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Τι (ποιος) είναι Distributed Logic Programming - ορισμός

LEARNING LOGIC PROGRAMS FROM DATA

Inductive Logic Programming; Probabilistic inductive logic programming

Distributed Logic Programming

<language> (DLP) A logic programming language similar to Prolog, combined with parallel object orientation similar to POOL. DLP supports distributed backtracking over the results of a rendezvous between objects. Multi-threaded objects have autonomous activity and may simultaneously evaluate method calls. ["DLP: A Language for Distributed Logic Programming", A. Eliens, Wiley 1992]. (1996-01-07)

Journal of Logical and Algebraic Methods in Programming

JOURNAL

Journal on Logic Programming; The Journal of Logic Programming; The Journal on Logic Programming; J. Log. Prog.; J. Log. Program.; Journal of Logic Programming; Journal of Logic and Algebraic Programming; The Journal of Logic and Algebraic Programming; J. Log. Algebr. Methods Program.; J Log Algebr Methods Program; J. Log. Algebr. Program.; J Log Algebr Program; The Journal of Logical and Algebraic Methods in Programming; Journal of Logical & Algebraic Methods in Programming; Journal of Logic & Algebraic Programming; The Journal of Logic & Algebraic Programming; The Journal of Logical & Algebraic Methods in Programming; J Log Program

The Journal of Logical and Algebraic Methods in Programming is a peer-reviewed scientific journal established in 1984. It was originally titled The Journal of Logic Programming; in 2001 it was renamed The Journal of Logic and Algebraic Programming, and in 2014 it obtained its current title.

https://en.wikipedia.org/wiki/Journal_of_Logical_and_Algebraic_Methods_in_Programming

logic programming

PROGRAMMING PARADIGM BASED ON FORMAL LOGIC

Logical programming; Rule-based programming; Logical (programming); Rule-based (programming); Logic programming language; Logic program; Logic Programming; Rule-Based; Relational programming; Higher order logic programming; Higher-order logic programming; Object-oriented logic programming; Metalogic programming; Linear logic programming; Transaction logic programming; Logic language; History of logic programming

<artificial intelligence, programming, language> A declarative, relational style of programming based on first-order logic. The original logic programming language was Prolog. The concept is based on Horn clauses. The programmer writes a "database" of "facts", e.g. wet(water). ("water is wet") and "rules", e.g. mortal(X) :- human(X). ("X is mortal is implied by X is human"). Facts and rules are collectively known as "clauses". The user supplies a "goal" which the system attempts to prove using "resolution" or "backward chaining". This involves matching the current goal against each fact or the left hand side of each rule using "unification". If the goal matches a fact, the goal succeeds; if it matches a rule then the process recurses, taking each sub-goal on the right hand side of the rule as the current goal. If all sub-goals succeed then the rule succeeds. Each time a possible clause is chosen, a "choice point" is created on a stack. If subsequent resolution fails then control eventually returns to the choice point and subsequent clauses are tried. This is known as "backtracking". Clauses may contain logic variables which take on any value necessary to make the fact or the left hand side of the rule match a goal. Unification binds these variables to the corresponding subterms of the goal. Such bindings are associated with the choice point at which the clause was chosen and are undone when backtracking reaches that choice point. The user is informed of the success or failure of his first goal and if it succeeds and contains variables he is told what values of those variables caused it to succeed. He can then ask for alternative solutions. (1997-07-14)

Βικιπαίδεια

Inductive logic programming

Inductive logic programming (ILP) is a subfield of symbolic artificial intelligence which uses logic programming as a uniform representation for examples, background knowledge and hypotheses. Given an encoding of the known background knowledge and a set of examples represented as a logical database of facts, an ILP system will derive a hypothesised logic program which entails all the positive and none of the negative examples.

Schema: positive examples + negative examples + background knowledge ⇒ hypothesis.

Inductive logic programming is particularly useful in bioinformatics and natural language processing. Gordon Plotkin and Ehud Shapiro laid the initial theoretical foundation for inductive machine learning in a logical setting. Shapiro built their first implementation (Model Inference System) in 1981: a Prolog program that inductively inferred logic programs from positive and negative examples. The first full first-order implementation of inductive logic programming was Theorist in 1986. The term Inductive Logic Programming was first introduced in a paper by Stephen Muggleton in 1991. Muggleton also founded the annual international conference on Inductive Logic Programming, introduced the theoretical ideas of Predicate Invention, Inverse resolution, and Inverse entailment. Muggleton implemented Inverse entailment first in the PROGOL system. The term "inductive" here refers to philosophical (i.e. suggesting a theory to explain observed facts) rather than mathematical (i.e. proving a property for all members of a well-ordered set) induction.

https://en.wikipedia.org/wiki/Inductive logic programming