Path: utzoo!utgpu!attcan!uunet!husc6!mailrus!purdue!decwrl!ucbvax!CS.ROCHESTER.EDU!nl-kr-request From: nl-kr-request@CS.ROCHESTER.EDU (NL-KR Moderator Brad Miller) Newsgroups: comp.ai.nlang-know-rep Subject: NL-KR Digest Volume 5 No. 20 Message-ID: <8811030502.AA23866@teak.cs.rochester.edu> Date: 3 Nov 88 05:00:00 GMT Sender: daemon@ucbvax.BERKELEY.EDU Reply-To: nl-kr@CS.ROCHESTER.EDU Organization: University of Rochester, Department of Computer Science Lines: 422 Approved: nl-kr@cs.rochester.edu NL-KR Digest (11/02/88 23:59:38) Volume 5 Number 20 Today's Topics: SEMINARS Machiavelli : A Polymorphic Lang. for oo db (Unisys Seminar) The Computational Linguistics of DNA (UPenn Seminar) Philosophy Colloquium: Pollock Seminar - To Think or Not to Think - McAllester SUNY Buffalo Logic Colloq: Nelson From CSLI Calendar, October 20, 4:5 Eric Saund--AI Revolving Seminar FRIDAY 10/28 Submissions: NL-KR@CS.ROCHESTER.EDU Requests, policy: NL-KR-REQUEST@CS.ROCHESTER.EDU ---------------------------------------------------------------------- Date: Sun, 16 Oct 88 14:46 EDT From: finin@PRC.Unisys.COM Subject: Machiavelli : A Polymorphic Lang. for oo db (Unisys Seminar) AI SEMINAR UNISYS PAOLI RESEARCH CENTER Atsushi Ohori University of Pennsylvania Machiavelli : A Polymorphic Language for Object-oriented Databases Machiavelli is a programming language for databases and object-oriented programming with a strong, statically checked type system. It is an extension of the programming language ML with generalized relational algebra, type inheritance and general recursive types. In Machiavelli, various database operations including join and projection are available as polymorphic operations, ML's abstract data types are extended with inheritance declarations, and the type system includes general recursive types. In this talk, I will first introduce Machiavelli and show examples demonstrating its expressive power in the context of both database programming and object-oriented programming. I will then describe the theoretical aspects of the language. For the theoretical aspects of the language, I will show that, by defining syntactic orderings on subsets of terms and types that correspond to database objects, a generalized relational algebra can be introduced in a strongly typed functional programming language. By allowing conditions on substitutions for type variables, Milner's type inference algorithm can be also extended to those new constructs. I will then show that by using the type inference mechanism, ML's abstract data types can be extended to support inheritance. Finally I will describe how the above mechanisms can be extended to recursive types. Joint work with Peter Buneman. 10:30 am - November 2, 1988 BIC Conference Room Unisys Paoli Research Center Route 252 and Central Ave. Paoli PA 19311 -- non-Unisys visitors who are interested in attending should -- -- send email to finin@prc.unisys.com or call 215-648-7446 -- ------------------------------ Date: Tue, 18 Oct 88 08:41 EDT From: finin@PRC.Unisys.COM Subject: The Computational Linguistics of DNA (UPenn Seminar) UNIVERSITY OF PENNSYLVANIA DEPARTMENT OF COMPUTER AND INFORMATION SCIENCE The Computational Linguistics of DNA David Searls Unisys Paoli Research Center Genetic information, as expressed in the four-letter alphabet of the DNA of living organisms, represents a complex and richly-expressive linguistic system that encodes procedural instructions on how to create and maintain life. There is a wealth of understanding of the semantics of this language from the field of molecular biology, but its syntax has been elaborated primarily at the lowest lexical levels, without benefit of formal computational approaches that might help to organize its description and analysis. In this talk, I will examine some linguistic properties of DNA, and propose that generative grammars can and should be used to describe genetic information in a declarative, hierarchical manner. Furthermore, I show how a Definite Clause Grammar implementation can be used to perform various kinds of analyses of sequence information by parsing DNA. This approach promises to be useful in recombinant DNA experiment planning systems, in simulation of genetic systems, in the interactive investigation of complex control sequences, and in large-scale search over huge DNA sequence databases. THURSDAY, OCTOBER 20, 1988 REFRESHMENTS 2:30 - 3:00 129 Pender COLLOQUIUM 3:00 - 4:30 216 MOORE ------------------------------ Date: Tue, 18 Oct 88 10:50 EDT From: William J. Rapaport Subject: Philosophy Colloquium: Pollock UNIVERSITY AT BUFFALO STATE UNIVERSITY OF NEW YORK DEPARTMENT OF PHILOSOPHY GRADUATE GROUP IN COGNITIVE SCIENCE and GRADUATE RESEARCH INITIATIVE IN COGNITIVE AND LINGUISTIC SCIENCES PRESENT JOHN POLLOCK Department of Philosophy University of Arizona OSCAR: A General Theory of Rationality [Background material for this colloquium, and an introduction ot Oscar, may be found in Prof. Pollock's article, ``My Brother, The Machine,'' _Nous_ 22 (1988) 173-212.] Wednesday, October 26, 1988 4:00 P.M. 684 Baldy Hall, Amherst Campus There will be an evening discussion at 8:00 P.M., at Mary Galbraith's, 130 Jewett Parkway, Buffalo. Call Bill Rapaport, Dept. of Computer Science, 636-3193, or Jim Lawler, Dept. of Philosophy, 636-2444, for further information. ------------------------------ Date: Wed, 19 Oct 88 16:27 EDT From: Barbara K. Moore Subject: Seminar - To Think or Not to Think - McAllester ============================================================================ AI REVOLVING SEMINAR ============================================================================ FRIDAY, OCTOBER 21, 1988 4:00 p.m. 8TH FLOOR PLAYROOM, MIT AI LAB David McAllester "To Think or not to Think" Automated inference is a central problem in type checking, program verification, optimizing compilers, automatic programming, and AI applications such as common sense knowledge representation, natural language understanding and planning. This talk discusses ongoing research in knowledge representation and automated reasoning. This research is based on new inference techniques such as focused forward chaining, monotone closure for taxanomic syntax, semantic modulation, and forward chaining mathematical induction. Each particular inference mechanism can be evaluated from both an engineering and a cognitive science perspective. >From an engineering perspective the significance of an inference mechanism is determined by its usefulness in solving engineering problems such as program verification or automated programming. From a cognitive science perspective the significance of an inference mechanism is determed by its match with human cognitive power. Data will be presented showing how certain inference mechanisms succeed or fail as cognitive models. ------------------------------ Date: Wed, 19 Oct 88 16:53 EDT From: William J. Rapaport Subject: SUNY Buffalo Logic Colloq: Nelson UNIVERSITY AT BUFFALO STATE UNIVERSITY OF NEW YORK BUFFALO LOGIC COLLOQUIUM GRADUATE GROUP IN COGNITIVE SCIENCE and GRADUATE RESEARCH INITIATIVE IN COGNITIVE AND LINGUISTIC SCIENCES PRESENT RAYMOND J. NELSON Truman Handy Professor of Philosophy Case Western Reserve University CHURCH'S THESIS, CONNECTIONISM, AND COGNITIVE SCIENCE Wednesday, November 16, 1988 4:00 P.M. 684 Baldy Hall, Amherst Campus The Church-Turing Thesis (CT) is a central principle of contemporary logic and computability theory as well as of cognitive science (which includes philosophy of mind). As a mathematical principle, CT states that any effectively computable function of non-negative integers is general recursive; in computer and cognitive-science terms, it states that any effectively algorithmic symbolic processing is Turing comput- able, i.e., can be carried out by an idealized stored-program digital computer (one with infinite memory that never fails or makes mistakes). In this form, CT is essentially an empirical principle. Many cognitive scientists have adopted the working hypothesis that the mind/brain (as a cognitive organ) is some sort of algorithmic symbol- processor. By CT, it follows that the mind/brain is (or realizes) a system of recursive rules. This may be interpreted in two ways, depend- ing on two types of algorithm, free or embodied. A free algorithm is represented by any program; an embodied algorithm is one built into a network (such as an ALU unit or a neuronal group). CT is being challenged by connectionism, which asserts that many cogni- tive processes, including perception in particular, are not symbol processes, but rather subsymbol processes of entities that have no literal semantic interpretation. These are parallel, distributed, asso- ciative memory processes totally unlike serial, executive-driven, von Neumann computers. CT is also being challenged by evolutionism, which is a form of connectionism that denies that phylogenesis produces a mind/brain adapted to fixed categories or distal stimuli (even fuzzy ones). Computers deal only with fixed categories (either in machine language, codes such as ASCII, or declarations in higher-level languages). So, if connectionists are right, CT is false: there are processes that are provably (I will suggest a proof) effective and algo- rithmic but are not Turing-computable. However, if CT in empirical form is true, and if the processes involved are effective, then connectionism or, in general, anti-computationalism is false. A direct argument that does not appeal to CT but that tends to confirm it is that embodied algorithm networks as a matter of fact are parallel, distributed, associative, and subsymbolic even in von Neumann computers, not to say super-multiprocessors. Finally, I claim that the embodied algorithm network models are not only _not_ antithetical to evolutionism but dovetail nicely with the theory that the mind/brain evolves through the life of the individual. REFERENCES Edelman, G. (1987), _Neural Darwinism_ (Basic Books). Nelson R. J. (1988), ``Connections among Connections,'' _Behavioral & Brain Sci._ 11. Smolensky, P. (1988), ``On the Proper Treatment of Connectionism,'' _Behavioral & Brain Sci._ 11. There will be an evening discussion at a time and place to be announced. Contact John Corcoran, Department of Philosophy, 636-2444 for further information. ------------------------------ Date: Wed, 19 Oct 88 20:31 EDT From: Emma Pease Subject: From CSLI Calendar, October 20, 4:5 The Resolution Problem for Natural-Language Processing Week 4: Psychological Processes Herb Clark (herb@psych.stanford.edu) 20 October I will review part of what is known about the process of resolving ambiguities and indeterminacies from work in psychology. Last week I took up, among other things, the issues of automaticity and modularity in resolving structural ambiguities--that is, ambiguous words, attachment ambiguities, and other local parsing ambiguities. The question is, how are these ambiguities resolved so quickly and apparently automatically on the basis of lexical, syntactic, semantic, and pragmatic information, and what does this say about the process of understanding in general? This week I will take up the more pragmatic issues in resolution, such as how people resolve references, illocutionary force, and implicatures, and how speakers and listeners manage to do this collectively. ____________ NEXT WEEK'S TINLECTURE Chaos Bernardo Huberman Xerox PARC and Applied Physics Department, Stanford University (huberman.pa@xerox.com) October 27 Recent developments in dynamical systems theory have led to a reappraisal of our understanding of determinism and the origin of noise in many physical systems. In particular, it has been established that certain deterministic systems with few degrees of freedom can exhibit random behavior that is analogous to that produced by the tossing of a coin. This talk will provide an introduction to the field of deterministic chaos. It will also elucidate the notion of universality, and its implications for the application of chaos theory to many fields of science. ____________ NEXT WEEK'S CSLI SEMINAR The Resolution Problem for Natural-Language Processing Week 5: Early AI Research on Local Pragmatics Jerry Hobbs (hobbs@warbucks.ai.sri.com) October 27 AI researchers have been grappling with problems in local pragmatics, or the resolution problem, for at least the last fifteen years. We will discuss Rieger's work on several of these problems, work on the interpretation of nominal compounds, including that of Finin, and early and more recent work on pronoun resolution, syntactic ambiguity, metonymy, and quantifier scope ambiguity that has been in the same spirit. All of this work has been characterized by attempts to aim toward efficient and effective heuristics that use world knowledge in a limited enough way to make the approach feasible. The shortcomings of this family of approaches will also be discussed. ____________ SYMBOLIC SYSTEMS FORUM Formalizing Commonsense Knowledge and Reasoning in Mathematical Logic John McCarthy Friday, 21 October, 3:15 Bldg. 60 This Friday John McCarthy will be speaking on formalizing commonsense knowledge and reasoning in mathematical logic. He is one of the cofounders of artificial intelligence. He has worked on problems associated with the logic approach to AI for thirty years and will discuss what has been accomplished and what seem to be the next problems. This involves representing by mathematical logical sentences what a computer program should know about the commonsense world in general and about specific situations. What it can infer about what actions will achieve its goals is determined by logical inference including both logical deduction and formalized nonmonotonic reasoning. As always, the Forum will be held at 3:15 in building 60. However, because we are expecting a large crowd, it will meet in the lecture hall right next to the entrances to the building instead of room 62N. ------------------------------ Date: Sat, 22 Oct 88 21:20 EDT From: barb@reagan.ai.mit.edu Subject: Eric Saund--AI Revolving Seminar FRIDAY 10/28 ============================================================================ AI REVOLVING SEMINAR ============================================================================ FRIDAY, OCTOBER 28, 1988 4:00 p.m. 8TH FLOOR PLAYROOM, MIT AI LAB Eric Saund "The Role of Knowledge in Visual Shape Representation" or "What Should a Visual System Know Next?" or "To Swim or Not to Swim?" This talk shows how knowledge about the visual world can be built into a shape representation in the form of a descriptive vocabulary making explicit the important spatial events and geometrical relationships comprising an object's shape. We offer two specific computational tools establishing a framework by which a shape representation may support a variety of Later visual processing tasks: (1) By maintaining shape tokens on a Scale-Space Blackboard, information about configurations of shape events such as contours and regions can be manipulated symbolically, while the pictorial organization inherent to a shape's spatial geometry is preserved. (2) Through the device of dimensionality-reduction, configurations of shape tokens can be interpreted in terms of their membership within deformation classes; this provides leverage in distinguishing shapes on the basis of subtle variations reflecting deformations in their forms. The power in these tools derives from their contributions to capturing knowledge about the visual world. In contrast to ``building block'' approaches to shape representation (eg. generalized cylinders), we employ a large and extensible vocabulary of shape descriptors tailored to the constraints and regularities of particular shape worlds. The approach is illustrated through a computer implementation of a hierarchical shape vocabulary designed to offer flexibility in supporting important aspects of shape recognition and shape comparison in the two-dimensional shape domain of the dorsal fins of fishes. ------------------------------ End of NL-KR Digest *******************