rapaport@BUFFALO.CSNET ("William J. Rapaport") (03/17/86)
STATE UNIVERSITY OF NEW YORK AT BUFFALO DEPARTMENT OF COMPUTER SCIENCE GRADUATE STUDENT OPEN HOUSE On Thursday, March 20, 1986, the graduate students of the SUNY Buffalo Dept. of Computer Science will be presenting an all-day conference on their recent research (most of which is on AI). A tech report with extended abstracts will be available; for further information, contact James Geller (geller%buffalo@csnet-relay). ABSTRACTS OF TALKS 9:00 - 9:30 JON HULL, A Theory of Hypothesis Generation in Visual Word Recognition An algorithm is presented that generates hypotheses about the identity of a word of text from its image. This algorithm is part of an effort to develop techniques for reading images of text that possess the human capability to adapt to variations in fonts, scripts, etc. This methodology is being pursued by using knowledge about the human reading process to direct the development of algorithms for reading text. The algorithm discussed in this talk locates a set of hypotheses about the identity of an input word (called the neighborhood of the input word). Results are reported in this talk on the size of neighborhoods for words printed in lower case that are drawn from a large text. Several statistical measures are computed from subsets of a text of over 1,000,000 words and their corresponding dictionaries. These results show that the average neighborhood in the dictionary of the entire text contains only 2.5 words. The feasibilty of this method is also shown by experimentation with a database of lower case word images. The application of this approach to 8700 word images taken from 29 different fonts, in three conditions of noise, shows that the correct neighborhood is determined in 80% to 100% of all cases. 9:30 - 10:00 GEORGE SICHERMAN, Databases that Refuse to Answer Queries Question-answering systems must often keep certain information secret. One way they can do this is by refusing to answer some queries. But if the user may be able to deduce information from the system's refusal to answer, the secrecy of the information is broken. In this talk I present a categorization of answer-refusing systems according to what they know, what the user knows, and when the system refuses to answer. I also give two formal results about when the user can deduce secrets from the system's refusals to answer, depending on how much she knows about the system. 10:00 - 10:30 JANYCE WIEBE, Understanding De Re and De Dicto Belief Reports in Discourse and Narrative Belief reports can be interpreted "de re" or "de dicto", and we investigate the disambiguation of belief reports as they appear in discourse and narrative. In earlier work by Rapaport and Shapiro [1984], representations for "de re" and "de dicto" belief reports were presented, and the distinction between them was made solely on the basis of their representations. This analysis is sufficient only when belief reports are considered in isolation. We need to consider more complicated belief structures in order to sufficiently represent "de re" and "de dicto" belief reports as they appear in discourse and narrative. Further, we cannot meaningfully apply one, but not the other, of the concepts "de re" and "de dicto" to these more complicated belief structures. We argue that the concepts "de re" and "de dicto" apply not to an agent's conceptual representation of her beliefs, but to the utterance of a belief report on a specific occasion. A cognitive agent interprets a belief report such as `` S believes that N is F '', or `` S said, ` N is F ' '' (where S and N are names or descriptions, and F is an adjective) "de dicto" if she interprets it from N 's perspective, and "de re" if from her own. 10:45 - 11:15 MINGRUEY TAIE, Device Representation Using Instantiation Rules and Structural Templates A device representation scheme for automatic electronic device fault diagnosis is described. Structural and functional descriptions of devices (which are central to design-model-based fault diagnosis) are represented as instantiation rules and structural templates in a semantic network. Device structure is represented hierarchically to reflect the design model of most devices in the domain. Each object of the device hierarchy has the form of a module. Instead of representing all objects explicitly, an expandable component library is maintained, and objects are instantiated only when needed. The component library consists of descriptions of component "types" used to construct devices at all hierarchical levels. Each component "type" is represented as an instantiation rule and a structural template. The instantiation rule is used to instantiate an object of the component "type" as a module with I/O ports and associated functional descriptions. Functional description is represented as procedural attachments to the semantic network; this allows the simulation of the behavior of objects. Structural templates describe sub-parts and wire connections at the next lower hierarchical level of the component "type". Advantages of the representation scheme are compactness and reasoning efficiency. 11:15 - 11:45 JAMES GELLER, Towards a Theory of Visual Reasoning Visual Knowledge Representation has not yet found the treatment it deserves as its own subfield of AI. Visual reasoning is fundamentally different from predicate calculus type logical reasoning and is of central importance for the field of Visual Knowledge Representation. A systematization of different types of visual reasoning requires the differentiation between purely geometrical reasoning and different types of knowledge-based reasoning. Knowledge-based reasoning in turn can use knowledge about the world, knowledge about abstract hierarchies, or knowledge about normality. Research on visual knowledge is directly applicable to graphics interface design for intelligent systems. The VMES maintenance expert system for circuit board repair uses such a user interface which is designed in analogy to a language generation program. 1:15 - 1:45 MICHAEL ALMEIDA, The Temporal Structure of Narratives Narratives are a type of discourse used to describe sequences of events. In order to understand a narrative, a reader must be able to extract the ``story'', that is, the described events and the temporal relations which hold between them, from the text. Our principle research goal has been to develop a system which can read a narrative and produce a model of the temporal structure of its story. The principle heuristic used in constructing such a model is the Narrative Convention: unless we are given some signal to the contrary, we assume that the events of the story occurred in the order in which they are presented in the text. In addition, however, a reader must deal with: (1) tense - in a standard past tense narrative the principle distinction is between the past and the past perfect tenses, (2) aspect - the distinction between events viewed perfectively or imperfectively, (3) aspectual class - the intrinsic temporal properties of various types of events, (4) time adverbials - these can be used to place events within various calendrical intervals, give their durations, or relate them directly to other events, and to some extent (5) world-knowledge. 1:45 - 2:15 WEI-HSING WANG, A Uniform Knowledge Representation for Intelligent CAI Systems In examining the current situation of Computer Aided Instruction (CAI), we find that Intelligent CAI (ICAI) and its authoring system are necessary. By studying the knowledge representation methods and expert system concepts, we choose a frame representation method to construct an Intelligent Tutor, called ITES. We show that a frame can be used to represent knowledge in semantic nets, procedures and production rules. Furthermore, this method is very convenient in authoring system creation. 2:15 - 2:45 RICK LIVELY, Semantics for Abstract Data Types An abstract data type is often defined as a pair < A , S >, where A is a set (of objects) and S is a set of operations defined on cartesian products of the types of the objects. Axiomatic methods are used to develop specifications for the defined data type. Semantics for abstract data types have been treated by Adj using initial algebras, and by Janssen (inspired by Montague semantics) using many-sorted algebras. A comparison is made of the mathematical properties and applicability to computer science of these approaches. 3:00 - 3:30 SCOTT CAMPBELL, Using Belief Revision to Detect Faults in Circuits To detect faults in electrical circuits, programs must be able to reason about whether the observed inputs and outputs are consistent with the desired function of the circuit. The SNePS Belief Revision System (SNeBR) is designed to reason about the consistency of rules and hypotheses defined within a particular context or belief space. This paper shows how belief revision can be used for fault detection in circuits, and so leads to a unification of the fields of belief revision (also known as truth maintenance) and fault detection. 3:30 - 4:00 DOUGLAS H. MacFADDEN, DUNE: A Demon Based Expert System Architecture for Complex and Incompletely Defined Domains Traditional expert system architectures use the rule (an `` if ... then '' data structure) as the primary unit of knowledge. The primary unit of knowledge in the DUNE system architecture is the demon. Each DUNE demon is an individual processing element that can contain a variety of types of data and can perform a variety of operations on its data. Each demon can communicate with any other demon or with the user via messages. Typical data for these demons may be a traditional type rule, a list of weight values for the features in the left-hand-side of the rule, an (English) description of each feature, a list of related demons, etc. Typical operations that these demons may perform are: calculating the ``closeness'' of the rule to firing, calculating the most important feature of the rule yet to be resolved, telling the system to not consider this demon anymore (entering a sleep state), telling other demons (and the user) that the demon is either satisfied or will never be satisfied, etc. We hope to show that these features of DUNE demons can be exploited to express the knowledge of many expert domains that have proven unfeasible to traditional expert system architectures. 4:00 - 4:30 JOYCE DANIELS, Understanding Time and Space in Narrative Text The Graduate Group in Cognitive Science at SUNY at Buffalo is an interdisciplinary group of faculty and graduate students. Participants in the group's activities come from over seventeen departments within the university and local colleges in Western New York and Canada. There are six core faculty and their graduate students, comprising a standing research group investigating how we understand movement through time and space in narrative text. This research addresses both the general issue of how time and space are expressed in language, and specific individual disciplinary interests such as identifying the exact lexical items signaling movement; developing experiments to collect data on the psychological validity of the supposed influence of suspected lexical items; examining the problems encountered by speech pathologists when a client cannot understand spatial or temporal concepts in language; and artificial intelligence program models of human and linguistic data on the SNePS network. Research conducted by group members has resulted in the identification of what we term the ``Deictic Center'' (DC). This contains a WHO-point, a WHEN-point, and a WHERE-point. It is the locus of a particular point in conceptual space-time. We will explain the significance of the DC concept in greater detail. and present some results of our linguistic and psychological investigation.