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An emprical foundation for a philosophy of Science

One advantage of studying a focused corpus like the A.I.T. is that we have an especially good chance of understanding some of these social relations. A history of A.I. often begins with a seminal conference that took place at Dartmouth in 1956 [McCorduck85] [Russell95] . If we treat the attendees at that meeting as ``founding fathers'' (in a population genetic sense!), we can attempt to track their ``genetic'' impact on the current field of A.I.

In an attempt to capture other significant intellectual influences beyond the advisor, the AIG questionnaire asks for committee members other than the chairman. The role of committee members varies a great deal from department to department, and campus to campus. But all of these numbers can be expected to exert some intellectual influence as well. Asking for committee members is a step towards other, non-advisor influencors, and it is also a matter of record. Research institutions are another way to capture intellectual interactions among collaborators.

Even if/when the AI-PhD family tree is completed, it will certainly not have captured all of what we mean by ``artificial intelligence research.'' For example: \item The Dartmouth ``founding fathers'' probably provide (direct) lineage for a {\em minority} of AI PhD's currently working in the field.

PhD theses, themselves, are probably some of the {\em worst} examples of research, in AI or elsewhere. By definition, we are talking about students who are doing some of their first work. They had {\em better} improve with time! \item PhD's account for only a fraction of AI research.

Nevertheless, Science is primarily concerned with {accumulating} knowledge, at least as much as it is about its initial discovery. A primary argument for interest in the AIG is that traditional academic relationships, as embodied by PhD genealogies, form a sort of ``skeleton'' around which the rest of scientific knowledge coalesces. Certainly individuals can pursue their own research program, and corporations can fund extended investigations into areas that are not represented in academia whatsoever. But it is hard to imagine extended research programs (like that ``fathered'' by Roger Schank, for example) that do not involve multiple ``generations'' of investigators; academia is almost certainly the most successful system for such propagation. Kuhnian and post-Kuhnian [REF623] [REF588] analyses highlight the importance of {\em social} aspects of Science. ``Paradigms'' are extremely appealing constructs, but they're also amorphous. For all of its faults, the AI-PhD tree represents incontrovertible {\em facts}, just as word frequencies do.

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