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Lecture Notes in Computer Science 3472

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36 Moez Krichen<br />

b/1<br />

a/0<br />

s1<br />

b/1<br />

a/1<br />

s2<br />

Fig. 2.1. Mach<strong>in</strong>e M1.<br />

many versions, depend<strong>in</strong>g on which types of experiments are allowed. A first<br />

classification can be made between simple experiments and multiple experiments<br />

[Moo56, Gil61]. In a simple experiment we dispose of a s<strong>in</strong>gle copy of<br />

the mach<strong>in</strong>e whereas <strong>in</strong> a multiple experiment we dispose of multiple identical<br />

copies of the mach<strong>in</strong>e which occupy the same <strong>in</strong>itial (unknown) state. We will<br />

only consider simple experiments <strong>in</strong> this chapter called dist<strong>in</strong>guish<strong>in</strong>g sequences.<br />

Another classification can be made between preset and adaptive dist<strong>in</strong>guish<strong>in</strong>g<br />

sequences. A preset dist<strong>in</strong>guish<strong>in</strong>g sequence (PDS) is a sequence of <strong>in</strong>puts<br />

whereas an adaptive dist<strong>in</strong>guish<strong>in</strong>g sequence (ADS) is really a tree where <strong>in</strong>puts<br />

may be different depend<strong>in</strong>g on the outputs (thus, the term “sequence” can be<br />

considered a misnomer, but it is kept for reasons of tradition). Thus, a PDS<br />

models an experiment where the sequence of <strong>in</strong>puts is determ<strong>in</strong>ed <strong>in</strong> advance,<br />

<strong>in</strong>dependently of the responses of the mach<strong>in</strong>e. On the other hand, an ADS models<br />

an experiment where at each step the experimenter decides which <strong>in</strong>put to<br />

apply next based on the observed output. Clearly, an ADS is a more powerful<br />

experiment than a PDS. Indeed, as we will see, there are cases where a mach<strong>in</strong>e<br />

has an ADS but has no PDS. Thus, it makes sense to make a dist<strong>in</strong>ction between<br />

the two.<br />

This br<strong>in</strong>gs us to another difference between the problem of f<strong>in</strong>d<strong>in</strong>g a hom<strong>in</strong>g<br />

sequence and the state identification problem. We know that for m<strong>in</strong>imal<br />

mach<strong>in</strong>es a hom<strong>in</strong>g sequence always exists. Such a sequence is, by def<strong>in</strong>ition,<br />

a “preset hom<strong>in</strong>g sequence”. Indeed, there is no need to consider adaptive sequences<br />

s<strong>in</strong>ce they do not add to the power of the experimenter. On the other<br />

hand, a m<strong>in</strong>imal mach<strong>in</strong>e may not have a PDS; <strong>in</strong> fact, it may not have an ADS<br />

either. Thus, contrary to the hom<strong>in</strong>g problem, there are mach<strong>in</strong>es for which the<br />

state identification problem cannot be solved, even though these mach<strong>in</strong>es are<br />

m<strong>in</strong>imal.<br />

As a last difference between the two problems, notice the follow<strong>in</strong>g. When<br />

a mach<strong>in</strong>e is not m<strong>in</strong>imal, we are not sure whether it has a hom<strong>in</strong>g sequence

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