seminar announcement: Modeling the Sequential Behavior of the Dog

[Gary Cottrell]

                                     SEMINAR

                   Modeling the Sequential Behavior of the Dog:
                      The Second Naive Dog Physics Manifesto

                               Garrison W. Cottrell
                            Department of Dog Science
               Condominium Community College of Southern California


          Most work in Dog Modeling has been content to make do  with  simple
     Stimulus-Response  type  models.   However,  the  thing  that  separates
     current work in Parallel Dog Processing from  the  Behaviorists  is  the
     emphasis  on  looking  inside  the  dog's  head.   So far, few dogs have
     consented to this procedure, hence, we have to make do with models  that
     tell  us  what we might find if we looked.  S-R models since Pavlov have
     assumed that there is not much in the head except a connection from  the
     dog's  nose  to his salivary gland, that may be detached at the nose end
     and reconnected to his  ear  via  a  process  called  "conditioning"[1].
     Departing   from   the  Behaviorists,  PDP  modelers  make  the  radical
     assumption that there is a brain inside  the  dog's  head[2],  mediating
     these reponses based on the current state of the dog's brain.   However,
     rather  than  treating  the  dog's  brain  as  analogous  to a telephone
     switching network as the neo-Skinnerians do[3], we will treat the dog as
     a  dynamical  system,  in particular, a dissipative system that takes in
     material from its environment,  extracts  energy  to  maintain  its  own
     structure, increasing the entropy of the material before returning it to
     the environment.  The main problem of the dog owner, then, is  to  train
     this  dynamical  system  to leave its entropy outside the house.  In our
     work this sequence of desired behavior is  specified  by  the  following
     action  grammar,  a  simplified  version  of  the one used in (Cottrell,
     1986a):

       Day -> Action Day | Sleep             Action -> Eat | leavecondo Walk

       Eat -> Eat chomp | chomp

       Walk -> poop Walk | trot Walk | sniff Walk | entercondo


     As previously noted,  these  rules  have  the  desirable  property  that
     entropy in the condo is ungrammatical.

          In our previous work (Cottrell, 1986a), we took a competence theory
     approach,  i.e.,  no  working  computer  program  was  necessary for our
     theory.  While the advantages of the lack of real world constraints that
     a competence theory approach allows are clear[4], it lacks the advantage
     of interchange with experiment that performance theories enjoy.  In this
     talk  we  will  describe  an  approach  that  avoids  the  pitfalls of a
     performance theory (having to deal with data)  while  incorporating  the
     exchange  with experimental modeling by building a computer model of our
     competence theory[5].

          In order to generate a sequence  such  as  that  specified  by  our
     action  grammar, a recurrent network is necessary.  To model the initial
     state of the de novo dog, we  start  with  a  randomly  wired  recurrent
     network  with  habituation in the weights.  The behavior of this network
     is  remarkably  similar  to  that  of  the  puppy,  oscillating  wildly,
     exhibiting totally undisciplined behavior, until reaching a fixed point.
     Habituation then determines the length of the  sleep  phase,  the  model
     slowly "wakes up", and the cycle starts again[6].  We then apply the WiZ
     algorithm  (Willy & Zippy, 1988) for recurrent bark propagation to train
     the network to the adult behavior[7].  The training set of sequences  of
     states  was  generated from the simplified grammar above.  Note that the
     network must actually choose a branch of the  grammar  to  take  on  any
     iteration.   By  simply  training the network to take different branches
     from a nonterminal state on different occasions, the network is unstable
     when  at  a  nonterminal  node.   Different  actions  are  then possible
     attractors from that state.  By using a random updating rule,  different
     transitions  naturally  occur.  Transitions out of a particular terminal
     state are due to habituation, i.e., our dog model stops doing  something
     because of the equivalent  of  boredom  with  any  particular  state[8].
     Thus, boredom is an emergent property of our model.
     ____________________

        [1]The obvious implausibility of such a process notwithstanding  (cf.
     Chompski's scathing critique, No Good Dogs, 1965), hordes of researchers
     have spent many years studying it.
        [2]However,  it is often hard to explain this view to the lay public,
     especially most dog owners.
        [3]This was a major improvement on older behaviorist  theories.   All
     that  is  needed  now is to posit that conditioning somehow accesses the
     "telephone operator" in the brain that pulls a  plug  and  reinserts  it
     somewhere  else.  This model is much more plausible since the mechanisms
     of conditioning have been fleshed out.  It also explains why dogs  some-
     times don't respond at all - they haven't kept up on the phone bill.
        [4]For  example, in (Cottrell, 1986a) we were able to assume that one
     could generate a context free language with a feed-forward network.  All
     you needed was "hidden units".  This is the familiar cry of the "connec-
     tionist" who has never implemented a network.
        [5]This is known as the autoerotic approach to  theory  building.   A
     danger  here is that, since Reality has no reason to dampen the possible
     oscillations between computer simulation and  competence  theory  forma-
     tion,  the  process may have positive Lyapunov exponents, and never con-
     verge on a theory.  Such unstable loops can lead to  strange  attractors
     that never settle down, such as current linguistic theory.
        [6]Since  the  length of time spent in the attractor is determined by
     the number of units participating in it, it was found that most  of  the
     puppy's  brain  is  actually  needed  for maintaining its extremely long
     sleep phase.  This could be an entirely new explanation of the  apparent
     lack of capacity for much else.
        [7]In order to get the proper behavior out of our  network,  teacher-
     forcing  was  necessary.   This confirms our experience with actual dogs
     that force is often a necessary component of the training process.
        [8]This is obviously an inadequate model of how the dog stops eating,
     which is more due to external reality than any internal control  on  the
     dog's  part.  For this simple process, a Skinnerian feedforward See-Food
     -> Eat network is sufficient.