AIList-REQUEST@SRI-AI.ARPA (AIList Moderator Kenneth Laws) (10/19/85)
AIList Digest Friday, 18 Oct 1985 Volume 3 : Issue 149 Today's Topics: Projects - University of Aberdeen & CSLI, Literature - New Complexity Journal, AI Tools - Lisp vs. Prolog, Opinion - AI Hype & Scaling Up, Cognition & Logic - Modus Ponens, Humor - Dognition ---------------------------------------------------------------------- Date: Thu 17 Oct 85 12:44:41-PDT From: Derek Sleeman <SLEEMAN@SUMEX-AIM.ARPA> Subject: University of Aberdeen Program UNIVERSITY of ABERDEEN Department of Computing Science The University of Aberdeen is now making a sizeable committment to build a research group in Intelligent Systems/Cognitive Science. Following the early work of Ted Elcock and his co-workers, the research work of the Department has been effectively restricted to databases. However, with the recent appointment of Derek Sleeman to the faculty from summer 1986, it is anticipated that a sizeable activity will be (re)established in AI. In particular we are anxious to have a number of visitors at any time - and funds have been set aside for this. So we would be particularly interested to hear from people wishing to spend Sabbaticals, short-term Research fellowships etc. Please contact Derek Sleeman at 415 497 3257 or SLEEMAN@SUMEX for further details. ------------------------------ Date: Wed 16 Oct 85 17:12:46-PDT From: Emma Pease <Emma@SU-CSLI.ARPA> Subject: CSLI Projects [Excerpted from the CSLI Newsletter by Laws@SRI-AI.] CSLI PROJECTS The following is a list of CSLI projects and their coordinators. AFT Lexical Representation Theory. Julius Moravcsik (AFT stands for Aitiuational Frame Theory) Computational Models of Spoken Language. Meg Withgott Discourse, Intention, and Action. Phil Cohen. Embedded Computation Group. Brian Smith (3 sub groups) sub 1: Research on Situated Automata. Stan Rosenschein sub 2: Semantically Rational Computer Languages. Curtis Abbott sub 3: Representation and Reasoning. Brian Smith Finite State Morphology. Lauri Karttunen Foundations of Document Preparation. David Levy. Foundations of Grammar. Lauri Karttunen Grammatical Theory and Discourse Structures. Joan Bresnan Head-driven Phrase Structure Grammar. Ivan Sag and Thomas Wasow Lexical Project. Annie Zaenen Linguistic Approaches to Computer Languages. Hans Uszkoreit Phonology and Phonetics. Paul Kiparsky Rational Agency. Michael Bratman Semantics of Computer Language. Terry Winograd Situation Theory and Situation Semantics (STASS). Jon Barwise Visual Communication. Sandy Pentland In addition, there are some interproject working groups. These include: Situated Engine Company. Jon Barwise and Brian Smith Representation and Modelling. Brian Smith and Terry Winograd ------------------------------ Date: Wed 16 Oct 85 09:56:32-EDT From: Susan A. Maser <MASER@COLUMBIA-20.ARPA> Subject: NEW JOURNAL JOURNAL OF COMPLEXITY Academic Press Editor: J.F. Traub, Columbia University FOUNDING EDITORIAL BOARD K. Arrow, Stanford University G. Debreu, University of California, Berkeley Z. Galil, Columbia University L. Hurwicz, University of Minnesota J. Kadane, Carnegie-Mellon University R. Karp, University of California, Berkeley S. Kirkpatrick, I.B.M. H.T. Kung, Carnegie-Mellon University M. Rabin, Harvard University and Hebrew University S. Smale, University of California, Berkeley S. Winograd, I.B.M. S. Wolfram, Institute for Advanced Study H. Wozniakowski, Columbia University and University of Warsaw YOU ARE INVITED TO SUBMIT YOUR MAJOR RESEARCH PAPERS TO THE JOURNAL. See below for further information. Publication Information and Rates: Volume 1 (1985), 2 issues, annual institutional subscription rates: In the US and Canada: $60 All other countries: $68 Volume 2 (1986), 4 issues, annual institutional subscription rates: In the US and Canada: $80 All other countries: $93 Send your subscription orders to: Academic Press, Inc. 1250 Sixth Avenue San Diego, CA 92101 (619) 230-1840 Contents of Volume 1, Issue 1: "A 71/60 Theorem for Bin Packing" by Michael R. Garey & David S. Johnson "Monte-Carlo Algorithms for the Planar Multiterminal Network Reliability Problem" by Richard M. Karp & Michael Luby "Memory Requirements for Balanced Computer Architectures" by H.T. Kung "Optimal Algorithms for Image Understanding: Current Status and Future Plans" by D. Lee "Approximation in a Continuous Model of Computing" by K. Mount & S. Reiter "Quasi-GCD Computations" by Arnold Schonhage "Complexity of Approximately Solved Problems" by J.F. Traub "Average Case Optimality" by G.W. Wasilkowski "A Survey of Information-Based Complexity" by H. Wozniakowski SUBMISSION OF PAPERS The JOURNAL OF COMPLEXITY is a multidisciplinary journal which covers complexity as broadly conceived and which publishes research papers containing substantial mathematical results. In the area of computational complexity the focus is on problems which are approximately solved and for which optimal algorithms or lower bound results are available. Papers which provide major new algorithms or make important progress on upper bounds are also welcome. Papers which present average case or probabilistic analyses are especially solicited. Of particular interest are papers involving distributed systems or parallel computers for which only approximate solutions are available. The following is a partial list of topics for which computational complexity results are of interest: applied mathematics, approximate solution of hard problems, approximation theory, control theory, decision theory, design of experiments, distributed computation, image understanding, information theory, mathematical economics, numerical analysis, parallel computation, prediction and estimation, remote sensing, seismology, statistics, stochastic scheduling. In addition to computational complexity the following are among the other complexity topics of interest: physical limits of computation; chaotic behavior and strange attractors; complexity in biological, physical, or artificial systems. Although the emphasis is on research papers, surveys or bibliographies of special merit may also be published. To receive a more complete set of authors' instructions (with format specifications), or to submit a manuscript (four copies please), write to: J.F. Traub, Editor JOURNAL OF COMPLEXITY Department of Computer Science 450 Computer Science Building Columbia University New York, New York 10027 ------------------------------ Date: Tue, 15 Oct 85 22:15 EDT From: Hewitt@MIT-MC.ARPA Subject: Lisp vs. Prolog (reply to Pereira) I would like to reply to Fernando Pereira's message in which he wrote: It is a FACT that no practical Prolog system is written entirely in Lisp: Common, Inter or any other. Fast Prolog systems have been written for Lisp machines (Symbolics, Xerox, LMI) but their performance depends crucially on major microcode support (so much so that the Symbolics implementation, for example, requires additional microstore hardware to run Prolog). The reason for this is simple: No Lisp (nor C, for that matter...) provides the low-level tagged-pointer and stack operations that are critical to Prolog performance. It seems to me that the above argument about Prolog not REALLY being implemented in Lisp is just a quibble. Lisp implementations from the beginning have provided primitive procedures to manipulate the likes of pointers, parts of pointers, invisible pointers, structures, and stack frames. Such primitve procedures are entirely within the spirit and practice of Lisp. Thus it is not surprising to see primitive procedures in the Lisp implementations of interpreters and compilers for Lisp, Micro-Planner, Pascal, Fortran, and Prolog. Before now no one wanted to claim that the interpreters and compilers for these other languages were not written in "Lisp". What changed? On the other hand primitive procedures to manipulate pointers, parts of pointers, invisible pointers, structures, and stack frames are certainly NOT part of Prolog! In FACT no one in the Prolog community even professes to believe that they could EVER construct a commercially viable (i.e. useful for applications) Common Lisp in Prolog. I certainly realize that interesting research has been done using Planner-like and Prolog-like languages. For example Terry Winograd implemented a robot world simulation with limited natural language interaction using Micro-Planner (the implementation by Sussman, Winograd, and Charniak of the design that I published in IJCAI-69). Subsequently Fernando did some interesting natural language research using Prolog. My chief chief concern is that some AILIST readers might be misled by the recent spate of publicity about the "triumph" of Prolog over Lisp. I simply want to point out that the emperor has no clothes. ------------------------------ Date: Thu, 10 Oct 85 11:03:00 GMT From: gcj%qmc-ori.uucp@ucl-cs.arpa Subject: AI hype A comment from Vol 3 # 128:- ``Since AI, by definition, seeks to replicate areas of human cognitive competence...'' This should perhaps be read in the context of the general discussion which has been taking place about `hype'. But it is still slightly off the mark in my opinion. I suppose this all rests on what one means by human cognitive competence. The thought processes which make us human are far removed from the cold logic of algorithms which are the basis for *all* computer software, AI or otherwise. There is an element in all human cognitive processes which derives from the emotional part of our psyche. We reach decisions not only because we `know' that they are right, but also because we `feel' them to be correct. I think really that AI must be seen as an important extension to the thinking process, as a way of augmenting an expert's scope. Gordon Joly (now gcj%qmc-ori@ucl-cs.arpa (formerly gcj%edxa@ucl-cs.arpa ------------------------------ Date: Fri 18 Oct 85 10:13:10-PDT From: WYLAND@SRI-KL.ARPA Subject: Scaling up AI solutions >From: Gary Martins <GARY@SRI-CSL.ARPA> >Subject: Scaling Up >Mr. Wyland seems to think that finding problem solutions which "scale up" >is a matter of manufacturing convenience, or something like that. What >he seems to overlook is that the property of scaling up (to realistic >performance and behavior) is normally OUR ONLY GUARANTEE THAT THE >"SOLUTION" DOES IN FACT EMBODY A CORRECT SET OF PRINCIPLES. [...] The problem of "scaling up" is not that our solutions do not work in the real world, but that we do not have general, universal solutions applicable to all AI problems. This is because we only understand *parts* of the problem at present. We can design solutions for the parts we understand, but cannot design the universal solution until we understand *all* of the problem. Binary vision modules provide sufficient power to be useful in many robot assembly applications, and simple word recognizers provide enough power to be useful in many speech control applications. These are useful, real-world solutions but are not *universal* solutions: they do not "scale up" as universal solutions to all problems of robot assembly or understanding speech, respectively. I agree with you that scientific theories are proven in the lab (or on-the-job) with real world data. The proof of the engineering is in the working. It is just that we have not reached the same level of understanding of intelligence that Newton's Laws provided for mechanics. Dave Wyland ------------------------------ Date: Tue 15 Oct 85 13:48:28-PDT From: Mike Dante <DANTE@EDWARDS-2060.ARPA> Subject: modus ponens Seems to me that McGee is the one guilty of faulty logic. Consider the following example: Suppose a class consists of three people, a 6 ft boy (Tom), a 5 ft girl (Jane), and a 4 ft boy (John). Do you believe the following statements? (1) If the tallest person in the class is a boy, then if the tallest is not Tom, then the tallest will be John. (2) A boy is the tallest person in the class. (3) If the tallest person in the class is not Tom then the tallest person in the class will be John. How many readers believe (1) and (2) imply the truth of (3)? - Mike ------------------------------ Date: Thu, 17 Oct 85 21:22:26 pdt From: cottrell@nprdc.arpa (Gary Cottrell) Subject: Seminar - Parallel Dog Processing SEMINAR Parallel Dog Processing: Explorations in the Nanostructure of Dognition Garrison W. Cottrell Department of Dog Science Condominium Community College of Southern California Recent advances in neural network modelling have led to its application to increasingly more trivial domains. A prominent example of this line of research has been the creation of an entirely new discipline, Dognitive Science[1], bringing together the insights of the previously disparate fields of obedience training, letter carrying, and vivisection on such questions as, "Why are dogs so dense?" or, "How many dogs does it take to change a lightbulb?"[2] This talk will focus on the first question. Early results suggest that the answer lies in the fact that most dog information processing occurs in their brains. Converging data from various fields (see, for example, "A vivisectionist approach to dog sense manipulation", Seligman, 1985) have shown that this "wetware" is composed of a massive number of slow, noisy switching elements, that are too highly connected to form a proper circuit. Further, they appear to be all trying to go off at the same time like popcorn, rather than proceeding in an orderly fashion. Thus it is no surprise to science that they are dumb beasts. Further impedance to intelligent behavior has been discovered by learning researchers. They have found that the connections between the elements have little weights on them, slowing them down even more and interfering with normal processing. Indeed, as the dog grows, so do these weights, until the processing elements are overloaded. Thus it is now clear why you can't teach an old dog new tricks, and also explains why elderly dogs tend to hang their heads. Experience with young dogs appears to bear this out. They seem to have very little weight in their brains, and their behavior is thus much more laissez faire than older dogs. We have applied these constraints to a neural network learning model of the dog brain. To model the noisy signal of the actual dog neurons, the units of the model are restricted to communicate by barking to one another. As these barks are passed from one unit to another, the weights on the units are increased by an amount proportional to the loudness of the bark. Hence we ____________________ [1]A flood of researchers finding Cognitive Science too hard are switching to this exciting new area. It appears that trivial results in this unknown field will beget journal papers and TR's for several years before funding agencies and reviewers catch on. [2]Questions from the Philosophy of dognitive science (dogmat- ics), such as "If a dog barks in the condo complex and I'm not there to hear it, why do the neighbors claim it makes a sound?" are beyond the scope of this talk. term this learning mechanism bark propagation. Since the weights only increase, just as in the normal dog, at asymptote the network has only one stable state, which we term the dead dog state. Our model is validated by the fact that many dogs appear to achieve this state while still breathing. We will demonstrate a live simulation of our model at the talk. ------------------------------ End of AIList Digest ********************