just those meaning of a sentence. screen “E” editor, courtesy of Ned cases which he knows the program Ambiguity. The same word can Irons. can handle. Finally, the experimental have a variety of meanings. (“John The focus of the initial research was paradigm implicit in this approach gave Mary a kiss.” versus “John gave natural language processing. At Stan- requires the researcher to build an Mary a book.“) The CD paradigm ford, Schank had developed the actual computer program. The pro- provided a means of distinguishing MARGIE system (Schank 1975) with gram is the crucible in which theories among multiple word senses. his students Goldman, Rieger, and are tested and molded. Without a pro- As the domain of concepts expand- Riesbeck. MARGIE was used to gram, many of the unstated supposi- ed in the subsequent years, new types demonstrate the effectiveness of con- tions in a theory are never revealed or of knowledge representations were ceptual dependency (CD) as a lan- examined. In writing a program, the developed These included primitives guage-free, canonical meaning repre- researcher must confront these for social acts, attitudes, and objects, sentation MARGIE would read an assumptions. as well as larger knowledge structures English sentence, using Riesbeck’s . Psychological Process Model. The built from these primitives, such as expectation-based parser to build a MARGIE program was a cognitive scripts, plans, goals, memory organi- CD form which represented the simulation. Not only did it try to per- zation packets (MOPS), thematic orga- meaning of the sentence MARGIE form tasks that people perform, but it nization packets (TOPS), and explana- would then make inferences based on tried to simulate the manner in which tion patterns (XPs). the meaning of the input sentence the human mind works. By compari- One additional feature of the using Rieger’s inferencing program. son, a computer chess program which MARGIE system that carried over to Finally, the results of the initial parse, exhaustively searches ahead several Yale researchers was the habit of giv- as well as the inferences, could be moves may be able to play a fine game ing programs names of people. From converted back into natural language of chess, but it is unrealistic to con- SAM and ELI through BORIS and with Goldman’s generator program, sider such a program a model of the CYRUS, this convention has been fre- producing paraphrases in either way in which a person plays chess. quently adopted. English or German. The underlying process model in The MARGIE program, though con- MARGIE comprised three stages: Scripts, Plans, Goals, sidered a toy system, was an effective parsing, inferencing, and generation. and Understanding and productive model for the ensuing This basic triad has been the founda- The Yale AI research reports begin research projects. There were several tion for the subsequent generations of with SAM, the Script Applier Mecha- salient characteristics of MARGIE programs. The primary focus has been nism, the first computer program to that have developed as themes in Yale on integrating the three processes to understand stories in context. cognitive modelling research through allow more interaction with memory. MARGIE had been able to understand to the present. In recent years, the role of memory simple sentences in terms of the l Task Orientation. An AI program has transcended the specific natural actions or states which they repre- should address a specific, real-world language concerns and has come to sented. However, connected text-a task. The program should model encompass learning and explanation story-could cause a problem if the something that a person actually does, processes. This development can be series of actions and states could not rather than an artificial abstraction of viewed as a natural evolution of the be connected through simple infer- intelligent behavior. MARGIE’s tasks original central inference process. ences. How can a program infer con- included reading, paraphrase, and . Canonical Representation of text? Two brief stories illustrate the translation. Subsequent programs Knowledge. The heart of the MARGIE problem. have created stories, answered ques- system was the conceptual dependen- John picked up a rock. John threw tions, summarized stories, skimmed cy knowledge representation system. the rock at Mary. The rock hit stories, professed opinions, related old CD provided a means of representing Mary. stories to new ones, and engaged in actions and states in a canonical, lan- conversations. There are several rea- guage independent fashion. A concept In this first case, the actions are causally linked. A reader can infer the sons for choosing real tasks. First, represented using the dozen CD prim- connections from one action to anoth- these tasks are in the realm of the itives might be expressed in any num- er and build a causal chain based on possible-people provide an existence ber of ways in any number of lan- the inferences associated with each proof. Second, the researcher has tan- guages. More specifically, CD action. For example, picking up an gible ways of assessing the results of addressed a broad range of problems object can enable a person to propel the program through comparisons associated with meaning in language. that object. with human performance Third, the Translation, Synonymy and Para- John went to a restaurant. He researcher has a ready supply of data. phrase CD insured an identical rep- It is preferable for a program to use resentation for two different sen- ordered a lobster. He paid the real data instead of canned examples. tences having the same meaning. check and left. 68 AI MAGAZINE This second story demonstrates Granger built a parsing module that was not about earthquakes at all, but that some causal chains are derived could infer the meaning of unknown concerned the tragic shooting death of from context. In this case, the reader words from context. the Mayor of San Francisco. FRUMP can infer a lot about what John did at SAM had a small repertoire of had taken the lead sentence literally: the restaurant simply because the scripts, and would read news stories in “San Francisco was shaken by the reader knows a lot about restaurants.
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