"Measuring universal intelligence: Towards an anytime intelligence test"; abstract:
In this paper, we develop the idea of a universal anytime intelligence test. The meaning of the terms “universal” and “anytime” is manifold here: the test should be able to measure the intelligence of any biological or artificial system that exists at this time or in the future. It should also be able to evaluate both inept and brilliant systems (any intelligence level) as well as very slow to very fast systems (any time scale). Also, the test may be interrupted at any time, producing an approximation to the intelligence score, in such a way that the more time is left for the test, the better the assessment will be. In order to do this, our test proposal is based on previous works on the measurement of machine intelligence based on Kolmogorov complexity and universal distributions, which were developed in the late 1990s (C-tests and compression-enhanced Turing tests). It is also based on the more recent idea of measuring intelligence through dynamic/interactive tests held against a universal distribution of environments. We discuss some of these tests and highlight their limitations since we want to construct a test that is both general and practical. Consequently, we introduce many new ideas that develop early “compression tests” and the more recent definition of “universal intelligence” in order to design new “universal intelligence tests”, where a feasible implementation has been a design requirement. One of these tests is the “anytime intelligence test”, which adapts to the examinee's level of intelligence in order to obtain an intelligence score within a limited time.
Example popular media coverage: http://www.sciencedaily.com/releases/2011/01/110127131122.htm
The group's homepage: http://users.dsic.upv.es/proy/anynt/
(There's an applet but it seems to be about constructing a simple agent and stepping through various environments, and no working IQ test.)
The basic idea, if you already know your AIXI*, is to start with simple programs** and then test the subject on increasingly harder ones. To save time, boring games such as random environments or one where the agent can 'die'*** are excluded and a few rules added to prevent gaming the test (by, say, deliberately failing on harder tests so as to be given only easy tests which one scores perfectly on) or take into account how slow or fast the subject makes predictions.
* apparently no good overviews of the whole topic AIXI but you could start at http://www.hutter1.net/ai/aixigentle.htm or http://www.hutter1.net/ai/uaibook.htm
** simple as defined by Kolmogorov complexity; since KC is uncomputable, one of the computable variants - which put bounds on resource usage - is used instead
*** make a mistake which turns any future rewards into fixed rewards with no connection to future actions
This seems like a glaring bias in algorithm design... like it is somehow building anthropic craziness into the definition of intelligence on purpose? If I was trying to design something that maximized its score it would never bother to consider situations of possible death because they "don't count" in the context it is practicing within, where it is presumably deploying priors/habits/heuristics for use in other contexts. Generalizing this insight to other design or educational processes is kind of worry inducing...
'die' is my own term, since it seemed to be the game term analogous to 'when an agent makes a move that renders it causally unconnected to all future rewards' (again, my own description).
The problem with including games in which one can 'die' is that they take much longer to learn. Suppose the agent the first time it plays the game happens to 'die', and now it only experiences a steady stream of 1,1,1,1,1... (low rewards). Nothing it does changes its future rewards, so exploration (trying different moves) is penalized. Dying on the first move might look like a good strategy!
Imagine if the rules looked like this: die~>1,1,1,1,...; not-die~>either -1 or +10. If the agent first tried out die, saw the +1 rewards, then the next game chose not-die and got -1, it may permanently start exploring down the die branch. An agent might eventually go back and try the not-die route and finally discover the +10, but this would take a while and is at odds with the idea of a reasonably quickly administered IQ test. Better to exclude such tests and switch to a more complex one.
Yes, now that I think about it, I guess their formalism tends towards incredibly low-signal environments where the actions are primarily simple "tokens" that can be named suggestively but aren't capable of actually revealing the data needed for the kind of sophistication I'm thinking of. That is, The environment is generally incapable of displaying an environmental tag that would suggest "novel action X (unlike novel actions Y or Z) could be dramatic and irreversible".
The only way to acquire such insight in a totally "from scratch" game context is to gain experience of having "died" after choosing X (probably several times), or else by having substantially richer environment cues than is normal for systems like this, where concepts like "reversibility" and "predictors of payoff size" could be worked out in trivial contexts and then correctly applied to more significant contexts later on, based on environmental cues that allow the model-based inference of both potential irreversibility and great importance in moderately novel situations.
Legg and Joel Veness have been working on a similar idea: http://www.vetta.org/2011/11/aiq/ But their approach, unsurprisingly, differs. From section 5 discussing OP and earlier approaches:
See Kaj's more recent list of articles: http://lesswrong.com/lw/h4x/intelligence_metrics_and_decision_theories/8ptd
It seems as though they are trying to take credit for Shane Legg's moves.
If you read the paper, I believe Legg is credited a number of times as are Hutter and Schmidhuber.