depth first search

In a recent conversation the idea of performance criteria as a measure of success in artificial intelligence came up. The context, if I recall, was whether intelligence is best understood as the end result of optimization, where the intermediate and final solutions may resist direct scrutiny, or as a sequence of representations (designed, learned, or both) with which an agent can act intelligently and robustly.

In some sense, these two approaches are both overly reductive, and not entirely exclusionary. I would not say they are orthogonal, but I do suspect that non-trivial intelligent systems of any kind require a fairly heterogeneous set of approaches. I’m immediately suspicious of any kind of universal framework for understanding intelligence. A lot of research focuses on parts of the intelligence problem, suitably constrained so as to admit the easy specification of performance criteria. Less research seems to focus on the ways in which these parts are intended to interact as complete agents that we hope would exhibit a kind of general intelligence.

As an example, the thermostat in this room meets a performance criteria. I can measure temperature deviations over time and develop a fairly accurate measure of how the thermostat is doing. The problem with performance measures, is that as a criteria for intelligence, these measures alone are incomplete. If you buy into the optimization view, then your work really only begins after the tricky problem of establishing a problem and a metric for performance is already complete. You are just evaluating thermostats, no matter how internally complex they might end up being.

If, on the other hand, you take a more representational view, then in designing the representational target, you are essentially exploring the problem and performance space (deciding for instance, that temparature control is sort of trivial, and can be left out), leaving aside till later the difficult learning problems that result only after the proper targets have been identified. This approach is not quite as clean cut, but it has the benefit of exposing the subjective, political nature of the discipline right up front, instead of burying that aspect of AI research in the group think of acceptable problems.

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