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Title: ** Protosynthex III, Example 3 in ‘A Deductive Question-Answerer for Natural Language Inference’, https://dl.acm.org/doi/10.1145/362052.362058
**Authors: Robert M. Schwarcz, John F. Burger, Robert F. Simmons
Languages: Protosynthex III, perhaps LISP?
I’ve been looking through a copy of Jean Sammet’s Programming Languages: History and Fundamentals (1969), particularly the sections on string processing languages and specialty languages. The latter contains a description of Protosynthex, which is a language designed to be used for question answering tasks. I was curious about this language because I’m interested in the history of AI, and 1960s AI research seems to have been tied very closely to linguistics and logical reasoning - arguably much closer than it is today. The Protosynthex code example in Sammet’s book is quite short, and it’s been difficult to find more examples. I did manage to find some example uses of Protosynthex III in a research paper from 1970 titled ‘A Deductive Question-Answerer for Natural Language Inference’, by Schwarcz, Burger, and Simmons. I’ve taken screenshots of the example code in Example 3 and included it in this thread as a PDF. I've also included the paper, where the code can also be read from pages 181-182. It’s not clear to me whether Protosynthex III is a distinct language like Protosynthex is, or if it is actually a form of the LISP language.
What I find interesting about the example code in _Example 3 _ (pp. 181-182), which I’ll call ‘There is a Monkey’, is that it appears to play out a scenario which could be seen in an animal experiment. ‘There is a Monkey’ declares a monkey, a box, and bananas. The bananas are positioned in such a way that they are only to be reached if the box is moved
TO THE BANANAS. Towards the end of the example code a query reads
DOES THE MONKEY GET THE BANANAS?, with the final line of code stating
MONKEY REACH BANANAS. . The paper states that this example is “...adapted from an Advice Taker problem proposed by [John] McCarthy” (p. 174) in his ‘Situations, actions, and causal laws’, found in Stanford Artificial Intelligence Project Memo No. 2, July 1963. McCarthy is the inventor of the LISP language, which has been historically very popular in AI.
There’s no background given about why or how the monkey, box, and bananas have been brought together, what their size and distance from each other is, why or how the bananas are suspended, nor any information about where this event or simulation is taking place. Indeed, we do not really know if
(THERE . PLACE) on line 7 even has a floor or walls, but I suspect that most people who read this code will assume that the event or scenario is not taking place in a monkey’s natural environment (do you agree or disagree?). This event or simulation could have instead easily been written by declaring a climbing tree instead of a box, but for whatever reason a human made object is declared as the means or tool for reaching the bananas. This seems very unnatural, as if an animal is being observed and tested in an experiment environment in order to collect evidence or to prove something. Of course, the actual purpose of this code is to prove or demonstrate what types of question answering experiments can be conducted using Protosynthex III. However, this in itself does not explain why a linguistic and AI experiment is performed as an event or simulation set up in a minimal, controlled environment designed to test if a monkey will reach its food. I therefore find it curious that the emerging field of AI in the 1960s would reproduce controversial forms of experimentation as seen in biology and psychology in its models. Perhaps there is a prestigious ‘scientific aesthetic’ that is trying to be emulated. Perhaps it’s just following what is perceived to be a convention. Perhaps you can see other readings?
The paper offers its own explanation of ‘There is a Monkey’, which I’ve copied here:
“Explanation. The monkey gets the bananas if he reaches them, and he can reach them if he stands on an elevated object under them and reaches for them; the monkey stands on the box, which is an elevated-object, and reaches for the bananas, and so if the box is under the bananas the monkey has succeeded in reaching them; the box is under the bananas if it is lower than they, which it is, and also at the bananas; the box is at the bananas if some animal has moved it to the bananas--which of course the monkey has done; therefore, the monkey reaches the bananas. The question-answerer follows just this reasoning path in answering the question (though in terms, of course, of the formal concept structure) ; the recombination of subgoal answers into an answer to the question is shown by the trace of STOGOAL in the Appendix. It is interesting to note here, of course, the input of inverses (converses) and inference rules by means of English sentences; with a few additional grammar rules and pieces of lexical information, properties could be input in this manner also”(p. 176).