Anytime Universal Intelligence

Project Summary

(March 2009)




One of the most debated issues in the field of artificial intelligence, cognitive science, psychometrics, biology and anthropology is the definition of intelligence and its measurement.


Measurement of human intelligence is of utmost relevance for many application areas. Consequently, psychometric tests are common in order to (i) help children and students learn efficiently (education), (ii) to assess personnel (selection and recruitment) and also (iii) when treating different diseases and learning problems (learning therapies).


In a quite similar way, measurements of machine intelligence are required for areas such as (i) making agents, robots, and other kinds of “intelligent systems” acquire knowledge quickly (knowledge acquisition), (ii) in order to select/assess the abilities of “intelligent systems” (accreditation/certification) and also (iii) when trying to fix or improve the capabilities of these systems (intelligent system design or amendment).


Although the terminology is different, the similarities of both areas are clear. Additionally, a third germane area is comparative psychology or comparative cognition for several kinds of animals, where there is also a debate about how to assess cognitive abilities in higher animals (especially great apes and cetaceans).


The case of machine intelligence is especially relevant because of several issues:

-      A science or technology cannot advance if we do not have proper measurement techniques to assess its progress. It is difficult to develop aeronautics even from watching birds fly if we do not have measurements for weight, altitude or aerodynamics. Artificial Intelligence has a reference (Homo sapiens) but suffers a lack of measures and measurement techniques for its artifacts.

-      The ubiquity of bots, assistants, majordomos and other kinds of autonomous agents who perform tasks on behalf of humans, makes it difficult to appropriately deal with them in a new space for collaboration where virtual interaction substitutes physical interaction, such as collaborative platforms, social networks, service-oriented architectures, e-bureaucracy, Web 2.0, etc. In many applications we need to differentiate between stupid single-task bots, general and more intelligent bots, and humans.

o   On the one hand, some applications require allowing bots to participate in projects or granting them permissions as interlocutors for management, negotiations or agreements. Therefore, bots are expected to be effective to carry on their delegated tasks, for which they need to be intelligent (non-intelligent bots are to be avoided)

o   On the other hand, in some cases, we want to prevent bots of all kinds from doing certain tasks (e.g. malicious bots could create millions of accounts in a free email service). For this purpose, CAPTCHAs  [von Ahn et al 2002] [von Ahn et al 2008] are widely used, but more precise and reliable mechanisms will be needed in the near-future.


Let us give an example. Imagine a collaborative environment, where several agents (some of them humans and some of them machines), have to build a specialized website with information about a specific topic (e.g. a Wikipedia entry, a European project outline, a recent event, a music library, a geographical project, etc). The team will have to decide the rules of interaction (how information is accepted, integrated and published). In order to become part of the team, each component will need to certify that she/he/it has some basic reasoning/learning abilities. If an inept agent (or person) tries to join the club we would require some kind of assessment to decide whether we should let him/her/it in.


Are mechanisms like this available nowadays? The answer is no. Apart from CAPTCHAs, which only measure some very specific abilities, we do not have any kind of test where we can quickly and reliably evaluate the intelligence of an intelligent system (an agent).


The purpose of this project is to explore the possibility of constructing tests which are:

·      Universal. They can be applied to humans, bots, animals, communities or even hybrids. In order to meet this property the test must be derived from universal non-anthropomorphic principles grounded on computer science and information theory. This goal, by itself, is very challenging since actual tests are based on anthropomorphic assumptions.

·      Anytime. They can adapt to the time-scale of the examinee, and can dynamically adapt its questions, interactions or items to the examinee, in order to assess its intelligence quicker. The test should be flexible enough to test very quickly (with low reliability) for some applications (e.g. applications where CAPTCHAs are used today) or to provide high reliability estimations if more time is provided (e.g. applications in which serious consequences derive from a stupid agent meddling around).




There are two kinds of tests that are of interest in this area: psychometric tests and machine intelligence tests.


Psychometric tests [Martínez-Arias et al 2006] have a long history [Spearman 1908], they are effective, easy to administer, fast and quite stable when used on the same individual across time. In fact, they have provided one of the best practical definitions of intelligence: “intelligence is what is measured by intelligence tests”. However, psychometric tests are anthropomorphic; they cannot evaluate the intelligence of systems other than Homo sapiens.


New approaches in psychometrics such as Item Response Theory (IRT) allows for item selection based on their cognitive demand features, providing results to understand what is being measured and adapting the test to the level of the individual being examined. Items generated from cognitive theory and analysed from IRT are a promising tool, but these models are not fully implemented in testing [Embretson & Mc Collam 2000].


Even though these and other efforts have tried to establish “a priori” what an intelligence test should be (e.g. [Embretson 1998] and then find adaptations for different kinds of subjects, in general we need different versions of the psychometric tests to evaluate children at different ages, since the psychometric tests for adult Homo sapiens rely on some knowledge and skills that children have not acquired yet.


The same happens for other animals. Comparative psychologists and other scientists in the area of comparative cognition usually devise specific tests for different species. An example of these specialised tests for children and apes can be found here [Herrmann et al 2007]. Additionally, it has been shown that psychometric tests do not work for machines at the current stage of progress in artificial intelligence [Sanghi and Dowe 2003], since they can be tricked by very simple computer programs. But the main drawback of psychometric tests to evaluate other subjects different from humans is that there is no mathematical definition behind them. Hence it is difficult to devise a test which will work on any intelligent subject (machine, human, animal ..), and not only on the specific kind of subjects we have experimentally evaluated that they work on.


Machine intelligence tests have been proposed since Alan Turing [Turing 1950] introduced the imitation game in 1950, now commonly known as Turing test. In this test, a system is considered intelligent if it is able to imitate a human (i.e., to be undistinguishable from a human) during a period of time and subject to a (tele-text) dialogue with one or more judges. Although still accepted as a reference to eventually check whether artificial intelligence will reach the intelligence of humans, it has generated debates all along. Of course, many variants and alternatives have been suggested. The Turing test and related ideas present several problems as a machine intelligence test: the Turing Test is anthropomorphic (it measures humanity, not intelligence), it is not gradual (it does not give a score), it is not practical (it is becoming easy to cheat and requires a long time to get reliable assessments) and it requires a human judge.

A recent and naïve approximation to a machine intelligence test is what is called CAPTCHAs (Completely Automated Public Turing test to tell Computers and Humans Apart) [von Ahn et al 2002] [von Ahn et al 2008]. CAPTCHAs are any kind of simple question that can be easily answered by a human but not by current artificial intelligence technology. Typical CAPTCHAs are character recognition problems where letters appear distorted. These distortions make it difficult for machines (bots) to recognize the letters. An example of CAPTCHA from Gmail can be seen below:

The immediate objective of a CAPTCHA is to tell humans and machines apart. The ultimate goal is to prevent bots and other kind of machine agents or programs from being able to create thousands of accounts (or other tasks that only humans should do). Note that bots could block or spoil many Internet services we use as a daily basis if these and other control techniques did not exist.


The problem of CAPTCHAs is that they are becoming more and more difficult for humans, since bots are being specialized and improved to read them. Whenever a new CAPTCHA technique is developed, new bots appear that have chances of getting through the test[1]. This forces CAPTCHA developers to change them again, and so on and so forth. The reason is that they are specific and they rely on some particular tasks. Although CAPTCHAs work reasonably well today, in about 10 or 20 years, they will need to make things so difficult and general, that humans will require more time and several attempts to make it through. In fact, this is already happening, and we lose more and more time in CAPTCHAs everyday.


Apart from tests, a much more theoretical and formal approach to machine intelligence has been undertaken by prominent scientists in the 20th century such as A.M. Turing, R.J. Solomonoff, E.M. Gold, C.S. Wallace, J.J. Rissanen, L. Blum and M. Blum, G.J. Chaitin and others. The landmark is the development of Algorithmic Information Theory (also known as Kolmogorov Complexity) (see [Li & Vitányi 2008] for the most complete reference on it), its relation to learning (inductive inference and prediction) [Solomonoff 1964] [Wallace and Boulton 1968] [Solomonoff 1986] [Wallace and Dowe 1999] [Wallace 2005] and ultimately its relation to intelligence.


These ideas led to some members of the team who are now presenting this project hereby to introduce some formal definitions of intelligence, namely the works [Dowe & Hajek 1997, 1998] [Hernandez-Orallo & Minaya-Collado 1998] [Hernandez-Orallo 2000a] [Hernandez-Orallo 2000b]. All of these tests and definitions are mathematical, non-anthropomorphic (i.e. universal), meaningful and intuitive. In this sense they do not take Homo Sapiens as a reference and judge (as in the Turing Test), they have not evolved by trial and error through experimentation on testing subjects during a century (such as generally done in psychometrics) but they are constructed over fundamental and mathematical notions in computation theory. And some of them succeeded to be practical (feasible as an intelligence test) at the cost of being partial (in the sense they measure necessary traits of intelligence but not all of them). Let us see these approaches in more detail below:


On the one hand, Dowe & Hajek [Dowe & Hajek 1997, 1998] suggested the introduction of induction inference problems in a somehow induction-enhanced or compression-enhanced Turing Test, in order to, among other things, deal with Searle’s "Chinese room" paradox, and also because an inductive inference ability is a necessary (though possibly "not sufficient") requirement for intelligence.


On the other hand, quite simultaneously and similarly, and also independently, in [Hernandez-Orallo & Minaya-Collado 1998] [Hernandez-Orallo 2000a], intelligence was defined as the ability to comprehend, giving a formal definition of the notion of comprehension as the identification of a ‘predominant’ pattern from a given evidence, derived from Solomonoff induction and prediction concepts, Kolmogorov complexity and Levin’s variants and optimal search. The definition was given as the result of a test, called C-test [Hernandez-Orallo & Minaya-Collado 1998], formed by computationally-obtained series of increasing complexity. The sequences were formatted and presented in a quite similar way to psychometric tests, and as a result, the test was administered to humans. Nonetheless, the main goal was that the test could eventually be administered to other kinds of intelligent systems as well.


A factorisation (and hence extension) of these inductive inference tests was sketched in order to explore which other abilities could conform a complete (and hence sufficient) test [Hernandez-Orallo 2000b]. In order to apply the test for low intelligent systems, (still) unable to understand natural language, the proposal for a dynamic extension of the C-test in [Hernandez-Orallo 2000a], was expressed like this: “the presentation of the test must change slightly. The exercises should be given one by one and, after each guess, the subject must be given the correct answer (rewards and penalties could be used instead)”.


Recent works by Legg and Hutter (e.g. [Legg and Hutter 2007]) have followed the previous steps and, strongly influenced by Hutter’s theory of AIXI optimal agents [Hutter 2005], have given another (and related) definition of machine intelligence, dubbed “Universal Intelligence”, also grounded in Kolmogorov complexity and Solomonoff's induction.


The basic idea is to evaluate the intelligence of an agent p on several environments m, chosen by using the universal distribution[2] (the one derived from Kolmogorov Complexity, i.e, p(m)= 2-K(m)), using rewards that are accumulated during the interaction with the environment. So intelligence is defined as the competence of an agent in different environments, where simple environments have more probability than complex environments.


The comparison with the works from Hernandez-Orallo (but also Dowe & Hajek's compression test) is summarised by [Legg and Hutter 2007] with the following table:



Universal agent

Universal test

Passive environment

Solomonoff induction[3]


Active environment


Universal intelligence


In fact, the definition based on the C-test can be considered an instance of Legg and Hutter’s work, where the environment outputs no rewards, the agent is not allowed to make an action until a number of observations is seen (the inductive inference sequence). In our opinion, one of the most relevant contributions in their work is that their definition of Universal Intelligence allows one to formally evaluate the theoretical performance of some agents: a random agent, a specialised agent, ..., or a super-intelligent agent, such as AIXI [Hutter 2005], which is claimed to be the agent that, if ever constructed, would score the best in the Universal Intelligent test. In a nutshell, Legg and Hutter's work is another step that helps to shape the things that should be addressed in the near future in order to eventually reach a theory of machine intelligence.


There are, however, five problems we have identified in their definition with regard to making it practical. First, we have an infinite sum over all the environments. Secondly, we also have an infinite sum over all the possible rewards (agent's life in each environment is infinite). Third, K() is not computable. Fourth, and more importantly, time is not taken into account. And, fifth, there is a conflation between induction and prediction.





In this work we propose a modification and extension of the previous definitions and tests in order to construct the first general, necessary, formal, feasible intelligence definition and test. The definition and test will be grounded on previous developments on tests based on Kolmogorov Complexity [Dowe & Hajek 1997, 1998] [Hernandez-Orallo & Minaya-Collado 1998] [Hernandez-Orallo 2000a, 2000b] [Legg and Hutter 2007]). The main idea of all of them is that the Universal Distribution is used for the generation of questions and environments, and hence any particular bias towards a specific individual, species or culture is avoided. This makes the test universal for any possible kind of subject. But apart from this “Universal” condition we will focus on some additional practical requirements:


-      It must allow one to measure any kind of intelligent system (biological or computational) which exists now or can be built in the future (anytime systems).

-      The test must quickly adapt to the level of intelligence and time scale of the system. It must be able to evaluate inept and brilliant systems (any intelligence level) as well as very slow to very fast systems (any time scale).

-      The quality of the assessment will depend on the time we leave for the test. That means that the test can be interrupted at any time, producing an approximation to the intelligent score. The more time we leave for the test, the better the assessment (anytime test).


Because of these requirements we will call the tests "anytime universal intelligence tests". If we succeed, science will be able to measure intelligence of higher animals (e.g. apes), humans, machines, hybrids or communities of humans and machines and, eventually, extraterrestrial beings in an absolute way.


The main difficulty to make it feasible is that the more general we intend the test to be, the fewer things about the agent should be assumed. That means that we may require more time to evaluate intelligence, because we can neither rely on a common knowledge nor language to explain the instructions or give things for granted. The problem is similar to evaluating children or animals, where everything must be quite symbolic and simple, and subjects must be led toward a goal through rewards and penalties. That is the reason why these tests are more interactive than traditional psychometric tests for adult Homo sapiens.


In fact, there is a common thesis in all intelligence tests (both from psychometrics, comparative cognition or artificial intelligence): the time which is required to evaluate the intelligence of a subject depends (1) on the knowledge or features which are expected and known about the subject (same species, same culture, same language, etc.) and (2) on the adaptability of the examiner. While (1) is taken to the extreme in human psychometrics, where tests are just a form with questions explained in natural language, (2) is taken to the extreme with the Turing Test or any kind of interview evaluation, where the examiners are humans who dynamically adapt their questions according to the subject’s answers.


Consequently, if we want to evaluate different kinds of subjects without relying on any assumption about their nature or knowledge, and if we want to get a reliable assessment in a reduced (practical) period of time, .we must necessarily devise interactive and adaptive tests,



Although the endeavour looks pretentious and bold, there are some facts and some reasonable hypotheses which give strong support, at least, in considering the effort of attempting a first prototype of this test and a preliminary evaluation in order to establish whether these tests are constructible.





§  We can only assume a few things about the subject to be examined. It will be an open active system that will have some sensors and some actuators, and, additionally, there will be a way to conduct its behaviour through rewards and penalties. All the subjects we contemplate (humans, animals, reactive machines with rewards) accomplish this[4], and we think there is no need to assume anything else about the physics, implementation or behaviour of the subject in order to evaluate it.

§  Interacting with a sample of environments under the universal distribution is sufficient to evaluate general intelligence. While it seems to only measure inductive abilities, many environments will also require memory and deductive abilities to succeed. This does not exclude the design of factorial tests, by posing specific constraints on the kind of environments. However, this factorisation goes out the scope of this project.

§  A relatively small sample of environments can be used instead of all of them, in order to make the test as shorter as possible. The complexity of the environments can also progressively be adjusted to match the intelligence of the agent, and their rewards weighted accordingly, in order to make the test more effective.

§  Time can be included by allowing the agent to play with a single environment for a fixed time. This time limit can progressively grow to make the test anytime. Rewards might be time-dependent so the agent can realise that time is relevant.


Finally, it may seem difficult to imagine what one of these "anytime universal intelligence tests" might look like, but we find similar things everywhere: games, child games, where children start with easy ones and soon move to more sophisticated ones. To find an even closer parallel, the anytime intelligence test may be quite similar to a random collection of videogames where we play for a little time with the easy ones first and then we are given more time to play with some other more difficult ones if we succeed on the easiest one. As usual, easy levels can be passed quickly and difficult levels require more time. The intelligence of an agent would be some kind of average of the scores in all the levels, possibly using some kind of log-loss measure on the complexity of each level. The key point here is that these games or environments will be generated according to a distribution which is independent (and hence neutral) over the different kinds of subjects (humans, machines, animals...) to be evaluated.





The project spans for one and a half years, starting in July 2009. The project is divided into three stages, about six months each.



In case the results of the project are successful, we will follow up with extensive publications of the results and also will start some other more traditional projects exploiting the results, in order to enhance the tests, factorise them and make them publicly available for several scientific disciplines: artificial intelligence, psychometrics, comparative cognition, etc.


Eventually, the acceptance and use of these tests would allow the following:


In other words, and getting back to the similarities between machine intelligence tests and psychometrics, we would expect at least the same applications that psychometrics have today in education, recruitment and therapy into the world of artificial intelligence, where the areas are known as knowledge acquisition, agent cognitive certification and agent redesign.



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[1] For instance, JAntiCaptcha is a tool developed by JDTeam that is included in JDownloader. It allows to crack CAPTCHAs from the most popular download sites (e.g. MegaUpload, RapidShare).

[2] Universal distribution is defined over a particular reference machine, but this choice only affects Kolmogorov complexity by a constant. As such, we take the liberty at saying "the" universal distribution.

[3] More precisely, it is really "Solomonoff prediction", but we show it as it is in the original table.

[4] Sensors may either be eyes and ears, or a camera and a microphone, or a communication-listening e-service, etc. Actuators may either be hands, muzzle, fins, a robotic pincer, or an e-service invocator software module, etc.