[unrev-II] A CYC competitor: a society of Webminds (Psynet), KNOW- language for knowledge representation & communications with WebSphere

From: John J. Deneen (jjdeneen@ricochet.net)
Date: Thu Jul 05 2001 - 23:45:22 PDT

  • Next message: Bernard Vatant: "Re: [unrev-II] A CYC competitor: a society of Webminds (Psynet), KNOW- language for knowledge representation & communications with WebSphere"

    "Basically, Cyc is based on getting humans to tell computers what words
    mean. Webmind can be taught in this way – we have our own knowledge
    entry language called KNOW, which we believe is simpler than Cyc and
    closer to everyday human psychology. But KNOW expressions inspire rather
    than control Webmind’s internal definitions, which in general are much
    bigger and messier than KNOW Cyc definitions. In dealing with most
    practical situations, humans, or Webminds, don’t refer to this kind of
    abstract definition at all, but we use more detailed, context-specific
    patterns of knowledge.

    Cyc tried to divorce information from learning, but it can't be done. A
    mind can only make intelligent use of information that it has figured
    out for itself. Despite sixteen years of programming, Cyc never
    succeeded in emulating an eight year old child. Nor has anyone yet found
    much use for a CD-ROM full of formal, logical definitions of common
    sense information. ...

    ... Based partically from the “Global Workspace” concept<
    http://www.phil.vt.edu/ASSC/esem2.html >, Webmind 1.0 is scheduled to be
    completed in 2001. Webmind 2.0 will incorporate the ability to reason
    about and modify its own source code, and also one additional sensory
    modality: sound processing (voice and music). This is expected about 18
    months after Webmind 1.0. ...

    ... Webmind is not the only AI system to seek a middle way between
    symbolic and connectionist AI. Gerald Edelman, a Nobel Prize winning
    biologist, proposed a theory of "neuronal group selection" or Neural
    Darwinism, which describes how the brain constructs larger scale
    networks called "maps" out of neural modules, and selects between these
    maps in an evolutionary manner, in order to find maps of optimum
    performance. And Marvin Minsky, the champion of rule-based AI, had moved
    in an oddly similar direction, proposing a "Society of Mind" theory in
    which is mind is viewed as a kind of society of actors or processes that
    send messages to each other and form alliances into temporary working
    groups. < http://www.intelligenesis.net/webmindandotherai.html >...
    < http://www.intelligenesis.net/webmindphilosophy.html >
    < http://www.intelligenesis.net/communicatorframework.html >

    3. The Webmind AI Architecture
    “Activation,” the Webmind form of energy, spreads through the network
    between nodes and links according to neural-net-like dynamics (embodying
    Peirce’s Law of Mind). Activation-spreading, in itself is goal-less and
    spontaneous, driven purely by the complex nonlinear dynamics of Peircean
    generalized association. But this isn’t the whole story: a substantial
    percentage of system activity is goal-directed. A goal activates things
    that help achieve it, also recording as lasting knowledge information
    about which things helped achieve it. Thus activation spreading, in
    large part, works in the service of activating things that can help
    fulfill basic system goals.
    < http://www.intelligenesis.net/webmindaiengine.html >

    KNOW (Knowledge Norm Of Webmind; likely be made public - open source?)
    is a language used for knowledge representation in Webmind. It is
    simpler and more cognitively natural than the predicate-logic knowledge
    representation languages used in many other AI systems, such as CYC. The
    main goals of KNOW are to enable

       * Humans to enter knowledge into Webmind unambiguously
       * Webmind to report its knowledge unambiguously
       * Simple import of knowledge from external resources (e.g. databases,
         other AI systems) into Webmind

    To serve these purposes, KNOW was designed to keep a subtle balance. In
    its relation to Webmind, the structure of the sentences in KNOW is
    closely related to how knowledge is represented within Webmind (i.e.,
    the nodes and links).

    A text in KNOW is composed of sentences, with each sentence having
    strength and confidence values. A sentence is a relation that has
    arguments. A relation can be either built-in or user-defined. The
    following example shows a simple text in KNOW:

    { [give John Mary book1 (1.0 0.9)]

    [Inheritance book1 book (1.0 0.9)]

    [author book1 John (1.0 0.9)] }

    This text in composed of three sentences. Give, inheritance and author
    are relations, and John, Mary, book and books are the arguments. A text
    in KNOW can also be represented in XML format.

    The complete KNOW knowledge representation grammar will likely be made
    public at some future point, to enable maximally fluid knowledge
    exchange between Webmind and other AI systems and computer programs.

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