I am restarting from scratch and hence I wish to solicit comments on whether following the curriculum would be enough to give me a good foundation and prepare me well to work with Prolog to develop LbAI oriented systems.
Thanks.
mathematics [algebra, trigonometry, statistics]
logic [openlogicproject]
mathematics for computer science (lehman, leighton, meyer)
computer science logo style (harvey)
the algorithm design manual (skiena)
common sense, the turing test, and the quest for real a i (levesque)
machines like us: toward āaiā with common sense (brachman, levesque)
learn prolog now (blackburn, bos, striegnitz)
the power of prolog (triska)
programming in prolog with the iso standard (clocksin, mellish)
simply logical (flach)
the art of prolog (sterling, shapiro)
the practice of prolog (sterling)
the craft of prolog (oākeefe)
essentials of constraint programming (fruhwirth, abdennadher)
constraint handling rules (fruhwirth)
thinking as computation (levesque)
knowledge representation and reasoning (levesque, brachman)
the logic of knowledge bases (levesque)
prolog programming for artificial intelligence (bratko)
prolog programming in depth (covington, nute, vellino)
logic and the organization of information (fricke)
theory and practice of logic programming (truszczynski)
LbAI is āLogic-based Artificial Intelligenceā, also known as āGood Old-fashioned Artificial Intelligenceā, a way of approaching AI using symbolic inference instead of the currently-used Machine Learning (which should actually be termed āAdaptive Machine Learningā) approach.
Prolog is most suited for that approach, though, one can also employ other relational programming systems like a miniKanren, which turns out to be lighter because it can be implemented in almost any Turing-complete programming language.
If you plan on using each book and working through them you have way too many books.
For someone self learning I would recommend just two.
Prolog Programming for Artificial Intelligence (4th Edition) by Ivan Bratko (WorldCat )
The Craft of Prolog by Richard A O`Keefe (WorldCat )
The second is the one of value but it is not a beginner book and thus the need to work through the first one.
Once you have those two down then you can branch out to more specific areas or more in depth into other areas, E.g. constraints, Natural Language Processing, parsing, problem solving, searching, RDF, domain specific languages, Answer Set Programming, sCASP, etc.