Beam Search and Best First Greedy Search

Lecture 8 of CS607 Artificial Intelligence explores heuristic tree search algorithms that balance search guidance with memory constraints: Beam Search and Best First Search (Greedy Search). Beam Search modifies breadth-first search by evaluating all successors at each level using a heuristic function and retaining only the best beta nodes (where beta is the predetermined Beam Width), discarding all other candidates. Students analyze how Beam Search bounds memory consumption to O(beta * b), though discarding nodes sacrifices completeness. The lecture then investigates Greedy Best First Search, which expands the node that appears closest to the goal according to f(n) = h(n). Using a Priority Queue ordered by h(n), Greedy Best First Search cuts through large state spaces rapidly; however, like DFS, it can be led astray by misleading heuristic gradients, frequently producing highly sub-optimal solutions because it completely ignores accumulated path cost g(n). Students investigate how intelligent agents perceive environmental percepts through sensors and actuate rational decisions through operational effectors. The discussion highlights how heuristic evaluation functions guide search algorithms toward goal states while dramatically reducing computational overhead. Key concepts such as chromosome encoding, fitness proportional selection, and genetic diversity preservation are analyzed in evolutionary computation. The lecture details formal logic inference mechanisms, demonstrating how resolution refutation and unification derive valid theorems from clausal knowledge bases. Production rule architectures are analyzed to understand how forward chaining and backward chaining drive automated expert diagnostic systems. CLIPS rule-based programming constructs provide practical engineering frameworks for managing working memory facts and agenda activations. 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.