"谓词演算及应用：归结原理与化子句集方法详解"。

The fifth chapter of the introduction to artificial intelligence discusses predicate calculus and its applications. Predicate calculus is a formal language with a strict theoretical system and is commonly used as a knowledge representation method. Examples of predicate calculus include statements like City (Beijing), City (Shanghai), Age (Zhang San, 23), and the general form (for all x) (for all y) (for all z)(F(x, y) and F(y, z) implies GF(x, z). The resolution principle is a theorem proving method introduced by Robinson in 1965, which theoretically solves the problem of proving theorems. The introduction of the resolution principle has played a significant role in advancing the field of automated theorem proving using machines. A clause set is a collection of clauses without explicit quantifier constraints, where variables are assumed to be universally quantified and elements are only disjunctions of literals. Negation symbols only apply to individual literals, and elements are assumed to be conjunctions by default. An example of a clause set is {~I(z) or R(z), I(A), ~R(x) or L(x), ~D(y)}. The method of deriving clause sets involves techniques like removing implication symbols by converting a → b to ~a or b, moving negation symbols by converting ~(a or b) to ~a and ~b and ~(a and b) to ~(a or ~b). An example of deriving a clause set involves converting (∃z) (∀x)(∃y){[(P(x) or Q(x)) implies R(y)] or U(z)} to (∃z) (∀x)(∃y){[~(P(x) or Q(x)) or R(y)] or U(z)}. In conclusion, predicate calculus and its applications offer a rigorous and systematic approach to knowledge representation in artificial intelligence. The resolution principle and clause set derivation methods play a crucial role in automating theorem proving and reasoning processes. The understanding of these concepts is essential for developing efficient and effective AI systems.