Update Tree-Search-and-Graph-Search.md

norvig · web-flow · commit 948e5d7f7f32 · 2019-02-06T20:22:39.000-08:00
diff --git a/md/Tree-Search-and-Graph-Search.md b/md/Tree-Search-and-Graph-Search.md
@@ -9,17 +9,27 @@ __function__ GRAPH-SEARCH(_problem_) __returns__ a solution, or failure
 &emsp;_solution_ &larr; failure  
 &emsp;__while__  _frontier_ is not empty __and__ _solution_ can possibly be improved __do__  
 &emsp;&emsp;&emsp;_parent_ &larr; some node that we choose to remove from _frontier_  
-&emsp;&emsp;&emsp;__for__ _child_ __in__ Expand(_parent_) __do__   
+&emsp;&emsp;&emsp;__for__ _child_ __in__ EXPAND(_parent_) __do__   
 &emsp;&emsp;&emsp;&emsp;&emsp;_s_ &larr; _child_.state  
 &emsp;&emsp;&emsp;&emsp;&emsp;__if__ _s_ is not in _reached_  __or__ _child_ is a cheaper path than _reached_[_s_] __then__  
 &emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;_reached_[_s_] &larr; _child_  
 &emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;add _child_ to _frontier_  
-&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;__if__ _child_ is a goal and is cheaper than _solution_ __then__  
+&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;__if__ _s_ is a goal and _child_ is cheaper than _solution_ __then__  
 &emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;_solution_  =  _child_  
 &emsp;__return__ _solution_
 
 ---
-__Figure__ ?? In the GENERIC-SEARCH algorithm, we keep track of the best _solution_ found so far, as well as a set of states that we have already _reached_, and a _frontier_ of paths from which we will choose 
+__function__ EXPAND(_problem, parent_) __returns__ a list of nodes  
+&emsp;_s_ &larr; _parent_.state  
+&emsp;_nodes_ &larr; an empty list  
+&emsp;__for__ _action_ __in__ _problem_.actions(_s_) __do__   
+&emsp;&emsp;&emsp;_s'_ &larr; _problem_.result(_s_, _action_)  
+&emsp;&emsp;&emsp;_cost_ &larr; _parent_.path-cost + _problem_.step-cost(_s, action, s')  
+&emsp;&emsp;&emsp;add _node_ to _nodes_  
+&emsp;__return__ _nodes_  
+
+---
+__Figure__ ?? In the GRAPH-SEARCH algorithm, we keep track of the best _solution_ found so far, as well as the states that we have already _reached_, and a _frontier_ of paths from which we will choose 
 the next path to expand.
 In any specific search algorithm, we specify (1) the criteria for ordering the paths in the frontier,
 and (2) the procedure for determining when it is no longer possible to improve on a solution.
