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diff --git a/figures/ geometric-scene-figure.png b/figures/ geometric-scene-figure.png
diff --git a/figures/DTL-algorithm.png b/figures/DTL-algorithm.png
diff --git a/figures/FigArm1.png b/figures/FigArm1.png
diff --git a/figures/airport-au-id-exercise.png b/figures/airport-au-id-exercise.png
diff --git a/figures/airport-id-figure.png b/figures/airport-id-figure.png
diff --git a/figures/airport-pddl-algorithm.png b/figures/airport-pddl-algorithm.png
diff --git a/figures/and-or-graph-search-algorithm.png b/figures/and-or-graph-search-algorithm.png
diff --git a/figures/australia-figure.png b/figures/australia-figure.png
diff --git a/figures/backward-chaining-algorithm.png b/figures/backward-chaining-algorithm.png
diff --git a/figures/battery-persistence-figure.png b/figures/battery-persistence-figure.png
diff --git a/figures/bayes-candy-figure.png b/figures/bayes-candy-figure.png
diff --git a/figures/burglary-figure.png b/figures/burglary-figure.png
diff --git a/figures/computer-brain-table.png b/figures/computer-brain-table.png
diff --git a/figures/cryptarithmetic-figure.png b/figures/cryptarithmetic-figure.png
diff --git a/figures/de-finetti-table.png b/figures/de-finetti-table.png
diff --git a/figures/dentist-joint-table.png b/figures/dentist-joint-table.png
diff --git a/figures/elimination-ask-algorithm.png b/figures/elimination-ask-algorithm.png
diff --git a/figures/enumeration-algorithm.png b/figures/enumeration-algorithm.png
diff --git a/figures/family2-figure.png b/figures/family2-figure.png
diff --git a/figures/forward-backward-algorithm.png b/figures/forward-backward-algorithm.png
diff --git a/figures/hybrid-wumpus-agent-algorithm.png b/figures/hybrid-wumpus-agent-algorithm.png
diff --git a/figures/illuminationfigure.png b/figures/illuminationfigure.png
diff --git a/figures/jobshop-cpm-figure.png b/figures/jobshop-cpm-figure.png
diff --git a/figures/line-game4-figure.png b/figures/line-game4-figure.png
diff --git a/figures/logical-equivalence-table.png b/figures/logical-equivalence-table.png
diff --git a/figures/maze-3x3-figure.png b/figures/maze-3x3-figure.png
diff --git a/figures/mixture-networks-figure.png b/figures/mixture-networks-figure.png
diff --git a/figures/mt-alignment-figure.png b/figures/mt-alignment-figure.png
diff --git a/figures/ontology-figure.png b/figures/ontology-figure.png
diff --git a/figures/pl-horn-example-figure.png b/figures/pl-horn-example-figure.png
diff --git a/figures/rain-clustering-figure.png b/figures/rain-clustering-figure.png
diff --git a/figures/restaurant-back-prop-figure.png b/figures/restaurant-back-prop-figure.png
diff --git a/figures/satplan-agent-algorithm.png b/figures/satplan-agent-algorithm.png
diff --git a/figures/sequential-decision-policies-figure.png b/figures/sequential-decision-policies-figure.png
diff --git a/figures/sequential-decision-world-figure.png b/figures/sequential-decision-world-figure.png
diff --git a/figures/sr-hmm-figure.png b/figures/sr-hmm-figure.png
diff --git a/figures/subsumption-lattice-figure.png b/figures/subsumption-lattice-figure.png
diff --git a/figures/sussman-anamoly-figure.png b/figures/sussman-anamoly-figure.png
diff --git a/figures/tree-csp-figure.png b/figures/tree-csp-figure.png
diff --git a/figures/vacuum-agent-function-table.png b/figures/vacuum-agent-function-table.png
diff --git a/figures/vacuum-maze-ch4-figure.png b/figures/vacuum-maze-ch4-figure.png
diff --git a/figures/vacuum-maze-hmm2-figure.png b/figures/vacuum-maze-hmm2-figure.png
diff --git a/figures/vacuum-world-figure.png b/figures/vacuum-world-figure.png
diff --git a/figures/vacuum2-sets-figure.png b/figures/vacuum2-sets-figure.png
diff --git a/figures/wumpus-entailment-figure.png b/figures/wumpus-entailment-figure.png
diff --git a/figures/wumpus-seq35-figure.png b/figures/wumpus-seq35-figure.png
diff --git a/markdown/1-Introduction/exercises/ex_2/question.md b/markdown/1-Introduction/exercises/ex_2/question.md
@@ -1,5 +1,4 @@
-Read Turing’s original paper on AI @Turing:1950. In the paper, he
-discusses several objections to his proposed enterprise and his test for
+Read Turing’s original paper on AI <a class="paperRef" title="" href="">Turing:1950 </a>.In the paper, he discusses several objections to his proposed enterprise and his test for
 intelligence. Which objections still carry weight? Are his refutations
 valid? Can you think of new objections arising from developments since
 he wrote the paper? In the paper, he predicts that, by the year 2000, a

diff --git a/markdown/1-Introduction/exercises/ex_3/question.md b/markdown/1-Introduction/exercises/ex_3/question.md
@@ -1,4 +1,4 @@
 Every year the Loebner Prize is awarded to the program that comes
-closest to passing a version of the Turing Test. Research and report on
+closest to passing a version of the <a href="https://en.wikipedia.org/wiki/Turing_test">Turing Test</a>. Research and report on
 the latest winner of the Loebner prize. What techniques does it use? How
 does it advance the state of the art in AI?
diff --git a/markdown/1-Introduction/exercises/ex_7/question.md b/markdown/1-Introduction/exercises/ex_7/question.md
@@ -1,9 +1,7 @@
-
-
 The neural structure of the sea slug <i>Aplysis</i> has been
 widely studied (first by Nobel Laureate Eric Kandel) because it has only
 about 20,000 neurons, most of them large and easily manipulated.
 Assuming that the cycle time for an <i>Aplysis</i> neuron is
 roughly the same as for a human neuron, how does the computational
 power, in terms of memory updates per second, compare with the high-end
-computer described in (Figure <a href="#">computer-brain-table</a>)?
+computer described in (Figure <a class ="insideBookFigRef" target="_blank" href="https://aimacode.github.io/aima-exercises/figures/computer-brain-table.png">computer-brain-table</a>)?
diff --git a/markdown/10-Classical-Planning/exercises/ex_10/question.md b/markdown/10-Classical-Planning/exercises/ex_10/question.md
@@ -1,4 +1,4 @@
 
 
 Construct levels 0, 1, and 2 of the planning graph for the problem in
-Figure <a href="#">airport-pddl-algorithm</a>
+Figure <a class="insideBookFigRef" target="_blank" href="https://aimacode.github.io/aima-exercises/figures/airport-pddl-algorithm.png">airport-pddl-algorithm</a>
diff --git a/markdown/10-Classical-Planning/exercises/ex_13/question.md b/markdown/10-Classical-Planning/exercises/ex_13/question.md
@@ -1,5 +1,5 @@
 
 
-The set-level heuristic (see page <a href="#">set-level-page</a> uses a planning graph
+The set-level heuristic (see page <a class="pageRef" title="" href="#">set-level-page</a> uses a planning graph
 to estimate the cost of achieving a conjunctive goal from the current
 state. What relaxed problem is the set-level heuristic the solution to?
diff --git a/markdown/10-Classical-Planning/exercises/ex_14/question.md b/markdown/10-Classical-Planning/exercises/ex_14/question.md
@@ -1,7 +1,7 @@
 
 
 Examine the definition of **bidirectional
-search** in Chapter <a href="#">search-chapter</a>.<br>
+search** in Chapter <a class="chapterRef" title="" href="{{site.baseurl}}/search-exercises/">search-chapter</a>.<br>
 
 1.  Would bidirectional state-space search be a good idea for planning?<br>
 

diff --git a/markdown/10-Classical-Planning/exercises/ex_18/question.md b/markdown/10-Classical-Planning/exercises/ex_18/question.md
@@ -1,7 +1,7 @@
 
 
 In the $SATPlan$ algorithm in
-Figure <a href="#">satplan-agent-algorithm</a> (page <a href="#">satplan-agent-algorithm</a>,
+Figure <a class="insideBookFigRef" target="_blank" href="https://aimacode.github.io/aima-exercises/figures/satplan-agent-algorithm.png">satplan-agent-algorithm</a> (page <a class="pageRef" title="" href="#">satplan-agent-algorithm</a>,
 each call to the satisfiability algorithm asserts a goal $g^T$, where
 $T$ ranges from 0 to $T_{max}$. Suppose instead that the
 satisfiability algorithm is called only once, with the goal

diff --git a/markdown/10-Classical-Planning/exercises/ex_3/question.md b/markdown/10-Classical-Planning/exercises/ex_3/question.md
@@ -1,7 +1,5 @@
-
-
-<a href="#">strips-airport-exercise</a>Given the action schemas and initial state
-from Figure <a href="#">airport-pddl-algorithm</a>, what are all the
+Given the action schemas and initial state
+from Figure <a class="insideBookFigRef" target="_blank" href="https://aimacode.github.io/aima-exercises/figures/airport-pddl-algorithm.png">airport-pddl-algorithm</a>, what are all the
 applicable concrete instances of ${Fly}(p,{from},{to})$ in the
 state described by<br>
 

diff --git a/markdown/10-Classical-Planning/exercises/ex_5/question.md b/markdown/10-Classical-Planning/exercises/ex_5/question.md
@@ -1,7 +1,7 @@
 
 
 The original {Strips} planner was designed to control Shakey the robot.
-Figure <a href="#shakey-figure">shakey-figure</a> shows a version of Shakey’s world
+Figure <a class="insideExercisesFigRef" href="#shakey-figure">shakey-figure</a> shows a version of Shakey’s world
 consisting of four rooms lined up along a corridor, where each room has
 a door and a light switch. The actions in Shakey’s world include moving from place to place,
 pushing movable objects (such as boxes), climbing onto and down from

diff --git a/markdown/10-Classical-Planning/exercises/ex_8/question.md b/markdown/10-Classical-Planning/exercises/ex_8/question.md
@@ -1,7 +1,7 @@
 
 
-Figure <a href="#">sussman-anomaly-figure</a>
-(page <a href="#">sussman-anomaly-figure</a>) shows a blocks-world problem that is known as the {Sussman anomaly}.
+Figure <a class="insideBookFigRef" target="_blank" href="https://aimacode.github.io/aima-exercises/figures/sussman-anamoly-figure.png">sussman-anomaly-figure</a>
+(page <a class="pageRef" title="" href="#">sussman-anomaly-figure</a>) shows a blocks-world problem that is known as the {Sussman anomaly}.
 The problem was considered anomalous because the noninterleaved planners
 of the early 1970s could not solve it. Write a definition of the problem
 and solve it, either by hand or with a planning program. A

diff --git a/markdown/10-Classical-Planning/index.md b/markdown/10-Classical-Planning/index.md
@@ -31,7 +31,7 @@ permalink: /planning-exercises/
 <br>
 <div class="card">
 <div class="card-header p-2">
-<a href='ex_3/' class="p-2">Exercise 3 </a>
+<a href='ex_3/' class="p-2">Exercise 3 (strips-airport-exercise) </a>
 <button type="button" class="btn btn-dark float-right" title="Bookmark Exercise" onclick="bookmark('ch10ex3');" href="#"><i id="ch10ex3" class="fas fa-bookmark" style="color:white"></i></button>
 <button type="button" class="btn btn-dark float-right" style="margin-left:10px; margin-right:10px;" title="Upvote Exercise" onclick="upvote('ex10.3);" href="#"><i id="ch10ex3" class="fas fa-thumbs-up" style="color:white"></i></button>
 </div>

diff --git a/markdown/11-Planning-And-Acting-In-The-Real-World/exercises/ex_5/question.md b/markdown/11-Planning-And-Acting-In-The-Real-World/exercises/ex_5/question.md
@@ -1,5 +1,3 @@
-[Exercise 11.5 \[HLA-progression-exercise\]](ex_5/)
-
 Write an algorithm that takes an initial
 state (specified by a set of propositional literals) and a sequence of
 HLAs (each defined by preconditions and angelic specifications of

diff --git a/markdown/11-Planning-And-Acting-In-The-Real-World/exercises/ex_6/question.md b/markdown/11-Planning-And-Acting-In-The-Real-World/exercises/ex_6/question.md
@@ -1,6 +1,6 @@
 
 
-In Figure <a href="#">jobshop-cpm-figure</a> we showed how to describe
+In Figure <a class="insideBookFigRef" target="_blank" href="https://aimacode.github.io/aima-exercises/figures/jobshop-cpm-figure.png">jobshop-cpm-figure</a> we showed how to describe
 actions in a scheduling problem by using separate fields for , , and .
 Now suppose we wanted to combine scheduling with nondeterministic
 planning, which requires nondeterministic and conditional effects.

diff --git a/markdown/12-Knowledge-Representation/exercises/ex_16/question.md b/markdown/12-Knowledge-Representation/exercises/ex_16/question.md
@@ -6,7 +6,7 @@ ${Go}(x,y)$, which takes it from city $x$ to city $y$ if there is a
 route between those cities. ${Road}(x, y)$ is true if and only if
 there is a road connecting cities $x$ and $y$; if there is, then
 ${Distance}(x, y)$ gives the length of the road. See the map on
-page <a href="#">romania-distances-figure</a> for an example. The robot begins in Arad and must
+page <a class="pageRef" title="" href="#">romania-distances-figure</a> for an example. The robot begins in Arad and must
 reach Bucharest.<br>
 
 1.  Write a suitable logical description of the initial situation of

diff --git a/markdown/12-Knowledge-Representation/exercises/ex_22/question.md b/markdown/12-Knowledge-Representation/exercises/ex_22/question.md
@@ -1,6 +1,6 @@
 
 
-(Adapted from @Fagin+al:1995.) Consider a game played
+(Adapted from <a class="paperRef" title="" href="">Fagin+al:1995</a>.) Consider a game played
 with a deck of just 8 cards, 4 aces and 4 kings. The three players,
 Alice, Bob, and Carlos, are dealt two cards each. Without looking at
 them, they place the cards on their foreheads so that the other players

diff --git a/markdown/12-Knowledge-Representation/exercises/ex_23/question.md b/markdown/12-Knowledge-Representation/exercises/ex_23/question.md
@@ -1,7 +1,7 @@
 
 
 The assumption of <i>logical omniscience,</i> discussed on
-page <a href="#">logical-omniscience</a>, is of course not true of any actual reasoners.
+page <a class="pageRef" title="" href="#">logical-omniscience</a>, is of course not true of any actual reasoners.
 Rather, it is an <i>idealization</i> of the reasoning process
 that may be more or less acceptable depending on the applications.
 Discuss the reasonableness of the assumption for each of the following

diff --git a/markdown/12-Knowledge-Representation/exercises/ex_24/question.md b/markdown/12-Knowledge-Representation/exercises/ex_24/question.md
@@ -1,7 +1,7 @@
 
 
 The assumption of <i>logical omniscience,</i> discussed on
-page <a href="#">logical-omniscience</a>, is of course not true of any actual reasoners.
+page <a class="pageRef" title="" href="#">logical-omniscience</a>, is of course not true of any actual reasoners.
 Rather, it is an <i>idealization</i> of the reasoning process
 that may be more or less acceptable depending on the applications.
 Discuss the reasonableness of the assumption for each of the following

diff --git a/markdown/12-Knowledge-Representation/exercises/ex_25/question.md b/markdown/12-Knowledge-Representation/exercises/ex_25/question.md
@@ -1,5 +1,5 @@
 Translate the following description logic expression (from
-page <a href="#">description-logic-ex</a>) into first-order logic, and comment on the result:
+page <a class="pageRef" title="" href="#">description-logic-ex</a>) into first-order logic, and comment on the result:
 <br>
 $$
 And(Man, AtLeast(3,Son), AtMost(2,Daughter), <br>All(Son,And(Unemployed,Married, All(Spouse,Doctor ))), <br>All(Daughter,And(Professor, Fills(Department ,Physics,Math))))

diff --git a/markdown/12-Knowledge-Representation/exercises/ex_29/question.md b/markdown/12-Knowledge-Representation/exercises/ex_29/question.md
@@ -3,8 +3,8 @@
 A complete solution to the problem of
 inexact matches to the buyer’s description in shopping is very difficult
 and requires a full array of natural language processing and information
-retrieval techniques. (See Chapters <a href="#">nlp1-chapter</a>
-and <a href="#">nlp-english-chapter</a>.) One small step is to allow the user to
+retrieval techniques. (See Chapters <a class="chapterRef" title="" href="{{site.baseurl}}/nlp1-exercises/">nlp1-chapter</a>
+and <a class="chapterRef" title="" href="{{site.baseurl}}/nlp-communicating-exercises/">nlp-english-chapter</a>.) One small step is to allow the user to
 specify minimum and maximum values for various attributes. The buyer
 must use the following grammar for product descriptions:<br>
 

diff --git a/markdown/12-Knowledge-Representation/exercises/ex_3/question.md b/markdown/12-Knowledge-Representation/exercises/ex_3/question.md
@@ -1,6 +1,6 @@
 
 
-Figure <a href="#">ontology-figure</a> shows the top levels of a
+Figure <a class="insideBookFigRef" target="_blank" href="https://aimacode.github.io/aima-exercises/figures/ontology-figure.png">ontology-figure</a> shows the top levels of a
 hierarchy for everything. Extend it to include as many real categories
 as possible. A good way to do this is to cover all the things in your
 everyday life. This includes objects and events. Start with waking up,

diff --git a/markdown/13-Quantifying-Uncertainity/exercises/ex_15/question.md b/markdown/13-Quantifying-Uncertainity/exercises/ex_15/question.md
@@ -1,4 +1,4 @@
 
 
 Show that the three forms of independence in
-Equation (<a href="#">independence-equation</a>) are equivalent.
+Equation (<a class="equationRef" title="" href="#">independence-equation</a>) are equivalent.
diff --git a/markdown/13-Quantifying-Uncertainity/exercises/ex_20/question.md b/markdown/13-Quantifying-Uncertainity/exercises/ex_20/question.md
@@ -11,4 +11,4 @@ some background evidence $\textbf{e}$: <br>
     $${\textbf{P}}(X,Y {{\,|\,}}\textbf{e}) = {\textbf{P}}(X{{\,|\,}}Y,\textbf{e}) {\textbf{P}}(Y{{\,|\,}}\textbf{e})\ .$$ <br>
 
 2.  Prove the conditionalized version of Bayes’ rule in
-    Equation (<a href="#">conditional-bayes-equation</a>). <br>
+    Equation (<a class="equationRef" title="" href="#">conditional-bayes-equation</a>). <br>
diff --git a/markdown/13-Quantifying-Uncertainity/exercises/ex_23/question.md b/markdown/13-Quantifying-Uncertainity/exercises/ex_23/question.md
@@ -5,5 +5,5 @@ normalization calculation for the meningitis example. First, make up a
 suitable value for $P(s{{\,|\,}}\lnot m)$, and use it to calculate
 unnormalized values for $P(m{{\,|\,}}s)$ and $P(\lnot m {{\,|\,}}s)$
 (i.e., ignoring the $P(s)$ term in the Bayes’ rule expression,
-Equation (<a href="#">meningitis-bayes-equation</a>). Now normalize
+Equation (<a class="equationRef" title="" href="#">meningitis-bayes-equation</a>). Now normalize
 these values so that they add to 1.
diff --git a/markdown/13-Quantifying-Uncertainity/exercises/ex_26/question.md b/markdown/13-Quantifying-Uncertainity/exercises/ex_26/question.md
@@ -1,6 +1,6 @@
 
 
-(Adapted from Pearl [-@Pearl:1988].) Suppose you are a witness to a
+(Adapted from Pearl [<a class="paperRef" title="" href="">Pearl:1988</a>].) Suppose you are a witness to a
 nighttime hit-and-run accident involving a taxi in Athens. All taxis in
 Athens are blue or green. You swear, under oath, that the taxi was blue.
 Extensive testing shows that, under the dim lighting conditions,

diff --git a/markdown/13-Quantifying-Uncertainity/exercises/ex_31/question.md b/markdown/13-Quantifying-Uncertainity/exercises/ex_31/question.md
@@ -2,5 +2,5 @@
 
 Implement a hybrid probabilistic agent for the wumpus world, based on
 the hybrid agent in
-Figure <a href="#">hybrid-wumpus-agent-algorithm</a> and the
+Figure <a class="insideBookFigRef" target="_blank" href="https://aimacode.github.io/aima-exercises/figures/hybrid-wumpus-agent-algorithm.png">hybrid-wumpus-agent-algorithm</a> and the
 probabilistic inference procedure outlined in this chapter.
diff --git a/markdown/13-Quantifying-Uncertainity/exercises/ex_4/question.md b/markdown/13-Quantifying-Uncertainity/exercises/ex_4/question.md
@@ -4,7 +4,7 @@ Would it be rational for an agent to hold the three beliefs
 $P(A) {{\,=\,}}{0.4}$, $P(B) {{\,=\,}}{0.3}$, and
 $P(A \lor B) {{\,=\,}}{0.5}$? If so, what range of probabilities would
 be rational for the agent to hold for $A \land B$? Make up a table like
-the one in Figure <a href="#">de-finetti-table</a>, and show how it
+the one in Figure <a class="insideBookFigRef" target="_blank" href="https://aimacode.github.io/aima-exercises/figures/de-finetti-table.png">de-finetti-table</a>, and show how it
 supports your argument about rationality. Then draw another version of
 the table where $P(A \lor B)
 {{\,=\,}}{0.7}$. Explain why it is rational to have this probability,

diff --git a/markdown/13-Quantifying-Uncertainity/exercises/ex_5/question.md b/markdown/13-Quantifying-Uncertainity/exercises/ex_5/question.md
@@ -1,7 +1,7 @@
 
 
 This question deals with the properties
-of possible worlds, defined on page <a href="#">possible-worlds-page</a> as assignments to all
+of possible worlds, defined on page <a class="pageRef" title="" href="#">possible-worlds-page</a> as assignments to all
 random variables. We will work with propositions that correspond to
 exactly one possible world because they pin down the assignments of all
 the variables. In probability theory, such propositions are called <b>atomic event</b>. For

diff --git a/markdown/13-Quantifying-Uncertainity/exercises/ex_6/question.md b/markdown/13-Quantifying-Uncertainity/exercises/ex_6/question.md
@@ -1,6 +1,6 @@
 
 
 Prove
-Equation (<a href="#">kolmogorov-disjunction-equation</a>) from
-Equations <a href="#">basic-probability-axiom-equation</a>
-and (<a href="#">proposition-probability-equation</a>.
+Equation (<a class="equationRef" title="" href="#">kolmogorov-disjunction-equation</a>) from
+Equations <a class="equationRef" title="" href="#">basic-probability-axiom-equation</a>
+and (<a class="equationRef" title="" href="#">proposition-probability-equation</a>.
diff --git a/markdown/13-Quantifying-Uncertainity/exercises/ex_8/question.md b/markdown/13-Quantifying-Uncertainity/exercises/ex_8/question.md
@@ -1,7 +1,7 @@
 
 
 Given the full joint distribution shown in
-Figure <a href="#">dentist-joint-table</a>, calculate the following:<br>
+Figure <a class="insideBookFigRef" target="_blank" href="https://aimacode.github.io/aima-exercises/figures/dentist-joint-table.png">dentist-joint-table</a>, calculate the following:<br>
 
 1.  $\textbf{P}({toothache})$.<br>
 

diff --git a/markdown/13-Quantifying-Uncertainity/exercises/ex_9/question.md b/markdown/13-Quantifying-Uncertainity/exercises/ex_9/question.md
@@ -1,7 +1,7 @@
 
 
 Given the full joint distribution shown in
-Figure <a href="#">dentist-joint-table</a>, calculate the following:<br>
+Figure <a class="insideBookFigRef" target="_blank" href="https://aimacode.github.io/aima-exercises/figures/dentist-joint-table.png">dentist-joint-table</a>, calculate the following:<br>
 
 1.  $\textbf{P}({toothache})$.<br>
 

diff --git a/markdown/14-Probabilistic-Reasoning/exercises/ex_10/question.md b/markdown/14-Probabilistic-Reasoning/exercises/ex_10/question.md
@@ -3,7 +3,7 @@
 Consider a simple Bayesian network with root variables ${Cold}$,
 ${Flu}$, and ${Malaria}$ and child variable ${Fever}$, with a
 noisy-OR conditional distribution for ${Fever}$ as described in
-Section <a href="#">canonical-distribution-section</a>. By adding
+Section <a class="sectionRef" title="" href="#">canonical-distribution-section</a>. By adding
 appropriate auxiliary variables for inhibition events and fever-inducing
 events, construct an equivalent Bayesian network whose CPTs (except for
 root variables) are deterministic. Define the CPTs and prove

diff --git a/markdown/14-Probabilistic-Reasoning/exercises/ex_11/question.md b/markdown/14-Probabilistic-Reasoning/exercises/ex_11/question.md
@@ -1,7 +1,7 @@
 
 
 Consider the family of linear Gaussian networks, as
-defined on page <a href="#">LG-network-page</a><br>.
+defined on page <a class="pageRef" title="" href="#">LG-network-page</a><br>.
 
 1.  In a two-variable network, let $X_1$ be the parent of $X_2$, let
     $X_1$ have a Gaussian prior, and let

diff --git a/markdown/14-Probabilistic-Reasoning/exercises/ex_12/question.md b/markdown/14-Probabilistic-Reasoning/exercises/ex_12/question.md
@@ -1,7 +1,7 @@
 
 
 The probit distribution defined on
-page <a href="#">probit-page</a> describes the probability distribution for a Boolean
+page <a class="pageRef" title="" href="#">probit-page</a> describes the probability distribution for a Boolean
 child, given a single continuous parent.<br>
 
 1.  How might the definition be extended to cover multiple continuous

diff --git a/markdown/14-Probabilistic-Reasoning/exercises/ex_14/question.md b/markdown/14-Probabilistic-Reasoning/exercises/ex_14/question.md
@@ -6,7 +6,7 @@ telescope can also (with a much smaller probability $f$) be badly out of
 focus (events $F_1$ and $F_2$), in which case the scientist will
 undercount by three or more stars (or if $N$ is less than 3, fail to
 detect any stars at all). Consider the three networks shown in
-Figure <a href="#telescope-nets-figure">telescope-nets-figure</a>.<br>
+Figure <a class="insideExercisesFigRef"  href="#telescope-nets-figure">telescope-nets-figure</a>.<br>
 
 1.  Which of these Bayesian networks are correct (but not
     necessarily efficient) representations of the preceding information?<br>

diff --git a/markdown/14-Probabilistic-Reasoning/exercises/ex_15/question.md b/markdown/14-Probabilistic-Reasoning/exercises/ex_15/question.md
@@ -1,12 +1,12 @@
 
 
 Consider the network shown in
-Figure <a href="#telescope-nets-figure"telescope-nets-figure</a>(ii), and assume that the
+Figure <a class="insideExercisesFigRef" href="#telescope-nets-figure">telescope-nets-figure</a>(ii), and assume that the
 two telescopes work identically. $N{{\,\in\\,}}\{1,2,3\}$ and
 $M_1,M_2{{\,\in\\,}}\{0,1,2,3,4\}$, with the symbolic CPTs as described
-in Exercise <a href="#">telescope-exercise</a>. Using the enumeration
-algorithm (Figure <a href="#">enumeration-algorithm</a> on
-page <a href="#">enumeration-algorithm</a>), calculate the probability distribution
+in Exercise <a class="exerciseRef" href="{{ site.baseurl }}/bayes-nets-exercises/ex_14/">telescope-exercise</a>. Using the enumeration
+algorithm (Figure <a class="insideBookFigRef" target="_blank" href="https://aimacode.github.io/aima-exercises/figures/enumeration-algorithm.png">enumeration-algorithm</a> on
+page <a class="pageRef" id="pageref" title="" href="#">enumeration-algorithm</a>), calculate the probability distribution
 ${\textbf{P}}(N{{\,|\,}}M_1{{\,=\,}}2,M_2{{\,=\,}}2)$.<br>
 
 
@@ -16,8 +16,3 @@ ${\textbf{P}}(N{{\,|\,}}M_1{{\,=\,}}2,M_2{{\,=\,}}2)$.<br>
 </figure>
 
 
-<figure>
-  <img src="https://aimacode.github.io/aima-exercises/figures/politics.svg" alt="car-starts-figure" id="car-starts-figure" style="width:100%">
-  <figcaption><center><b>A simple Bayes net with
-  Boolean variables B = {BrokeElectionLaw}, I = {Indicted}, M = {PoliticallyMotivatedProsecutor}, G= {FoundGuilty}, J = {Jailed}.</b></center></figcaption>
-</figure>
diff --git a/markdown/14-Probabilistic-Reasoning/exercises/ex_16/question.md b/markdown/14-Probabilistic-Reasoning/exercises/ex_16/question.md
@@ -1,6 +1,6 @@
 
 
-Consider the Bayes net shown in Figure <a href="#politics-figure">politics-figure</a><br>.
+Consider the Bayes net shown in Figure <a class="insideExercisesFigRef" href="#politics-figure">politics-figure</a><br>.
 
 1.  Which of the following are asserted by the network
     <i>structure</i>?<br>
@@ -18,12 +18,17 @@ Consider the Bayes net shown in Figure <a href="#politics-figure">politics-figu
     motivated prosecutor.<br>
 
 4.  A <b>context-specific independence</b> (see
-    page <a href="#">CSI-page</a>) allows a variable to be independent of some of
+    page <a class="pageRef" title="" href="#">CSI-page</a>) allows a variable to be independent of some of
     its parents given certain values of others. In addition to the usual
     conditional independences given by the graph structure, what
     context-specific independences exist in the Bayes net in
-    Figure <a href="#politics-figure">politics-figure</a>?<br>
+    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
     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%">
+  <figcaption><center><b>A simple Bayes net with
+  Boolean variables B = {BrokeElectionLaw}, I = {Indicted}, M = {PoliticallyMotivatedProsecutor}, G= {FoundGuilty}, J = {Jailed}.</b></center></figcaption>
+</figure>