How To Design Eurisko

Th开发者_StackOverflowe program Eurisko was developed by Douglas Lenat in the late 70s and 80s. It's allegedly adept at learning general patterns and heuristics, and at improving it's own performance. Naturally, Lenat has never released the source code, and has published very little information about the exact inner workings of the program. So, in lieu of an official explanation, how might a program like Eurisko be designed? What open source technologies available today might make an implementation more practical?

Actually, Lenat published a fair amount on Eurisko (I was pretty interested in this 20 years ago). IIRC correctly, he published a number of papers in the AI literature, (here's a key one: "Why Eurisko appears to work" http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.89.1269&rep=rep1&type=pdf

Eurisko is based on his PhD thesis on AM ("A Mathematician") and that you can get through Stanford.

I'd go look at those first :-}

I don't know about "open source", but I'd certainly consider using LISP (Lenat did), or Prolog (because it has good symbolic manipulation support), and Eurisko is about symbolic computation.

All of this from past reading, with possible inaccuracies due to Somezheimer's. As I recall, Eurisko grew out of a postdoc with Herbert Simon, in which the decision was made to isolate AM's inference capabilities from those of the underlying Lisp.

Thus, a paper https://pdfs.semanticscholar.org/4cc4/a5e1591a5a4e81f6ad52e05833b3e750f56e.pdf described RLL, arguably an early DSL for writing discovery programs served as the platform on which Eurisko was written. I think I recall reading that Lenat made portions of the Eurisko code visible to Ken Haase

http://www.kenhaase.com/aboutkh.html

who was working on a rational reconstruction of Eurisko. My view is that it is possible to reconstruct Eurisko if one follows carefully all of the documents about it, though there are probably newer insights which can lead to improvements.

Overall, I believe the key insights are those which relate to complex-adaptive systems: feedback, decay, and process. Eurisko, like AM, used an agenda mechanism to organize agent behaviors and used feedback to adjust priorities of agenda items, and decay(Eurisko).

Eurisko had 4 agendas, each playing in different spaces, but, crucially, sharing the same knowledge base. Thus, the feedback given from one space might bump an agenda item in another space above the "boredom" threshold, returning that agenda to life.

Under that was a loop first seen in AM: Find something to do Do it Study what you just did Loop

To me, those key points seem profound and offer a glimpse into going beyond Eurisko.