The Evaluation of AGI Systems

The paper surveys the evaluation approaches used in AGI research, and argues that the proper way of evaluation is to combine empirical comparison with human intelligence and theoretical analysis of the assumptions and implications of the AGI models. Approaches of Evaluation In recent years, the problem of evaluation is getting more and more attention in the field of Artificial General Intelligence, or AGI (GB09; LIIL09; LGW09; MAP09; Was09). Though the evaluation of research results is important in any field of scientific research, the problem has special difficulty in the current context of AGI, since the research activities belong to many different paradigms, and there seems to be no “neutral” way to compare them (Wan08). In traditional AI research, since the projects are typically problem-oriented, it is natural to compare competing theories and techniques by the scope, correctness, and complexity of the algorithms involved, or by their performance on a set of benchmark instances of the problem. Obviously, this methodology is no longer applicable to AGI, given its stress of generality. Very commonly, one technique performs better on one problem than another technique, but not as well on a second problem. In this case, how can we decide which technique is “generally better”? One solution proposed in mainstream AI is to select typical intellectual problems as “Grand Challenges” (Coh05; Bra06). Though such activities do stimulate interesting research, it still has the danger of leading the research to problem-specific solutions, no matter how carefully the problems are selected — after all, this was why problems like theorem proving and game playing were selected in the early days of AI, and the resulting techniques have not been generalized to other fields very well. An alternative is to use multiple tasks (GB09) as a kind of “Cognitive Decathlon” (MJMH07). This approach clearly covers a larger field than a single challenge problem, but the selection of the components for the compound problem, as well as the way to calculate the “total score”, may still look more or less arbitrary. One way to be less problem-specific is to move away from testing problems, and to evaluate the systems according to certain properties (Min61; BBI98; LIIL09). While providing important insights, this approach also needs to justify its selection of desiderata and its method of overall evaluation, especially since the proposed property lists are all different from each other, and few technique has all the properties. Due to the lack of a common evaluation methodology, in papers surveying AGI what we usually find are descriptions of the special properties (both desired and undesired) of each system, without an overall grading or ranking (PG07; DOP08). Though this kind of description is often fair and informative, it does not resolve the evaluation problem, but avoids it to a large extent. For the field of AGI as a whole, there is a need for clear, justified, and widely applicable ways to evaluate research results, rather than treating them as equally good (though different). Obviously, an evaluation should start from the research goal — without a clearly specified destination, it is simply impossible to compare who is closer to it. Unfortunately, here the lacking of a common goal is exactly the reason for the lacking of a common evaluation methodology. By comparing two AAAI Presidential Addresses a decade apart (Dav98; Bra06), it seems clear that mainstream AI as a whole is not moving closer to a consensus on what its research goal really is. This diversity in objective inevitably causes the research efforts to move in different directions (Wan08), and to apply different evaluation criteria. At the most general level, AI has been driven by the motivations of understanding human intelligence, and reproducing it in computers to solve practical problems. Therefore, there are two fundamental approaches for