Predicate Logic Resolution and Midterm Review

Lecture 22 of CS607 Artificial Intelligence synthesizes formal logic reasoning by executing complete First-Order Resolution Refutation proofs with unification and answer extraction. When answering non-ground queries such as 'Who is a student?', the refutation engine tracks the variable substitutions applied during each resolution step to return the concrete binding that satisfies the query. The lecture then conducts a comprehensive midterm syllabus review, integrating the major themes mastered across Lectures 1 through 21: the philosophical nature of intelligence and rational agents; blind versus informed state-space search algorithms (BFS, DFS, IDDFS, Hill Climbing, Beam Search, and A*); adversarial Minimax game trees with Alpha-Beta pruning; biological Genetic Algorithms; and classical symbolic knowledge representation using propositional logic and first-order predicate calculus. Exam-focused preparation strategies for university midterm assessments are highlighted. Fuzzy membership functions and linguistic variables are formulated to model real-world vagueness and human approximate reasoning mathematically. Defuzzification techniques such as center of gravity and mean of maxima are applied to convert fuzzy inference outputs into crisp operational control signals. Supervised learning algorithms are contrasted regarding inductive bias, hypothesis space representations, and generalization capabilities on unseen data. Information theoretic metrics including entropy and information gain are computed to construct optimal decision trees using the ID3 algorithm. The curriculum traces the evolutionary transition from single-layer linear perceptrons to multi-layer feedforward networks trained via backpropagation. Practical considerations for activation function selection, learning rates, and gradient descent convergence provide rigorous quantitative decision support. Scholars examine how automated planning algorithms decompose high-level goals into partially ordered action sequences with verified causal links. Computer vision methodologies spanning edge detection, feature extraction, and image segmentation are evaluated alongside robotic kinematic architectures. Unsupervised clustering algorithms such as k-means are examined to discover latent taxonomic groupings within unlabelled multidimensional datasets. Minimax game tree evaluations demonstrate how optimal zero-sum strategies are determined in competitive two-player adversarial environments. Alpha-beta pruning mathematical cutoffs are analyzed, proving that identical game decisions are reached with significantly fewer evaluated board states.