Fix mathematical equations for chapter 1-19

Author: Nalinc

.jekyll-metadata

@@ -1,8 +1,8 @@
 GEM
   remote: https://rubygems.org/
   specs:
-    addressable (2.6.0)
-      public_suffix (>= 2.0.2, < 4.0)
+    addressable (2.7.0)
+      public_suffix (>= 2.0.2, < 5.0)
     colorator (1.1.0)
     concurrent-ruby (1.1.5)
     em-websocket (0.5.1)
@@ -16,7 +16,7 @@ GEM
     http_parser.rb (0.6.0)
     i18n (0.9.5)
       concurrent-ruby (~> 1.0)
-    jekyll (3.8.5)
+    jekyll (3.8.6)
       addressable (~> 2.4)
       colorator (~> 1.0)
       em-websocket (~> 0.5)
@@ -36,19 +36,17 @@ GEM
       listen (~> 3.0)
     kramdown (1.17.0)
     liquid (4.0.3)
-    listen (3.1.5)
-      rb-fsevent (~> 0.9, >= 0.9.4)
-      rb-inotify (~> 0.9, >= 0.9.7)
-      ruby_dep (~> 1.2)
+    listen (3.2.0)
+      rb-fsevent (~> 0.10, >= 0.10.3)
+      rb-inotify (~> 0.9, >= 0.9.10)
     mercenary (0.3.6)
     pathutil (0.16.2)
       forwardable-extended (~> 2.6)
-    public_suffix (3.1.0)
+    public_suffix (4.0.1)
     rb-fsevent (0.10.3)
     rb-inotify (0.10.0)
       ffi (~> 1.0)
-    rouge (3.3.0)
-    ruby_dep (1.5.0)
+    rouge (3.11.1)
     safe_yaml (1.0.5)
     sass (3.7.4)
       sass-listen (~> 4.0.0)

Gemfile.lock

@@ -170,9 +170,9 @@ <h3 class="masthead-title">
 Exercise <a class="exerciseRef" href="/aima-exercises/search-exercises/ex_9/">path-planning-exercise</a> into an environment as
 follows:<br />
 
--   The percept will be a list of the positions, *relative to the
-    agent*, of the visible vertices. The percept does
-    *not* include the position of the robot! The robot must
+-   The percept will be a list of the positions, <i>relative to the
+    agent</i>, of the visible vertices. The percept does
+    <i>not</i> include the position of the robot! The robot must
     learn its own position from the map; for now, you can assume that
     each location has a different “view.”<br />
 
@@ -181,7 +181,7 @@ <h3 class="masthead-title">
     otherwise, the robot stops at the point where its path first
     intersects an obstacle. If the agent returns a zero motion vector
     and is at the goal (which is fixed and known), then the environment
-    teleports the agent to a *random location* (not inside
+    teleports the agent to a <i>random location</i> (not inside
     an obstacle).<br />
 
 -   The performance measure charges the agent 1 point for each unit of
@@ -240,9 +240,9 @@ <h3 class="masthead-title">
 Exercise <a class="exerciseRef" href="/aima-exercises/search-exercises/ex_9/">path-planning-exercise</a> into an environment as
 follows:<br>
 
--   The percept will be a list of the positions, *relative to the
-    agent*, of the visible vertices. The percept does
-    *not* include the position of the robot! The robot must
+-   The percept will be a list of the positions, <i>relative to the
+    agent</i>, of the visible vertices. The percept does
+    <i>not</i> include the position of the robot! The robot must
     learn its own position from the map; for now, you can assume that
     each location has a different “view.”<br>
 
@@ -251,7 +251,7 @@ <h3 class="masthead-title">
     otherwise, the robot stops at the point where its path first
     intersects an obstacle. If the agent returns a zero motion vector
     and is at the goal (which is fixed and known), then the environment
-    teleports the agent to a *random location* (not inside
+    teleports the agent to a <i>random location</i> (not inside
     an obstacle).<br>
 
 -   The performance measure charges the agent 1 point for each unit of

_site/advanced-search-exercises/ex_12/index.html

@@ -170,8 +170,8 @@ <h3 class="masthead-title">
 maze environment like the one shown in
 Figure <a class="insideBookFigRef" target="_blank" href="https://aimacode.github.io/aima-exercises/figures/maze-3x3-figure.png">maze-3x3-figure</a>. The agent knows that its
 initial location is (1,1), that the goal is at (3,3), and that the
-actions *Up*, *Down*, *Left*, *Right* have their usual
-effects unless blocked by a wall. The agent does *not* know
+actions <i>Up</i>, <i>Down</i>, <i>Left</i>, <i>Right</i> have their usual
+effects unless blocked by a wall. The agent does <i>not</i> know
 where the internal walls are. In any given state, the agent perceives
 the set of legal actions; it can also tell whether the state is one it
 has visited before.<br />
@@ -186,8 +186,8 @@ <h3 class="masthead-title">
 3.  Describe the first few branches of a contingency plan for this
     problem. How large (roughly) is the complete plan?<br />
 
-Notice that this contingency plan is a solution for *every
-possible environment* fitting the given description. Therefore,
+Notice that this contingency plan is a solution for <i>every
+possible environment</i> fitting the given description. Therefore,
 interleaving of search and execution is not strictly necessary even in
 unknown environments.
 </div>
@@ -214,8 +214,8 @@ <h3 class="masthead-title">
 maze environment like the one shown in
 Figure <a class="insideBookFigRef"  target="_blank" href="https://aimacode.github.io/aima-exercises/figures/maze-3x3-figure.png">maze-3x3-figure</a>. The agent knows that its
 initial location is (1,1), that the goal is at (3,3), and that the
-actions *Up*, *Down*, *Left*, *Right* have their usual
-effects unless blocked by a wall. The agent does *not* know
+actions <i>Up</i>, <i>Down</i>, <i>Left</i>, <i>Right</i> have their usual
+effects unless blocked by a wall. The agent does <i>not</i> know
 where the internal walls are. In any given state, the agent perceives
 the set of legal actions; it can also tell whether the state is one it
 has visited before.<br>
@@ -230,8 +230,8 @@ <h3 class="masthead-title">
 3.  Describe the first few branches of a contingency plan for this
     problem. How large (roughly) is the complete plan?<br>
 
-Notice that this contingency plan is a solution for *every
-possible environment* fitting the given description. Therefore,
+Notice that this contingency plan is a solution for <i>every
+possible environment</i> fitting the given description. Therefore,
 interleaving of search and execution is not strictly necessary even in
 unknown environments.
 </p>

_site/advanced-search-exercises/ex_13/index.html

@@ -166,11 +166,11 @@ <h3 class="masthead-title">
 
 <div id="hiddden">
 
-The **And-Or-Graph-Search** algorithm in
+The <b>And-Or-Graph-Search</b> algorithm in
 Figure <a class="insideBookFigRef" target="_blank" href="https://aimacode.github.io/aima-exercises/figures/and-or-graph-search-algorithm.png">and-or-graph-search-algorithm</a> checks for
 repeated states only on the path from the root to the current state.
 Suppose that, in addition, the algorithm were to store
-*every* visited state and check against that list. (See in
+<i>every</i> visited state and check against that list. (See in
 Figure <a class="insideBookFigRef" href="#">breadth-first-search-algorithm</a> for an example.)
 Determine the information that should be stored and how the algorithm
 should use that information when a repeated state is found.
@@ -199,11 +199,11 @@ <h3 class="masthead-title">
     <div class="card-body">
         <p class="card-text">
 
-The **And-Or-Graph-Search** algorithm in
+The <b>And-Or-Graph-Search</b> algorithm in
 Figure <a class="insideBookFigRef" target="_blank" href="https://aimacode.github.io/aima-exercises/figures/and-or-graph-search-algorithm.png">and-or-graph-search-algorithm</a> checks for
 repeated states only on the path from the root to the current state.
 Suppose that, in addition, the algorithm were to store
-*every* visited state and check against that list. (See in
+<i>every</i> visited state and check against that list. (See in
 Figure <a class="insideBookFigRef" href="#">breadth-first-search-algorithm</a> for an example.)
 Determine the information that should be stored and how the algorithm
 should use that information when a repeated state is found.

_site/advanced-search-exercises/ex_5/index.html

@@ -166,10 +166,10 @@ <h3 class="masthead-title">
 
 <div id="hiddden">
 
-Explain precisely how to modify the **And-Or-Graph-Search** algorithm to
+Explain precisely how to modify the <b>And-Or-Graph-Search</b> algorithm to
 generate a cyclic plan if no acyclic plan exists. You will need to deal
 with three issues: labeling the plan steps so that a cyclic plan can
-point back to an earlier part of the plan, modifying **Or-Search** so that it
+point back to an earlier part of the plan, modifying <b>Or-Search</b> so that it
 continues to look for acyclic plans after finding a cyclic plan, and
 augmenting the plan representation to indicate whether a plan is cyclic.
 Show how your algorithm works on (a) the slippery vacuum world, and (b)
@@ -195,10 +195,10 @@ <h3 class="masthead-title">
     <div class="card-body">
         <p class="card-text">
 
-Explain precisely how to modify the **And-Or-Graph-Search** algorithm to
+Explain precisely how to modify the <b>And-Or-Graph-Search</b> algorithm to
 generate a cyclic plan if no acyclic plan exists. You will need to deal
 with three issues: labeling the plan steps so that a cyclic plan can
-point back to an earlier part of the plan, modifying **Or-Search** so that it
+point back to an earlier part of the plan, modifying <b>Or-Search</b> so that it
 continues to look for acyclic plans after finding a cyclic plan, and
 augmenting the plan representation to indicate whether a plan is cyclic.
 Show how your algorithm works on (a) the slippery vacuum world, and (b)

_site/advanced-search-exercises/ex_6/index.html

@@ -173,8 +173,8 @@ <h3 class="masthead-title">
 optimality still make sense in this context, or does it require
 modification? Consider also various possible definitions of the “cost”
 of executing an action in a belief state; for example, we could use the
-*minimum* of the physical costs; or the
-*maximum*; or a cost *interval* with the lower
+<i>minimum</i> of the physical costs; or the
+<i>maximum</i>; or a cost <i>interval</i> with the lower
 bound being the minimum cost and the upper bound being the maximum; or
 just keep the set of all possible costs for that action. For each of
 these, explore whether A* (with modifications if necessary) can return
@@ -206,8 +206,8 @@ <h3 class="masthead-title">
 optimality still make sense in this context, or does it require
 modification? Consider also various possible definitions of the “cost”
 of executing an action in a belief state; for example, we could use the
-*minimum* of the physical costs; or the
-*maximum*; or a cost *interval* with the lower
+<i>minimum</i> of the physical costs; or the
+<i>maximum</i>; or a cost <i>interval</i> with the lower
 bound being the minimum cost and the upper bound being the maximum; or
 just keep the set of all possible costs for that action. For each of
 these, explore whether A* (with modifications if necessary) can return

_site/advanced-search-exercises/ex_9/index.html

@@ -258,11 +258,11 @@ <h1 id="4-beyond-classical-search">4. Beyond Classical Search</h1>
     <div class="card-body">
         <p class="card-text">
 
-The **And-Or-Graph-Search** algorithm in
+The <b>And-Or-Graph-Search</b> algorithm in
 Figure <a class="insideBookFigRef" target="_blank" href="https://aimacode.github.io/aima-exercises/figures/and-or-graph-search-algorithm.png">and-or-graph-search-algorithm</a> checks for
 repeated states only on the path from the root to the current state.
 Suppose that, in addition, the algorithm were to store
-*every* visited state and check against that list. (See in
+<i>every</i> visited state and check against that list. (See in
 Figure <a class="insideBookFigRef" href="#">breadth-first-search-algorithm</a> for an example.)
 Determine the information that should be stored and how the algorithm
 should use that information when a repeated state is found.
@@ -286,10 +286,10 @@ <h1 id="4-beyond-classical-search">4. Beyond Classical Search</h1>
     <div class="card-body">
         <p class="card-text">
 
-Explain precisely how to modify the **And-Or-Graph-Search** algorithm to
+Explain precisely how to modify the <b>And-Or-Graph-Search</b> algorithm to
 generate a cyclic plan if no acyclic plan exists. You will need to deal
 with three issues: labeling the plan steps so that a cyclic plan can
-point back to an earlier part of the plan, modifying **Or-Search** so that it
+point back to an earlier part of the plan, modifying <b>Or-Search</b> so that it
 continues to look for acyclic plans after finding a cyclic plan, and
 augmenting the plan representation to indicate whether a plan is cyclic.
 Show how your algorithm works on (a) the slippery vacuum world, and (b)
@@ -367,8 +367,8 @@ <h1 id="4-beyond-classical-search">4. Beyond Classical Search</h1>
 optimality still make sense in this context, or does it require
 modification? Consider also various possible definitions of the “cost”
 of executing an action in a belief state; for example, we could use the
-*minimum* of the physical costs; or the
-*maximum*; or a cost *interval* with the lower
+<i>minimum</i> of the physical costs; or the
+<i>maximum</i>; or a cost <i>interval</i> with the lower
 bound being the minimum cost and the upper bound being the maximum; or
 just keep the set of all possible costs for that action. For each of
 these, explore whether A* (with modifications if necessary) can return
@@ -430,9 +430,9 @@ <h1 id="4-beyond-classical-search">4. Beyond Classical Search</h1>
 Exercise <a class="exerciseRef" href="/aima-exercises/search-exercises/ex_9/">path-planning-exercise</a> into an environment as
 follows:<br />
 
--   The percept will be a list of the positions, *relative to the
-    agent*, of the visible vertices. The percept does
-    *not* include the position of the robot! The robot must
+-   The percept will be a list of the positions, <i>relative to the
+    agent</i>, of the visible vertices. The percept does
+    <i>not</i> include the position of the robot! The robot must
     learn its own position from the map; for now, you can assume that
     each location has a different “view.”<br />
 
@@ -441,7 +441,7 @@ <h1 id="4-beyond-classical-search">4. Beyond Classical Search</h1>
     otherwise, the robot stops at the point where its path first
     intersects an obstacle. If the agent returns a zero motion vector
     and is at the goal (which is fixed and known), then the environment
-    teleports the agent to a *random location* (not inside
+    teleports the agent to a <i>random location</i> (not inside
     an obstacle).<br />
 
 -   The performance measure charges the agent 1 point for each unit of
@@ -495,8 +495,8 @@ <h1 id="4-beyond-classical-search">4. Beyond Classical Search</h1>
 maze environment like the one shown in
 Figure <a class="insideBookFigRef" target="_blank" href="https://aimacode.github.io/aima-exercises/figures/maze-3x3-figure.png">maze-3x3-figure</a>. The agent knows that its
 initial location is (1,1), that the goal is at (3,3), and that the
-actions *Up*, *Down*, *Left*, *Right* have their usual
-effects unless blocked by a wall. The agent does *not* know
+actions <i>Up</i>, <i>Down</i>, <i>Left</i>, <i>Right</i> have their usual
+effects unless blocked by a wall. The agent does <i>not</i> know
 where the internal walls are. In any given state, the agent perceives
 the set of legal actions; it can also tell whether the state is one it
 has visited before.<br />
@@ -511,8 +511,8 @@ <h1 id="4-beyond-classical-search">4. Beyond Classical Search</h1>
 3.  Describe the first few branches of a contingency plan for this
     problem. How large (roughly) is the complete plan?<br />
 
-Notice that this contingency plan is a solution for *every
-possible environment* fitting the given description. Therefore,
+Notice that this contingency plan is a solution for <i>every
+possible environment</i> fitting the given description. Therefore,
 interleaving of search and execution is not strictly necessary even in
 unknown environments.
 </p>

_site/advanced-search-exercises/index.html

@@ -173,9 +173,9 @@ <h3 class="masthead-title">
 
     1.  ${\textbf{P}}(B,I,M) = {\textbf{P}}(B){\textbf{P}}(I){\textbf{P}}(M)$.<br />
 
-    2.  ${\textbf{P}}(JG) = {\textbf{P}}(JG,I)$.<br />
+    2.  ${\textbf{P}}(J|G) = {\textbf{P}}(J|G,I)$.<br />
 
-    3.  ${\textbf{P}}(MG,B,I) = {\textbf{P}}(MG,B,I,J)$.<br />
+    3.  ${\textbf{P}}(M|G,B,I) = {\textbf{P}}(M|G,B,I,J)$.<br />
 
 2.  Calculate the value of $P(b,i,\lnot m,g,j)$.<br />
 
@@ -191,7 +191,7 @@ <h3 class="masthead-title">
     Figure <a class="insideExercisesFigRef" href="#politics-figure">politics-figure</a>?<br />
 
 5.  Suppose we want to add the variable
-    $P{PresidentialPardon}$ to the network; draw the new
+    $P={PresidentialPardon}$ to the network; draw the new
     network and briefly explain any links you add.<br />
 <figure>
   <img src="https://aimacode.github.io/aima-exercises/figures/politics.svg" alt="politics-figure" id="politics-figure" style="width:100%" />
@@ -224,9 +224,9 @@ <h3 class="masthead-title">
 
     1.  ${\textbf{P}}(B,I,M) = {\textbf{P}}(B){\textbf{P}}(I){\textbf{P}}(M)$.<br>
 
-    2.  ${\textbf{P}}(JG) = {\textbf{P}}(JG,I)$.<br>
+    2.  ${\textbf{P}}(J|G) = {\textbf{P}}(J|G,I)$.<br>
 
-    3.  ${\textbf{P}}(MG,B,I) = {\textbf{P}}(MG,B,I,J)$.<br>
+    3.  ${\textbf{P}}(M|G,B,I) = {\textbf{P}}(M|G,B,I,J)$.<br>
 
 2.  Calculate the value of $P(b,i,\lnot m,g,j)$.<br>
 
@@ -242,7 +242,7 @@ <h3 class="masthead-title">
     Figure <a class="insideExercisesFigRef" href="#politics-figure">politics-figure</a>?<br>
 
 5.  Suppose we want to add the variable
-    $P{PresidentialPardon}$ to the network; draw the new
+    $P={PresidentialPardon}$ to the network; draw the new
     network and briefly explain any links you add.<br>
 <figure>
   <img src="https://aimacode.github.io/aima-exercises/figures/politics.svg" alt="politics-figure" id="politics-figure" style="width:100%">

_site/bayes-nets-exercises/ex_16/index.html

@@ -173,9 +173,9 @@ <h3 class="masthead-title">
 
     1.  ${\textbf{P}}(B,I,M) = {\textbf{P}}(B){\textbf{P}}(I){\textbf{P}}(M)$.<br />
 
-    2.  ${\textbf{P}}(JG) = {\textbf{P}}(JG,I)$.<br />
+    2.  ${\textbf{P}}(J|G) = {\textbf{P}}(J|G,I)$.<br />
 
-    3.  ${\textbf{P}}(MG,B,I) = {\textbf{P}}(MG,B,I,J)$.<br />
+    3.  ${\textbf{P}}(M|G,B,I) = {\textbf{P}}(M|G,B,I,J)$.<br />
 
 2.  Calculate the value of $P(b,i,\lnot m,g,j)$.<br />
 
@@ -191,7 +191,7 @@ <h3 class="masthead-title">
     Figure <a class="insideExercisesFigRef" id="insideexercisesfigref" href="#politics-figure">politics-figure</a>?<br />
 
 5.  Suppose we want to add the variable
-    $P{PresidentialPardon}$ to the network; draw the new
+    $P={PresidentialPardon}$ to the network; draw the new
     network and briefly explain any links you add.<br />
 </div>
 
@@ -220,9 +220,9 @@ <h3 class="masthead-title">
 
     1.  ${\textbf{P}}(B,I,M) = {\textbf{P}}(B){\textbf{P}}(I){\textbf{P}}(M)$.<br>
 
-    2.  ${\textbf{P}}(JG) = {\textbf{P}}(JG,I)$.<br>
+    2.  ${\textbf{P}}(J|G) = {\textbf{P}}(J|G,I)$.<br>
 
-    3.  ${\textbf{P}}(MG,B,I) = {\textbf{P}}(MG,B,I,J)$.<br>
+    3.  ${\textbf{P}}(M|G,B,I) = {\textbf{P}}(M|G,B,I,J)$.<br>
 
 2.  Calculate the value of $P(b,i,\lnot m,g,j)$.<br>
 
@@ -238,7 +238,7 @@ <h3 class="masthead-title">
     Figure <a class="insideExercisesFigRef" id="insideexercisesfigref" href="#politics-figure">politics-figure</a>?<br>
 
 5.  Suppose we want to add the variable
-    $P{PresidentialPardon}$ to the network; draw the new
+    $P={PresidentialPardon}$ to the network; draw the new
     network and briefly explain any links you add.<br>
 </p>
     </div>