Relay-Version: version B 2.10 5/3/83; site utzoo.UUCP Posting-Version: version B 2.10.3 4.3bsd-beta 6/6/85; site ucbvax.ARPA Path: utzoo!watmath!clyde!burl!ulysses!mhuxr!mhuxt!houxm!ihnp4!ucbvax!LAWS From: LAWS@SRI-AI.ARPA Newsgroups: net.ai Subject: AIList Digest V3 #90 Message-ID: <8887@ucbvax.ARPA> Date: Mon, 8-Jul-85 23:34:59 EDT Article-I.D.: ucbvax.8887 Posted: Mon Jul 8 23:34:59 1985 Date-Received: Wed, 10-Jul-85 23:37:18 EDT Sender: daemon@ucbvax.ARPA Organization: University of California at Berkeley Lines: 250 From: AIList Moderator Kenneth LawsAIList Digest Tuesday, 9 Jul 1985 Volume 3 : Issue 90 Today's Topics: Query - Workstations for AI and Image Processing & PSL Flavors Implementation & Representation of Knowledge, New List - PARSYM for Parallel Symbolic Computing, Psychology - Distributed Associative Memory Systems ---------------------------------------------------------------------- Date: 8 JUL 85 15:10-N From: APPEL%CGEUGE51.BITNET@WISCVM.ARPA Subject: QUERY ON WORKSTATIONS FOR AI AND IMAGE PROCESSING AI on workstations We are looking for a workstation for developping AI systems, mixed with an image processing system. The workstation has to work under Unix (if possible 4.2). We intend to buy a Sun-2, with floating-point accelerator and color graphic system. We heard that the SUN is slow for floating point operations. Does somebody have informations on Franz LISP's performance on SUN, or other AI tools on SUN, versus other similar workstations (in costs)? We are interrested in positive and negative arguments to buy or NOT buy a SUN. Ron Appel ------------------------------ Date: Mon, 8 Jul 85 10:40:59 EST From: munnari!elecadel.oz!alex@seismo Subject: PSL Flavors Implementation. The implementation of Flavors we received with our PSL package from Utah does not permit the mixing of Flavors (whats the point of calling it Flavors then you might ask...). Can anyone tell me of a complete version of Flavors that runs on PSL? I'm also looking for a ZetaLisp compatability package that implements the &.... function parameter conventions. Thanks, Alex Dickinson, The University of Adelaide, South Australia. ------------------------------ Date: Mon, 8 Jul 85 00:00:19 cdt From: Mark Turner Subject: representation of knowledge I am gathering for my students a bibligraphy of works on representation of knowledge. I am particularly concerned with cognitive psychology, artificial intelligence, philosophy, linguistics, and natural language processing. I would appreciate receiving copies of bibliographies others may already have on-line. Mark Turner Department of English U Chicago 60637 >ihnp4!gargoyle!puck!mark ------------------------------ Date: Sun, 7 Jul 1985 21:31 PDT From: DAVIES@Sumex Subject: PARSYM -- new mailing list for Parallel Symbolic Computing PARSYM: A Netwide Mailing List for Parallel Symbolic Computing The PARSYM mailing list has been started to encourage communication between individuals and groups involved in PARALLEL SYMBOLIC COMPUTING (non-numeric computing using multiple processors). The moderator encourages submissions relating either to parallelism in symbolic computing or to the use of symbolic computing techniques (AI, objects, logic programming, expert systems) in parallel computing. All manner of communication is welcomed: project overviews, research results, questions, answers, commentary, criticism, humor, opinions, speculation, historical notes, or any combination thereof, as long as it relates to the hardware, software, or application of parallel symbolic computing. To contribute, send mail to PARSYM@SUMEX (or PARSYM@SUMEX-AIM.ARPA, if your mailer requires). To be added to the PARSYM distribution list, or to make other editorial or administrative requests, send mail to PARSYM-Request@SUMEX. When you are added to the PARSYM distribution list, I will send you a welcoming message with additional information about PARSYM and some necessary cautions about copyright and technology export. To get the list off the ground, I offer the following set of discussion topics: 1. Will there be a general-purpose parallel symbolic processor, or should parallel architectures always be specialized to particular tasks? 2. The primary languages for sequential symbolic computing are Lisp, Prolog, and SmallTalk. Which is a better basis for developing a programming language for parallel computing? Do we need something fundamentally different? 3. Sequential computing took about 30 years to reach its current state. Thirty years ago, programming tools were nonexistent: programmers spent their time cramming programs into a few hundred memory cells, without programming languages or compilers or symbolic debuggers. Now, sequential programming is in a highly developed state: most programmers worry less about the limitations of their hardware than about managing the complexity of their applications and of their evolving computer systems. Today, parallel programming is where sequential programming was thirty years ago: to optimize computation and communication, programmers spend their time manually assigning processes to a few processors, without benefit of programming languages or compilers or symbolic debuggers that deal adequately with parallelism. Will it take 30 years to bring parallel computing up to the current level of serial computing? Submissions, queries, and suggestions are equally welcome. Fire away! PARSYM's Moderator, Byron Davies (Davies@SUMEX) ------------------------------ Date: Sun, 7 Jul 85 21:47:37 EST From: munnari!psych.uq.oz!ross@seismo Subject: instantiation in distributed associative memory systems I was reading some papers by James A. Anderson the other day on the psychological properties of distributed associative memory systems ("Cognitive and psychological computation with neural models",IEEE Transactions on Systems, Man, and Cybernetics, Vol 13, pp 799-815, 1983; "Fun with parallel systems", unpublished paper, 1984). His simulation model associates different features with state vectors (patterns of activation of the neurons) instead of with individual neurons. Orthogonality in this system is achieved in two ways. Alternative values of the same variable (e.g. black-white, mortal-immortal) use the same neurons but have orthogonal codings, whereas dissimilar things (e.g. shoes-sealing wax, cabbages-kings)use entirely different sets of neurons. He taught his system various associations such as Plato -> Man, Man -> Mortal, Zeus -> God, God -> Immortal and the system was able to output triples such as from input of single components. This system can be viewed as approximately equivalent to a production system with rules such as "Man(X) -> Mortal(X)". In Anderson's simulation a better pattern match leads to faster activation so conflict resolution uses a "best match fires first" strategy. I think that his model also allows multiple rules to fire simultaneously provided that they conclude about different attributes. For example it would be possible to conclude simultaneously that Plato is Greek and Mortal. However the superposition of the neural activation patterns for Mortal and Immortal does not necessarily represent anything at all. OK, so much for the rough sketch of Anderson's system. The questions which interest me about it deal with instantiation. In a production system we can arrange things so that values get bound to the variables in the rules. What is the equivalent process in the neural network? My guess is that the activation process is the closest equivalent. The total activity pattern of the network represents the current entity being thought about and it possesses some number of more or less independent attributes. Thus the binding process is particularly simple because there is no choice of entities to bind. There is only one value, the current state, and the choice of attributes of the current state to bind is wired into the synapses of each rule. So a rule looks more like "Big_animal(Current_state) & Teeth(Current_ state) -> Dangerous(Current_state)". You could say that all the rules are permanently bound. If this is a reasonable description of instantiation in neural nets, then the next obvious question is "How the hell do you represent multiple entities?" If multiple entities are represented by the current state of activity on the network there is no way that the rules can decide which attributes go with what entity. As far as they are concerned there is only one entity. So what are the possibilities for keeping entities separate in the neural representation? 1. Attribute separation. If two entities have no attributes in common then they can be represented simultaneously. As noted above, the rules can't break them apart but for some purposes this may not matter. If the entities have an attribute in common then provided they have the same value on that attribute no harm is done. If they have conflicting values on a shared attribute then the representation of at least one of the entities will be distorted. 2. Temporal separation. If a pattern of neural activity causes a short term increase in the ease with which that pattern can be re-triggered then several entities could be juggled by time division multiplexing. Only one entity would be actively represented at a single time, but the other recently represented entities could be easily recalled. This scheme prevents entities interfering and also seems to stop them usefully interacting. It is not clear how the rule mechanism could be modified to allow references to multiple entities in the pattern. 3. Spatial separation. Assume that instead of one neural population there are several of them, all with identical knowledge bases, and communicating with each other. These neural populations are not necessarily physically separate. Each population would be capable of representing and manipulating an entity without interference from the representations of the other entities. Furthermore, because the populations are connected it would be possible for rules to know about multiple entities. The difference between this scheme and the attribute separation scheme is that for a given attribute there will be a distinct group of neurons in each population rather than a single global group of neurons. Any rule which is looking for a pattern involving multiple entities will be able to see them as distinct because the information will come in over distinct synaptic pathways. This spatial separation scheme would be ideal for visual processing because the populations could be arranged in a topographic mapping and allowed to communicate only with their neighbours. This could deal with rules like, "If the neighbour on my left sees an object moving right then I will see it soon and it will still have the attributes my neighbour labelled it with." This scheme could also be used for more cognitive calculations but obviously there would need to be mechanisms for coordination to replace the simple static coordination structure provided by topographic mapping and communication with neighbours. Work done in cognitive psychology shows that children's increasing ability to perform difficult tasks can be attributed to the increasing number of concepts which can be simultaneously activated and manipulated (Graeme S. Halford, "Can young children integrate premises in transitivity and serial order tasks?", Cognitive Psychology, 1984, Vol 16, pp 65-93). Perhaps the children are slowly learning the coordination rules needed to stop the populations acting as one large population and allow them to run as a coordinated group of entity processors. That's my quota of armchair theorising for the week. Anyone got a comment? Ross Gayler | ACSnet: ross@psych.uq.oz Division of Research & Planning | ARPA: ross%psych.uq.oz@seismo.arpa Queensland Department of Health | CSNET: ross@psych.uq.oz GPO Box 48 | UUCP: seismo!munnari!psych.uq.oz!ross Brisbane 4001 | AUSTRALIA | Phone: +61 7 224 7060 ------------------------------ End of AIList Digest ********************