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AGI
2015

Toward Tractable Universal Induction Through Recursive Program Learning

3 years 11 days ago
Toward Tractable Universal Induction Through Recursive Program Learning
Abstract. Since universal induction is a central topic in artificial general intelligence (AGI), it is argued that compressing all sequences up to a complexity threshold should be the main thrust of AGI research. A measure for partial progress in AGI is suggested along these lines. By exhaustively executing all two and three state Turing machines a benchmark for low-complexity universal induction is constructed. Given the resulting binary sequences, programs are induced by recursively constructing a network of functions. The construction is guided by a breadthfirst search departing only from leaves of the lowest entropy programs, making the detection of low entropy (“short”) programs efficient. This way, all sequences (80% of the sequences) generated by two (three) state machines could be compressed back roughly to the size defined by their Kolmogorov complexity.
Arthur Franz
Added 13 Apr 2016
Updated 13 Apr 2016
Type Journal
Year 2015
Where AGI
Authors Arthur Franz
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