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ref: work-0 tags: metacognition AI bootstrap machine learning Pitrat self-debugging date: 08-07-2010 04:36 gmt revision:7 [6] [5] [4] [3] [2] [1] [head]

Jacques Pitrat seems to have many of the same ideas that I've had (only better, and he's implemented them!)--

A Step toward and Artificial Scientist

  • The overall structure seems good - difficult problems are attacked by 4 different levels. First level tries to solve the problem semi-directly, by writing a program to solve combinatorial problems (all problems here are constraint based; constraints are used to pare the tree of possible solutions; these trees are tested combinatorially); second level monitors lower level performance and decides which hypotheses to test (which branch to pursue on the tree) and/or which rules to apply to the tree; third level directs the second level and restarts the whole process if a snag or inconsistency is found, forth level gauges the interest of a given problem and looks for new problems to solve within a family so as to improve the skill of the 3 lower levels.
    • This makes sense, but why 4? Seems like in humans we only need 2 - the actor and the critic, bootstrapping forever.
    • Also includes a "Zeus" module that periodically checks for infinite loops of the other programs, and recompiles with trace instructions if an infinite loop is found within a subroutine.
  • Author claims that the system is highly efficient - it codes constraints and expert knowledge using a higher level language/syntax that is then converted to hundreds of thousands of lines of C code. The active search program runs runtime-generated C programs to evaluate and find solutions, wow!
  • This must have taken a decade or more to create! Very impressive. (seems it took 2 decades, at least according to http://tunes.org/wiki/jacques_20pitrat.html)
    • Despite all this work, he is not nearly done - it has not "learning" module.
    • Quote: In this paper, I do not describe some parts of the system which still need to be developed. For instance, the system performs experiments, analyzes them and finds surprising results; from these results, it is possible to learn some improvements, but the learning module, which would be able to find them, is not yet written. In that case, only a part of the system has been implemented: on how to find interesting data, but still not on how to use them.
  • Only seems to deal with symbolic problems - e.g. magic squares, magic cubes, self-referential integer series. Alas, no statistical problems.
  • The whole CAIA system can effectively be used as a tool for finding problems of arbitrary difficulty with arbitrary number of solutions from a set of problem families or meta-families.
  • Has hypothesis based testing and backtracking; does not have problem reformulation or re-projection.
  • There is mention of ALICE, but not the chatbot A.L.I.C.E - some constraint-satisfaction AI program from the 70's.
  • Has a C source version of MALICE (his version of ALICE) available on the website. Amazingly, there is no Makefile - just gcc *.c -rdynamic -ldl -o malice.
  • See also his 1995 Paper: AI Systems Are Dumb Because AI Researchers Are Too Clever images/815_1.pdf

Artificial beings - his book.