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FAQ: comp.ai.genetic part 2/6 (A Guide to Frequently Asked Questions)
Section - Q1.4: What's a Classifier System (CFS)?

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See reader questions & answers on this topic! - Help others by sharing your knowledge
 The name of the Game
     First,  a word on naming conventions is due, for no other paradigm of
     EC has undergone more changes  to  its  name  space  than  this  one.
     Initially,  Holland  called his cognitive models "Classifier Systems"
     abbrv. with CS, and sometimes CFS, as can be found in [GOLD89].

     Whence Riolo came into play in 1986 and Holland added a reinforcement
     component to the overall design of a CFS, that emphasized its ability
     to learn. So, the word "learning" was prepended to the name, to make:
     "Learning  Classifier Systems" (abbrv. to LCS).  On October 6-9, 1992
     the "1st Inter'l Workshop on Learning Classifier Systems" took  place
     at  the  NASA  Johnson  Space Center, Houston, TX.  A summary of this
     "summit" of all leading researchers in LCS can  be  found  on  ENCORE
     (See Q15.3) in file CFS/papers/lcs92.ps.gz

     Today, the story continues, LCSs are sometimes subsumed under a "new"
     machine  learning   paradigm   called   "Evolutionary   Reinforcement
     Learning" or ERL for short, incorporating LCSs, "Q-Learning", devised
     by Watkins (1989), and a paradigm of the same name, devised by Ackley
     and Littman [ALIFEIII].

     And  then,  this  latter  statement  is really somewhat confusing, as
     Marco Dorigo has pointed out in a letter to editors  of  this  guide,
     since  Q-Learning  has  no  evolutionary component. So please let the
     Senior Editor explain: When I wrote this part of the  guide,  I  just
     had  in mind that Q-Learning would make for a pretty good replacement
     of  Holland's  bucket-brigade  algorithm,  so  I  used   this   litte
     speculation to see what comes out of it; in early December 95, almost
     two years  later,  it  has  finally  caught  Marco's  attention.  But
     meanwhile,  I  have been proven right: Wilson has suggested to use Q-
     Learning in CLASSIFIER SYSTEM (Wilson  1994)  and  Dorigo  &  Bersini
     (1994)  have  shown  that Q-Learning and the bucket-brigade are truly
     equivalent concepts.

     We would therefore be allowed to call a CFS that uses Q-Learning  for
     rule  discovery,  rather than a bucket-brigade, a Q-CFS, Q-LCS, or Q-
     CS; in any case would the result be subsumed under the term ERL, even
     if Q-Learning itself is not an EVOLUTIONARY ALGORITHM!

     Interestingly,  Wilson  called  his  system  ZCS  (apparantly  no "Q"
     inside), while Dorigo & Bersini called their system a D-Max-VSCS,  or
     "discounted  max  very  simple classifier system" (and if you know Q-
     Learning at least the D-Max part of the name will remind you of  that
     algorithm).   The  latter paper can be found on Encore (see Q15.3) in
     file CFS/papers/sab94.ps.gz

     And by the way in [HOLLAND95] the term  "classifier  system"  is  not
     used  anymore.  You cannot find it in the index. Its a gone!  Holland
     now stresses the adaptive component  of  his  invention,  and  simply
     calls  the  resulting systems adaptive agents.  These agents are then
     implemented within the framework of his recent invention called ECHO.
     (See http://www.santafe.edu/projects/echo for more.)

     Alwyn  Barry's  LCS  Web has a great deal of information on LCS. See:
     http://www.csm.uwe.ac.uk/~ambarry/LCSWEB/

 On Schema Processors and ANIMATS
     So, to get back to the question above, "What  are  CFSs?",  we  first
     might  answer,  "Well,  there  are  many interpretations of Holland's
     ideas...what do you like to know in particular?"  And then we'd start
     with  a recitation from [HOLLAND75], [HOLLAND92], and explain all the
     SCHEMA processors, the broadcast language, etc.  But, we will take  a
     more  comprehensive,  and intuitive way to understand what CLASSIFIER
     SYSTEMs are all about.

     The easiest road to explore the very nature of classifier systems, is
     to take the animat (ANIMAl + ROBOT = ANIMAT) "lane" devised by Booker
     (1982) and later studied  extensively  by  Wilson  (1985),  who  also
     coined  the  term for this approach. Work continues on animats but is
     often  regarded  as  ARTIFICIAL   LIFE   rather   than   EVOLUTIONARY
     COMPUTATION.   This  thread  of  research has even its own conference
     series: "Simulation of Adaptive Behavior (SAB)" (cf  Q12),  including
     other   notions   from   machine  learning,  connectionist  learning,
     evolutionary robotics, etc.  [NB: the latter is obvious, if an animat
     lives  in  a  digital microcosm, it can be put into the real world by
     implantation   into   an   autonomous   robot   vehicle,   that   has
     sensors/detectors   (camera   eyes,  whiskers,  etc.)  and  effectors
     (wheels, robot arms, etc.); so  all  that's  needed  is  to  use  our
     algorithm  as  the  "brain"  of this vehicle, connecting the hardware
     parts with the software learning component.  For a fascinating  intro
     to the field see, e.g. Braitenberg (1984)]

     classifier  systems,  however,  are  yet  another  offspring  of John
     Holland's aforementioned book, and can be seen as one  of  the  early
     applications  of  GAs,  for  CFSs  use this EVOLUTIONARY ALGORITHM to
     adapt their behavior toward a changing ENVIRONMENT, as  is  explained
     below in greater detail.

     Holland  envisioned  a  cognitive  system  capable of classifying the
     goings on in its environment, and then reacting to  these  goings  on
     appropriately.  So  what is needed to build such a system? Obviously,
     we need (1) an environment; (2) receptors that tell our system  about
     the  goings  on;  (3)  effectors,  that let our system manipulate its
     environment; and (4) the system itself, conveniently a "black box" in
     this first approach, that has (2) and (3) attached to it, and "lives"
     in (1).

     In the animat  approach,  (1)  usually  is  an  artificially  created
     digital  world,  e.g.  in Booker's Gofer system, a 2 dimensional grid
     that contains "food" and "poison".  And the Gofer itself, that  walks
     across  this grid and tries (a) to learn to distinguish between these
     two items, and (b) survive well fed.

     Much the same for Wilson's animat,  called  "*".  Yes,  its  just  an
     asterisk,  and a "Kafka-esque naming policy" is one of the sign posts
     of the whole field; e.g. the  first  implementation  by  Holland  and
     Reitmann  1978  was  called CS-1, (cognitive system 1); Smith's Poker
     player LS-1 (1980)  followed  this  "convention".  Riolo's  1988  LCS
     implementations  on  top  of  his CFS-C library (cf Q20), were dubbed
     FSW-1 (Finite State World 1), and LETSEQ-1 (LETter SEQuence predictor
     1).

     So  from  the  latter  paragraph we can conclude that environment can
     also mean something completely different (e.g. an infinite stream  of
     letters,  time  serieses,  etc.)  than  in  the  animat approach, but
     anyway; we'll stick to it, and go on.

     Imagine a very simple animat, e.g. a  simplified  model  of  a  frog.
     Now,  we  know  that  frogs  live  in (a) Muppet Shows, or (b) little
     ponds; so we chose the latter as our demo environment  (1);  and  the
     former  for  a  non-Kafka-esque  name  of  our model, so let's dub it
     "Kermit".

     Kermit has eyes, i.e. sensorial input detectors (2); hands and  legs,
     i.e.    environment-manipulating   effectors  (3);  is  a  spicy-fly-
     detecting-and-eating device, i.e. a frog (4); so we  got  all  the  4
     pieces needed.

 Inside the Black Box
     The most primitive "black box" we can think of is a computer.  It has
     inputs (2), and outputs (3), and a message passing system  inbetween,
     that  converts  (i.e.,  computes), certain input messages into output
     messages, according to a set of rules, usually called  the  "program"
     of that computer.  From the theory of computer science, we now borrow
     the simplest of all program  structures,  that  is  something  called
     "production  system"  or  PS  for  short.   A PS has been shown to be
     computationally complete by Post (1943), that's why it  is  sometimes
     called  a  "Post  System",  and  later by Minsky (1967).  Although it
     merely consists of a set of if-then rules, it still resembles a full-
     fledged computer.

     We  now  term  a  single  "if-then" rule a "classifier", and choose a
     representation that makes it easy to manipulate these, for example by
     encoding  them  into  binary  strings.   We  then  term  the  set  of
     classifiers, a "classifier population", and immediately know  how  to
     breed  new  rules  for  our  system:  just  use  a GA to generate new
     rules/classifiers from the current POPULATION!

     All that's left are the messages  floating  through  the  black  box.
     They  should also be simple strings of zeroes and ones, and are to be
     kept in a data structure, we call "the message list".

     With all this given, we can imagine the goings on  inside  the  black
     box as follows: The input interface (2) generates messages, i.e., 0/1
     strings, that are written on the message list.  Then  these  messages
     are  matched  against  the condition-part of all classifiers, to find
     out which actions are to be triggered.   The  message  list  is  then
     emptied,  and  the  encoded  actions,  themselves  just messages, are
     posted to the message list.  Then, the output  interface  (3)  checks
     the message list for messages concerning the effectors. And the cycle
     restarts.

     Note, that it is possible in this set-up to have "internal messages",
     because  the message list is not emptied after (3) has checked; thus,
     the input interface messages are added to the initially  empty  list.
     (cf Algorithm CFS, LCS below)

     The  general  idea  of  the  CFS is to start from scratch, i.e., from
     tabula rasa  (without  any  knowledge)  using  a  randomly  generated
     classifier  population,  and  let  the  system  learn  its program by
     induction, (cf Holland et al. 1986), this reduces the input stream to
     recurrent  input patterns, that must be repeated over and over again,
     to enable the animat to classify its  current  situation/context  and
     react on the goings on appropriately.

 What does it need to be a frog?
     Let's  take a look at the behavior emitted by Kermit. It lives in its
     digital microwilderness where it moves around  randomly.   [NB:  This
     seemingly  "random"  behavior  is not that random at all; for more on
     instinctive, i.e., innate behavior  of  non-artificial  animals  see,
     e.g.  Tinbergen (1951)]

     Whenever  a  small flying object appears, that has no stripes, Kermit
     should eat it, because its very likely a spicy fly, or  other  flying
     insect.   If it has stripes, the insect is better left alone, because
     Kermit had better not bother with wasps, hornets, or bees.  If Kermit
     encounters a large, looming object, it immediately uses its effectors
     to jump away, as far as possible.

     So, part of these behavior patterns within the "pond  world",  in  AI
     sometimes called a "frame," from traditional knowledge representation
     techniques, Rich (1983), can be expressed in a set of "if <condition>
     then <action>" rules, as follows:

	  IF small, flying object to the left THEN send @
	  IF small, flying object to the right THEN send %
	  IF small, flying object centered THEN send $
	  IF large, looming object THEN send !
	  IF no large, looming object THEN send *
	  IF * and @ THEN move head 15 degrees left
	  IF * and % THEN move head 15 degrees right
	  IF * and $ THEN move in direction head pointing
	  IF ! THEN move rapidly away from direction head pointing

     Now,  this set of rules has to be encoded for use within a CLASSIFIER
     SYSTEM.  A possible encoding of the above rule set in  CFS-C  (Riolo)
     classifier   terminology.   The   condition   part  consists  of  two
     conditions, that are combined with a logical AND, thus  must  be  met
     both  to  trigger  the  associated action. This structure needs a NOT
     operation, (so we get NAND, and know from hardware  design,  that  we
     can  build  any computer solely with NANDs), in CFS-C this is denoted
     by the `~' prefix character (rule #5).

	  IF             THEN
	   0000,  00 00  00 00
	   0000,  00 01  00 01
	   0000,  00 10  00 10
	   1111,  01 ##  11 11
	  ~1111,  01 ##  10 00
	   1000,  00 00  01 00
	   1000,  00 01  01 01
	   1000,  00 10  01 10
	   1111,  ## ##  01 11
     Obviously, string `0000' denotes small, and `00' in the fist part  of
     the  second  column,  denotes flying.  The last two bits of column #2
     encode the direction of the  object  approaching,  where  `00'  means
     left, `01' means right, etc.

     In  rule  #4  a the "don't care symbol" `#' is used, that matches `1'
     and `0',  i.e.,  the  position  of  the  large,  looming  object,  is
     completely   arbitrary.   A  simple  fact,  that  can  save  Kermit's
     (artificial) life.

 PSEUDO CODE (Non-Learning CFS)
     Algorithm CFS is

	  // start with an initial time
	  t := 0;

	  // an initially empty message list
	  initMessageList ML (t);

	  // and a randomly generated population of classifiers
	  initClassifierPopulation P (t);

	  // test for cycle termination criterion (time, fitness, etc.)
	  while not done do

	       // increase the time counter
	       t := t + 1;

	       // 1. detectors check whether input messages are present
	       ML := readDetectors (t);

	       // 2. compare ML to the classifiers and save matches
	       ML' := matchClassifiers ML,P (t);

	       // 3. process new messages through output interface
	       ML := sendEffectors ML' (t);
	  od
     end CFS.

     To convert the previous, non-learning CFS into a learning  CLASSIFIER
     SYSTEM,  LCS,  as  has  been proposed in Holland (1986), it takes two
     steps:

     (1)   the major cycle has to be changed such that the  activation  of
	   each  classifier depends on some additional parameter, that can
	   be modified as a result of experience, i.e. reinforcement  from
	   the ENVIRONMENT;

     (2)   and/or  change  the  contents  of  the  classifier  list, i.e.,
	   generate  new  classifiers/rules,  by  removing,   adding,   or
	   combining condition/action-parts of existing classifiers.

     The algorithm thus changes accordingly:

 PSEUDO CODE (Learning CFS)
     Algorithm LCS is

	  // start with an initial time
	  t := 0;

	  // an initially empty message list
	  initMessageList ML (t);

	  // and a randomly generated population of classifiers
	  initClassifierPopulation P (t);

	  // test for cycle termination criterion (time, fitness, etc.)
	  while not done do

	       // increase the time counter
	       t := t + 1;

	       // 1. detectors check whether input messages are present
	       ML := readDetectors (t);

	       // 2. compare ML to the classifiers and save matches
	       ML' := matchClassifiers ML,P (t);

	       // 3. highest bidding classifier(s) collected in ML' wins the
	       // "race" and post the(ir) message(s)
	       ML' := selectMatchingClassifiers ML',P (t);

	       // 4. tax bidding classifiers, reduce their strength
	       ML' := taxPostingClassifiers ML',P (t);

	       // 5. effectors check new message list for output msgs
	       ML := sendEffectors ML' (t);

	       // 6. receive payoff from environment (REINFORCEMENT)
	       C := receivePayoff (t);

	       // 7. distribute payoff/credit to classifiers (e.g. BBA)
	       P' := distributeCredit C,P (t);

	       // 8. Eventually (depending on t), an EA (usually a GA) is
	       // applied to the classifier population
	       if criterion then
		    P := generateNewRules P' (t);
	       else
		    P := P'
	  od
     end LCS.

 What's the problem with CFSs?
     Just  to list the currently known problems that come with CFSs, would
     take some additional pages; therefore only  some  interesting  papers
     dealing  with  unresolved riddles are listed; probably the best paper
     containing most of these is the aforementioned  summary  of  the  LCS
     Workshop:

     Smith,  R.E.  (1992) "A report on the first Inter'l Workshop on LCSs"
     avail. from ENCORE (See Q15.3) in file CFS/papers/lcs92.ps.gz

     Other noteworthy critiques on LCSs include:

     Wilson, S.W. (1987)  "Classifier  Systems  and  the  Animat  Problem"
     Machine Learning, 2.

     Wilson,  S.W.  (1988)  "Bid Competition and Specificity Reconsidered"
     Complex Systems, 2(5):705-723.

     Wilson, S.W. & Goldberg, D.E. (1989) "A critical review of classifier
     systems" [ICGA89], 244-255.

     Goldberg,  D.E., Horn, J. & Deb, K. (1992) "What makes a problem hard
     for a classifier system?"  (containing the Goldberg  citation  below)
     is    also    available    from    Encore   (See   Q15.3)   in   file
     CFS/papers/lcs92-2.ps.gz

     Dorigo, M. (1993) "Genetic  and  Non-genetic  Operators  in  ALECSYS"
     Evolutionary  Computation,  1(2):151-164.   The technical report, the
     journal article is based on is avail. from Encore (See Q15.3) in file
     CFS/papers/icsi92.ps.gz

     Smith,  R.E.  Forrest,  S.  &  Perelson,  A.S.  (1993) "Searching for
     Diverse,   Cooperative   POPULATIONs   with    Genetic    Algorithms"
     Evolutionary Computation, 1(2):127-149.

 Conclusions?
     Generally speaking:  "There's much to do in CFS research!"

     No  other  notion of EC provides more space to explore and if you are
     interested in a PhD in the field, you might want  to  take  a  closer
     look  at  CFS.   However,  be warned!, to quote Goldberg: "classifier
     systems  are  a  quagmire---a  glorious,  wondrous,  and    inventing
     quagmire, but a quagmire nonetheless."

     References

     Booker, L.B. (1982) "Intelligent behavior as an adaption to the  task
     environment" PhD Dissertation, Univ. of Michigan, Logic of  Computers
     Group, Ann Arbor, MI.

     Braitenberg,   V.   (1984)   "Vehicles:   Experiments   in  Synthetic
     Psychology" Boston, MA: MIT Press.

     Dorigo M. & H.  Bersini  (1994).  "A  Comparison  of  Q-Learning  and
     Classifier  Systems."   Proceedings of From Animals to Animats, Third
     International Conference on SIMULATION of Adaptive Behavior  (SAB94),
     Brighton,  UK, D.Cliff, P.Husbands, J.-A.Meyer and S.W.Wilson (Eds.),
     MIT                          Press,                          248-255.
     http://iridia.ulb.ac.be/dorigo/dorigo/conferences/IC.11-SAB94.ps.gz

     Holland,  J.H.  (1986)  "Escaping  Brittleness:  The possibilities of
     general-purpose learning algorithms applied  to  parallel  rule-based
     systems".  In:  R.S. Michalski, J.G. Carbonell & T.M. Mitchell (eds),
     Machine  Learning:  An  Artificial  Intelligence  approach,  Vol  II,
     593-623, Los Altos, CA: Morgan Kaufman.

     Holland,  J.H.,  et  al.  (1986)  "Induction: Processes of Inference,
     Learning, and Discovery", Cambridge, MA: MIT Press.

     Holland, J.H. (1992) "Adaptation in natural and  artificial  systems"
     Boston, MA: MIT Press.

     Holland, J.H. (1995) "Hidden Order: How adaptation builds complexity"
     Reading, MA: Addison-Wesley. [HOLLAND95]:

     Holland, J.H. & Reitman, J.S.  (1978)  "Cognitive  Systems  based  on
     Adaptive  Algorithms" In D.A. Waterman & F.Hayes-Roth, (eds) Pattern-
     directed inference systems. NY: Academic Press.

     Minsky,  M.L.   (1961)   "Steps   toward   Artificial   Intelligence"
     Proceedings  IRE, 49, 8-30. Reprinted in E.A. Feigenbaum & J. Feldman
     (eds) Computers and Thought, 406-450, NY: McGraw-Hill, 1963.

     Minsky, M.L.  (1967)  "Computation:  Finite  and  Infinite  Machines"
     Englewood Cliffs, NJ: Prentice-Hall.

     Post,  Emil L. (1943) "Formal reductions of the general combinatorial
     decision problem" American Journal of Mathematics, 65, 197-215.
     Rich, E. (1983) "Artificial Intelligence" NY: McGraw-Hill.

     Tinbergen, N. (1951) "The Study of Instinct" NY: Oxford Univ.  Press.

     Watkins,  C. (1989) "Learning from Delayed Rewards" PhD Dissertation,
     Department of Psychology, Cambridge Univ., UK.

     Wilson, S.W. (1985) "Knowledge growth in  an  artificial  animal"  in
     [ICGA85], 16-23.

     Wilson,  S.W.  (1994)  "ZCS:  a zeroth level classifier system" in EC
     2(1), 1-18.

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