Fix typos <noupdate>

src/fundamentals-of-ai-and-kr/module1/sections/_local_search.tex

@@ -91,7 +91,7 @@ Can be seen as a search process over graphs:
     \end{subfigure}
 \end{figure}
 
-Meta heuristics finds a balance between:
+Meta heuristics aim to find a balance between:
 \begin{descriptionlist}
     \item[Intensification] \marginnote{Intensification}
         Look for moves near the neighborhood.
@@ -292,4 +292,4 @@ Genetic operators are:
             population = offspring
             evaluate(population)
 \end{lstlisting}
-\end{algorithm}
+\end{algorithm}

src/fundamentals-of-ai-and-kr/module1/sections/_search.tex

@@ -335,7 +335,7 @@ The fringe is ordered according the estimated scores.
             \end{tabular}
         \end{center}
 
-        In generally, it is better to use heuristics with large values (i.e. heuristics that don't underestimate too much).
+        In general, it is better to use heuristics with large values (i.e. heuristics that don't underestimate too much).
 
         \begin{figure}[ht]
             \centering

src/fundamentals-of-ai-and-kr/module1/sections/_swarm_intelligence.tex

@@ -74,8 +74,8 @@ They also tend to prefer paths marked with the highest pheromone concentration.
     \item[Pheromone update]
         After each step, the pheromone trail is updated depending on an evaporation factor $\rho \in [0, 1]$:
         \[ \tau_{ij} = (1 - \rho) \tau_{ij} + \sum_{k=1}^{n_\text{ants}} \Delta \tau_{ij}^{(k)} \]
-        $\tau_{ij}^{(k)}$ of the $k$-th ant is defined as:
-        \[ \tau_{ij}^{(k)} = \begin{cases}
+        $\Delta \tau_{ij}^{(k)}$ of the $k$-th ant is defined as:
+        \[ \Delta \tau_{ij}^{(k)} = \begin{cases}
             \frac{1}{L_k} & \text{if ant } k \text{ used the arch } (i, j) \\
             0 & \text{otherwise}
         \end{cases} \]
@@ -172,7 +172,7 @@ In a bird flock, the movement of the individuals tend to:
     \item Stay in the flock.
     \item Avoid collisions.
 \end{itemize}
-However, a model based on the these rules does not have a common objective.
+However, a model based on these rules does not have a common objective.
 
 PSO introduces as common objective the search of food.
 Each individual that finds food can: