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nowledge representation and learning. Among the many kinds of patterns in intelligent systems, semiotic patterns are particularly interesting ones. Peirce decomposed these into three categories: • iconic patterns, which are patterns of contextually important internal similarity between two entities (e
h to Superhuman AGI 317 18.1 Introduction 317 18.2 Cognitive Schema Learning 318 18.3 Self-Modification via Supercompilation 319 18.3.1 Three Aspects of Supercompilation 321 18.3.2 Supercompilation for Goal-Directed Program Modification 322 18.4 Self-Modification via Theorem-Proving 323 A Glossary 325 A.1
knowledge representation and learning. Among the many kinds of patterns in intelligent systems, semiotic patterns are particularly interesting ones. Peirce decomposed these into three categories: @ iconic patterns, which are patterns of contextually important internal similarity between two entities (e.
Cognitive Schema Learning .......... 0.000 e cee eee eens 318 18.3 Self-Modification via Supercompilation ..............0..00 000 cee eee 319 18.3.1 Three Aspects of Supercompilation .............0..00 000022 eee eee ee 321 18.3.2 Supercompilation for Goal-Directed Program Modification............. 322 18.4 Self-Modification via T
The Origin of Life. Dover, 1952. Pal82. Gunter Palm. Neural Assemblies. An Alternative Approach to Artificial Intelligence. Springer, 1982. Pei34. C, Peirce. Collected papers: Volume V. Pragmatism and pragmaticism. Harvard University Press. Cambridge MA., 1934. Pel05. Martin Pelikan. Hierarchical Bayesian
ummary. For an accessible overview of the supercompila- tion algorithm, the reader is referred to the article “What is Supercompilation?” [1] 18.3.1 Three Aspects of Supercompilation There are three separate levels to the supercompilation idea: first, a general philosophy; second a translation of this philosophy into a concrete a
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