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Microblog |
Notes and reviews on papers, books and learning materials.
"Paper reading Microblog", I learned this term from Mark J. Nelson and his http://www.kmjn.org/paperlog
From John McCarthy's works, my enlightenment begun. After I read his "Recursive Functions of Symbolic Expressions and Their Computation by Machine, Part I", My programming story begun.
His home page is still hosted by Stanford, with great honor, at http://www-formal.stanford.edu/jmc
http://www-formal.stanford.edu/jmc/circumscription/circumscription.html
Humans and intelligent computer programs must often jump to the conclusion that the objects they can determine to have certain properties or relations are the only objects that do. Circumscription formalizes such conjectural reasoning.
http://www-formal.stanford.edu/jmc/applications/applications.html
We present a new and more symmetric version of the circumscription method of nonmonotonic reasoning first described in (McCarthy 1980) and some applications to formalizing common sense knowledge. The applications in this paper are mostly based on minimizing the abnormality of different aspects of various entities. Included are nonmonotonic treatments of is-a hierarchies, the unique names hypothesis, and the frame problem. The new circumscription may be called formula circumscription to distinguish it from the previously defined domain circumscription and predicate circumscription. A still more general formalism called prioritized circumscription is briefly explored.
http://www-formal.stanford.edu/jmc/ailogic/ailogic.html
This is a position paper about the relations among artificial intelligence (AI), mathematical logic and the formalization of common-sense knowledge and reasoning. It also treats other problems of concern to both AI and philosophy. I thank the editor for inviting it. The position advocated is that philosophy can contribute to AI if it treats some of its traditional subject matter in more detail and that this will advance the philosophical goals also. Actual formalisms (mostly first order languages) for expressing common-sense facts are described in the references.
http://www-formal.stanford.edu/jmc/elaboration
A formalism is elaboration tolerant to the extent that it is convenient to modify a set of facts expressed in the formalism to take into account new phenomena or changed circumstances. Representations of information in natural language have good elaboration tolerance when used with human background knowledge. Human-level AI will require representations with much more elaboration tolerance than those used by present AI programs, because human-level AI needs to be able to take new phenomena into account.
The simplest kind of elaboration is the addition of new formulas. We'll call these additive elaborations. Next comes changing the values of parameters. Adding new arguments to functions and predicates represents more of a change. However, elaborations not expressible as additions to the object language representation may be treatable as additions at a meta-level expression of the facts.
Elaboration tolerance requires nonmonotonic reasoning. The elaborations that are tolerated depend on what aspects of the phenomenon are treated nonmonotonically. Representing contexts as objects in a logical formalism that can express relations among contexts should also help.
We use the missionaries and cannibals problem and about 20 variants as our Drosophila in studying elaboration tolerance in logical AI.
The present version has only some parts of a situation calculus formalization. However, the English language elaborations listed are enough to serve as a challenge to logical AI formalisms claiming elaboration tolerance.
http://www-formal.stanford.edu/jmc/robotandbaby/robotandbaby.html
https://en.wikipedia.org/wiki/Joseph_Goguen
A Categorical View of Substitution, Equation and Solution
By a whole bunch of current, former, and honorary MIT AI Lab graduate students.
This document presumptuously purports to explain how to do research. We give heuristics that may be useful in picking up the specific skills needed for research (reading, writing, programming) and for understanding and enjoying the process itself (methodology, topic and advisor selection, and emotional factors).