Unit 1: AI algorithm implementation and mini projects

pythonfrom collections import deque
def bfs_water_jug(a, b, target):
    seen, q = set(), deque([((0, 0), [])])
    while q:
        (x, y), path = q.popleft()
        if x == target or y == target: return path + [(x, y)]
        if (x, y) in seen: continue
        seen.add((x, y))
        moves = [(a, y), (x, b), (0, y), (x, 0),
                 (x - min(x, b - y), y + min(x, b - y)),
                 (x + min(y, a - x), y - min(y, a - x))]
        for m in moves: q.append((m, path + [(x, y)]))
bfs_water_jug(4, 3, 2)

• DFS: recursive with a visited set.
• Hill climbing: move to the best neighbour while it improves the score; it can get stuck at a local maximum.
• *A on a grid:** use a priority queue ordered by f = g + h with Manhattan distance as h.

pythonimport nltk
from nltk.corpus import stopwords
from nltk.stem import PorterStemmer
text = "Students at SBS are learning artificial intelligence"
tokens = nltk.word_tokenize(text)
filtered = [w for w in tokens if w.lower() not in stopwords.words("english")]
stems = [PorterStemmer().stem(w) for w in filtered]
tags = nltk.pos_tag(tokens)                 # part-of-speech tags
entities = nltk.ne_chunk(tags)              # named entity recognition

Propositional logic can be evaluated by generating all truth assignments with itertools.product([True, False], repeat=n).

pythondef minimax(board, is_max):
    winner = check_winner(board)
    if winner is not None: return winner          # +1, -1 or 0
    scores = []
    for move in empty_cells(board):
        board[move] = "X" if is_max else "O"
        scores.append(minimax(board, not is_max))
        board[move] = " "
    return max(scores) if is_max else min(scores)