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Research in Artificial Intelligence at the University of Pennsylvania

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Research in Artificial Intelligence at the University of Pennsylvania AI Magazine Volume 7 Number 3 (1986) (© AAAI) RESEARCH IN PROGRESS Brad A. Cheikes Research in Artificial Intelligence at the University of Pennsylvania Overview be described in more detail later), with all CIS computers connected by an Ethernet. The CIS research facilities are The Department of Computer and Information Science managed by Ira Winston, with assistance from Suzanne (CIS), in the University of Pennsylvania’s School of En- Morin. gineering and Applied Science, conducts a wide range of research projects in artificial intelligence and related disci- The CIS department maintains connections to CSNET plines. Participating in this research effort are not only the (node UPENN) and ARPANET for exchanging informa- CIS faculty and students but also members of the chem- tion with other universities and research institutes. ical, civil, electrical, mechanical, and systems engineering Research Laboratories. The bulk of the research departments; the linguistics, philosophy, and psychology conducted in the CIS department takes place in four departments; the School of Medicine; and the Wharton laboratories: the Language, Information, and Computa- School. Primary research interests include natural lan- tion (LINC) Laboratory; the Computer Graphics Labora- guage processing, knowledge representation, expert sys- tory; the General Robotics and Active Sensory Processing tems, automated reasoning and logic programming, anal- (GRASP) Laboratory; and the GRASP Image Process- ysis and synthesis of motion, computer graphics, graph- ing (GRIP) Laboratory. This report first describes the ics animation, program specification and transformation, research being conducted in the various labs, then the re- parallel processing for AI systems, computer vision and search carried on outside the lab environment. robotics, integration of visual and tactile perception, real- time distributed operating systems for vision and robotics, and medical imaging. The LINC Laboratory Research Computing Facilities. Facilities available The LINC laboratory is the center for research in natu- for research computing include VAXes, Symbolics LISP ral language processing, expert systems, knowledge repre- machines, MicroVAXes, Sun and Hewlett-Packard work- sentation, automatic theorem proving, and logic program- stations, high-speed graphics displays, and special-purpose ming. The faculty members supervising projects in this vision and robotics equipment. In addition, some parallel laboratory are Aravind Joshi and Bonnie Webber (natu- computers are expected to be installed later in the year. ral language processing), Tim Finin (knowledge represen- The CIS Research Computing Facility, consisting of six tation and expert systems), Lokendra Shastri (knowledge VAX computers running VMS and arranged in a VAX- representation), and Dale Miller (automatic theorem prov- cluster, provides general computing services to all CIS and ing and logic programming). Technical support for the lab CIS-affiliated researchers. Besides these general facilities, is provided by Richard Billington. individual laboratories maintain their own equipment (to Abstract This report describes recent and continuing research in artificial Brant A Cheiks is affiliated with the Department of Computer and Information Science, The Moore School of Electrical Engineering, intelligence and related fields being conducted at the Univer- University of Pennsylvania, Philadelphia PA 19104 sity of Pennsylvania. Although AI research takes place primar- I would like to thank all the faculty members who put up with my ily in the Department of Computer and Information Science initial and follow-up interviews, as well as those who wrote or edited (in the School of Engineering and Applied Science), many as- portions of this report; it would have been impossible to write this pects of this research are performed in collaboration with other report without their time and interest I would like to especially engineering departnrents as well as other scl~oolsat the Univer- thank Ellen Hays for the many ways she helped make this paper more readable Finally, I would like to express my appreciation to sity, such as the College of Arts and Sciences, the School of Aravind Joshi and Bonnie Webber for their thoughtful critiques of Medicine, and the Wharton School. various drafts of the report 128 THE AI MAGAZINE August, 1986 Research facilities available to the LINC lab include definition of a term or for an explanation of the difference a VAX-11/785 ( running Ultrix), ten Symbolics LISP between two terms, TEXT does the following: machines (a combination of 3600s 3640s and 3645s), Collects all the relevant information it might include in twenty Hewlett-Packard Bobcat 9000/300 AI development its response (not all of this information will necessar- workstations’ (of which eight are in the LINC lab proper ily appear in the actual response, but no information and twelve are distributed throughout various other labs other than this will appear) for expert system development work), a Sun workstation, Chooses an appropriate, fairly flexible format (called and an Apple Macintosh. a schema) for presenting the information to the user Fills in the schema with a subset of the relevant infor- Research in Natural Language Processing mation, guided by the options allowed in the schema LINC lab researchers in natural language processing have and rules that constrain the focus of the text to change been focusing their attention on the design of coopera- in a natural way tive interactive systems, such as database query systems Generates natural language text from the instantiated and expert advisory systems (Finin, Webber, & Joshi, schema 1986), and the computational properties of grammatical In the past year, TEXT has been ported to the Sym- formalisms for expressing the syntactic, semantic, and (ul- bolics LISP machine environment (it was originally written timately) pragmatic meaning of natural language utter- in Franz LISP on a VAX) and connected with McDonald’s antes . text generator MUMBLE (McDonald, 1983), which was written at the University of Massachusetts and adapted Interactive Systems locally to work with TEXT (Rubinoff, 1986). Input to With computer systems becoming increasingly popular TEXT now comes from the RUS parser, imported from both for their ability to maintain large and complex Bolt, Beranek, and Newman, Inc. (BBN). Future work databases and for their usefulness in expert advisory ca- with TEXT involves grounding it in a richer knowledge pacities, the need for such systems to be capable of sophis- representation system which should enable it to produce ticated conversational interaction has become clear. The definitions that are better tailored to the specific situation. desire to allow natural language communication between Although TEXT was designed to provide definitions system and user is motivated by the recognition that such of concepts known to a database, Robert Rubinoff devel- a capability can substantially ease the information-seeking oped the CLEAR system (Rubinoff, 1985) to do the same process (that is, users can ask their questions in a natu- for concepts contained in an expert system. CLEAR bases ral manner, rather than in a structured, formal query lan- its response on information extracted from the expert sys- guage). Furthermore, a system’s ability to understand and tem’s rule base and requires little or no augmentation of respond in a natural language greatly reduces or eliminates the system to generate explanations. In deciding whether the need for training users in system operation. to use a rule in its description of a concept, CLEAR ranks Unfortunately, as computer systems are able to per- the rule according to where and how the concept is used form increasingly complex activities, the kinds of interac- in that rule. Based on these rankings, CLEAR selects the tion such systems must support increase accordingly. Re- rules that are most useful for explaining the concept and search in the LINC lab has been geared toward identifying then uses templates for generating the actual response. properties of interaction deemed necessary in cooperative Correcting Misconceptions. Another possible imped- systems and developing computational methods by which iment to successful interaction between user and system they can be implemented. Several recent projects are de- involves discrepancies between the system’s beliefs about scribed in the following sections. the world and those of the user. Many difficulties in inter- Providing Definitions of Concepts. When database action arise because the participants hold different views systems are large and complex, it is generally undesir- about the world and either do not realize it or fail to do able to assume that users know the system’s structure and anything about it. As a result, they may leave the conver- content or that they understand the terms and concepts sation with different views regarding what was communi- stored in the database. Recognizing this, Kathleen McK- cated. eown devoted her Ph.D. thesis (McKeown, 1985) to dcvcl- In a recently completed Ph.D. thesis, Kathleen Mc- oping a domain-independent technique by which a system Coy developed a method for correcting object-related mis- can generate natural language explanations of the terms conceptions (McCoy, 1985). Such misconceptions may be it uses. Her system, called TEXT, works by “piggyback- revealed when a user’s utterance describes an object in ing” its definitional machinery on an augmented version terms
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