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For the theory behind this see:

An Approximation of the Universal Intelligence Measure
by Shane Legg and Joel Veness, 2011

and for the theory behind that see:

Universal Intelligence: A formal definition of machine intelligence
by Shane Legg and Marcus Hutter, 2007

The code is released under the GNU GPLv3.  See Licence.txt file.

Known issues

Sometimes a stage in the adaptive sampler completes with a test missing,
or even with an additional test run, according to the log files.  It 
seems to happen about once every 10,000 trials, based on previous
experience.  It might be a numerical bug in the way in which the true
adaptive strata targets (which are floats) are converted into integers.
It might even be OS dependent.  I just spent an hour running it on a
Mac and couldn't replicate this bug again.  Anyway, it's not really going
to change anything.  Any new adaptive stratification allocation rounds
will correct for it, plus it's only something like 1 sample out of 10k
samples so any effect should be very small compared to the general
variability in the estimates.  If anybody does track it down, let me
know and I'll fix the code.

Be careful when looking at results from the program
if the log file in questions is still being computed.  The reason is
that the samples tends to run through the strata in order during each
stage and thus there can be some autocorrelation in the results due
to different strata having different mean values.  So either you should
really wait until the end of a stage for the results to be sensible.
In the results only appear at the end of stages so this isn't
an issue.

The CI estimates when the name of samples is low seems to be too low.
We have checked the equations and everything is as it should be in
the paper.  Furthermore, as the number of samples increase past 1000
or so, our empirical tests indicate that the CI's are indeed accurate.
Maybe it's a bug in the paper where this sampler is described?  We
don't know.  In any case, do wait for 1000 samples before you place
too much confidence in the estimated CI figures.

Outline of files and directories:

Conf Paper Settings.ods

This spreadsheet file (in open document format, open with OpenOffice
or LibreOffice) contains the settings used to obtain the results in
the AIQ conference paper. 

This is the main program to use to compute an agent's AIQ value for a
given reference machine, discount rate etc.


-a agent_name,param1,param2,...   

-r ref_machine_name,param1,param2,...

-d discount_rate

-l episode_length (if you don't give it it will compute one for you
 with 95% of the total infinite length episode reward covered)

-s sample_size Size of sample to use.

-n cluster_node Name of the cluster node.  Used for naming output log
 files and which sample file to read (need to think about latter
 aspect, maybe change).

-t threads_to_use Default is the number of cores on the machine.
   For a multi threaded agent you might want to set it to be less.

--log  Switch on output logging

--simple_mc Use a simple MC sample rather than the stratified sampler.
  Useful for sanity checks and also debugging as it doesn't do any
  async stuff etc.

An example run of AIQ would be:

python -r BF -a Q_l,0.0,0.5,0.5,0.05,0.9  -l 1000 -s 1000

which is a BF reference machine with a 5 symbol tape (this is the
default, you can specify other values), an episode length of 1000
with no discounting, and Q lambda with parameters...

If you want to try BF with, say, a 14 symbol tape, you'll first need
to generate a program sample file for this (see below).  We only
include our BF5 sample file in github to save space. 

Give it a log file name and it will compute the AIQ as well as results
for each strata.  At the moment you can't combined logs simply because
the first line is the program sample distribution information (needed
to work out the stratified estimate of the AIQ value).

You can provide it with multiple file names, e.g. with a *, but they
are all computed individually.

The --full option reports also the strata statistics.


The raw results of runs are dumped here if logging is turned on with
the -log option in AIQ.

First line of a log file is the estimated strata distribution computed
by AIQ from the sample file, followed by lines containing a time
stamp, strata number, and result.  The file name contains the Agent,
Reference machine, discount, episode length.  You can then quickly
compute the AIQ from one of these log files later on using the program (see above)


This contains the code for the various agents.  Base class for agents Agent that takes random actions  Slightly smarter agent that looks at reward associated with actions.  Q learning with eligibility traces.  Like Q learning but with an automatic learning rate. Wrapper for Monte Carlo AIXI agent.  Must have an
executable call mc-aixi in this directory in order to run.  C++ code
for MC-AIXI can be downloaded from the internet.


This contains the code for the reference machines.  Base class for reference machines BF based reference machine.  Take parameters for the number of
symbols (i.e. alphabet size, default is 3) and the number of cells
that the observations use (default is 1).  Actions and rewards are
still fixed at 1 tape cell. Generates samples of BF programs, works out their
strata, and outputs these to the terminal.  You'll want to stick these
in a sample file.  You have to name the file correctly yourself to
match what AIQ expects.  The -s option tell it how many samples to
generate. The file consists of just rows of samples so you can
concatenate the output of different runs to make a combined sample


Directory of program samples along with there strata.  Files
are named by the reference machine (including parameters) followed by
.sample.  This saves AIQ having to generate new samples and work out
what strata they are in, and AIQ also computes the estimated true
strata probabilities based on this sample.  So make it reasonable
large.  Say 100k programs for proper tests.  Use to
generate these for the BF reference machine.  We include a sample
file for a 5 symbol tape to get you started.


Algorithmic Intelligence Quotient







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