To describe a PAC learning algorithm for monotone conjunctions, we will use a simple and intuitive algorithm called the "Conjunction-Finder" algorithm. The idea is to start with the most general hypothesis (a conjunction containing all positive literals) and iteratively refine the hypothesis based on the training examples.

Algorithm: Conjunction-Finder

1. Input: A set of m labeled examples, D = {(x^(1), y^(1)), (x^(2), y^(2)), ..., (x^(m), y^(m))}, drawn i.i.d. from an unknown target concept c ∈ M_n.

2. Initialize the hypothesis h to the most general conjunction, containing all n positive literals:
   h(X) = x_1 AND x_2 AND ... AND x_n

3. For each example (x^(i), y^(i)) in D:
   a. If h(x^(i)) ≠ y^(i) (the example is misclassified) and y^(i) = 1 (the example is positive):
      i. For each variable x_j in h:
         1. If x_j is present in the conjunction and x^(i)_j = 0 (the corresponding input value is False):
            a. Remove x_j from the conjunction h.

4. Output: The refined hypothesis h.

The Conjunction-Finder algorithm starts with the most general hypothesis and refines it based on positive examples. When a misclassified positive example is encountered, the algorithm removes literals from the hypothesis that do not satisfy the example, making the hypothesis more specific.