diff --git a/OMSCS/Courses/AI/AIMA Textbook Notes/AIMA - Chapter 6 - Constraint Satisfaction Problems.md b/OMSCS/Courses/AI/AIMA Textbook Notes/AIMA - Chapter 6 - Constraint Satisfaction Problems.md
index 00bf4e5..72b2f99 100644
--- a/OMSCS/Courses/AI/AIMA Textbook Notes/AIMA - Chapter 6 - Constraint Satisfaction Problems.md	
+++ b/OMSCS/Courses/AI/AIMA Textbook Notes/AIMA - Chapter 6 - Constraint Satisfaction Problems.md	
@@ -4,7 +4,7 @@ tags:
   - AI
   - AIMA
 ---
-**# AIMA - Chapter 6 - Constraint Satisfaction Problems
+# AIMA - Chapter 6 - Constraint Satisfaction Problems
 - [[AIMA - Chapter 3 - Solving Problems by Searching]] and Chapter 4 (no chapter notes, module notes link: [[Module 02 - Simulated Annealing]])
 	- These prior chapters considered the "state" of a problem to be a black box, atomic, indivisible.
 	- Searching for solutions within the state space required domain-specific code to describe the transition between states.
@@ -253,4 +253,4 @@ Notes incomplete. Subsection headings and key terms:
 - **Conflict-directed backjumping** backtracks directly to the source of the problem.
 - **Constraint learning** records the conflicts as they are encountered during search in order to avoid the same conflict later in the search.
 - Local search using the **min-conflicts** heuristic has also been applied to constraint satisfaction problems with great success.
-- The complexity of solving a CSP is strongly related to the structure of its constraint graph. Tree-structured problems can be solved in linear time. **Cutset conditioning** can reduce a general CSP to a tree-structured one and is quite efficient (requiring only linear memory) if a small cutset can be found. **Tree decomposition** techniques transform the CSP into a tree of subproblems and are efficient if the **tree width** of the constraint graph is small; however they need memory exponential in the tree width of the constraint graph. Combining cutset conditioning with tree decomposition can allow a better tradeoff of memory versus time.
\ No newline at end of file
+- The complexity of solving a CSP is strongly related to the structure of its constraint graph. Tree-structured problems can be solved in linear time. **Cutset conditioning** can reduce a general CSP to a tree-structured one and is quite efficient (requiring only linear memory) if a small cutset can be found. **Tree decomposition** techniques transform the CSP into a tree of subproblems and are efficient if the **tree width** of the constraint graph is small; however they need memory exponential in the tree width of the constraint graph. Combining cutset conditioning with tree decomposition can allow a better tradeoff of memory versus time.