β Scribed by Paolo Garbolino
No coin nor oath required. For personal study only.
The problem of knowledge-base updating is addressed from an abstract point of view in the attempt to identify some general desiderata the updating mechanism should satisfy. They are recognized to be basically two: evaluating the local impact of new data on the single items of knowledge already stored, and propagating this effect through the knowledge-base maintaining at the same time its global coherence. It will be shown that Bayesian updating, difficult to implement, satisfies simultaneously these two requirements, and that, on the other hand, Dempster-Sharer updating, easy to implement, does not satisfy the requirement of global coherent propagation. I will point out the existence of a trade-off between coherence and effectiveness in the methods for representing uncertainty currently proposed in AI. Two kinds of learning machines, Boltzmann machines and Harmonium, will be discussed and considered as first attempts to give a non-behavioral characterization of coherence in a cognitive agent, a characterization still consistent with the behavioral (probabilistic) definition.
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