Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search,
Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
Lists
Minmax Algorithm In Artificial Intelligence slidesSamiaAziz4
Mini-max algorithm is a recursive or backtracking algorithm that is used in decision-making and game theory. Mini-Max algorithm uses recursion to search through the game-tree.
Min-Max algorithm is mostly used for game playing in AI. Such as Chess, Checkers, tic-tac-toe, go, and various tow-players game. This Algorithm computes the minimax decision for the current state.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search,
Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
Lists
Minmax Algorithm In Artificial Intelligence slidesSamiaAziz4
Mini-max algorithm is a recursive or backtracking algorithm that is used in decision-making and game theory. Mini-Max algorithm uses recursion to search through the game-tree.
Min-Max algorithm is mostly used for game playing in AI. Such as Chess, Checkers, tic-tac-toe, go, and various tow-players game. This Algorithm computes the minimax decision for the current state.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
2. Game Theory,
Optimal Decisions in Games,
Heuristic Alpha–Beta Tree Search,
Monte Carlo Tree Search,
Stochastic Games, Partially Observable
Games,
Limitations of Game Search Algorithms,
Constraint Satisfaction Problems (CSP),
Constraint Propagation: Inference in CSPs,
Backtracking Search for CSPs.
3. Many applications for AI
Computer vision, natural language processing,
speech recognition, search …
But games are some of the more interesting
Opponents that are challenging, or allies that
are helpful
Unit that is credited with acting on own
Human-level intelligence too hard
But under narrow circumstances can do pretty well
(ex: chess and Deep Blue)
For many games, often constrained (by game rules)
4. we cover competitive environments, in
which the agents’
goals are in conflict, giving rise GAME to
adversarial search problems—often known
as games.
5. MinMax the heart of almost every computer board
game
Applies to games where:
Players take turns
Have perfect information
Chess, Checkers, Tactics
But can work for games without perfect information
or chance
Poker, Monopoly, Dice
Can work in real-time (ie- not turn based) with timer
(iterative deepening, later)
6. Search tree
Squares represent decision states (ie- after a move)
Branches are decisions (ie- the move)
Start at root
Nodes at end are leaf nodes
Ex: Tic-Tac-Toe (symmetrical positions removed)
• Unlike binary trees can have any number of children
– Depends on the game situation
• Levels usually called plies (a ply is one level)
– Each ply is where "turn" switches to other player
• Players called Min and Max (next)
7. Named MinMax because of algorithm behind data
structure
Assign points to the outcome of a game
Ex: Tic-Tac-Toe: X wins, value of 1. O wins, value -
1.
Max (X) tries to maximize point value, while Min
(O) tries to minimize point value
Assume both players play to best of their ability
Always make a move to minimize or maximize
points
So, in choosing, Max will choose best move to
get highest points, assuming Min will choose best
move to get lowest points
Click to add text
8. With full tree, can determine best possible move
However, full tree impossible for some games! Ex: Chess
At a given time, chess has ~ 35 legal moves. Exponential
growth:
35 at one ply, 352 = 1225 at two plies … 356 = 2 billion and 3510 = 2
quadrillion
Games can last 40 moves (or more), so 3540 … Stars in universe:
~ 228
For large games (Chess) can’t see end of the game. Must estimate
winning or losing from top portion
Evaluate() function to guess end given board
A numeric value, much smaller than victory (ie- Checkmate for
Max will be one million, for Min minus one million)
So, computer’s strength at chess comes from:
How deep can search
How well can evaluate a board position
(In some sense, like a human – a chess grand master can
evaluate board better and can look further ahead)
9. How do we search this tree to find the optimal move?
10. Search – no adversary
Solution is (heuristic) method for finding goal
Heuristics and CSP techniques can find optimal solution
Evaluation function: estimate of cost from start to goal through given node
Examples: path planning, scheduling activities
Games – adversary
Solution is strategy
strategy specifies move for every possible opponent reply.
Time limits force an approximate solution
Evaluation function: evaluate “goodness” of game position
Examples: chess, checkers, Othello, backgammon
11. Two players: MAX and MIN
MAX moves first and they take turns until the game is over
Winner gets reward, loser gets penalty.
“Zero sum” means the sum of the reward and the penalty is a constant.
Formal definition as a search problem:
Initial state: Set-up specified by the rules, e.g., initial board configuration
of chess.
Player(s): Defines which player has the move in a state.
Actions(s): Returns the set of legal moves in a state.
Result(s,a): Transition model defines the result of a move.
(2nd ed.: Successor function: list of (move,state) pairs specifying legal
moves.)
Terminal-Test(s): Is the game finished? True if finished, false otherwise.
Utility function(s,p): Gives numerical value of terminal state s for player p.
E.g., win (+1), lose (-1), and draw (0) in tic-tac-toe.
E.g., win (+1), lose (0), and draw (1/2) in chess.
MAX uses search tree to determine next move.
12. Designed to find the optimal strategy for Max and find
best move:
1. Generate the whole game tree, down to the
leaves.
2. Apply utility (payoff) function to each leaf.
3. Back-up values from leaves through branch nodes:
a Max node computes the Max of its child values
a Min node computes the Min of its child values
4. At root: choose the move leading to the child of
highest value.
16. Mini-max algorithm is a recursive or backtracking algorithm
which is used in decision-making and game theory.
It provides an optimal move for the player assuming that
opponent is also playing optimally.
Mini-Max algorithm uses recursion to search through the
game-tree.
Min-Max algorithm is mostly used for game playing in AI.
Such as Chess, Checkers, tic-tac-toe, go, and various two-
players game. This Algorithm computes the minimax decision
for the current state.
17. In this algorithm two players play the game, one is called MAX
and other is called MIN.
Both the players fight it as the opponent player gets the
minimum benefit while they get the maximum benefit.
Both Players of the game are opponent of each other, where MAX
will select the maximized value and MIN will select the
minimized value.
The minimax algorithm performs a depth-first search algorithm
for the exploration of the complete game tree.
The minimax algorithm proceeds all the way down to the
terminal node of the tree, then backtrack the tree as the
recursion.
18. function MINIMAX-DECISION(state) returns an action
return argmax
a ∈ ACTIONS(s) MIN-VALUE(RESULT(state, a))
function MAX-VALUE(state) returns a utility value
if TERMINAL-TEST(state) then return UTILITY(state)
v ←−∞
for each a in ACTIONS(state) do
v ←MAX(v, MIN-VALUE(RESULT(s, a)))
return v
function MIN-VALUE(state) returns a utility value
if TERMINAL-TEST(state) then return UTILITY(state)
v←∞
for each a in ACTIONS(state) do
v ←MIN(v, MAX-VALUE(RESULT(s, a)))
return v
19. The main drawback of the minimax algorithm is that
it gets really slow for complex games such as Chess,
go, etc.
This type of games has a huge branching factor, and
the player has lots of choices to decide.
This limitation of the minimax algorithm can be
improved from alpha-beta pruning
20.
21.
22. •Alpha-beta pruning is a modified version of the
minimax algorithm.
• It is an optimization technique for the minimax
algorithm.
•game tree we can compute the correct minimax
decision, and this technique is called pruning.
23. The two-parameter can be defined as:
Alpha: The best (highest-value) choice we
have found so far at any point along the
path of Maximizer. The initial value of
alpha is -∞.
Beta: The best (lowest-value) choice we
have found so far at any point along the
path of Minimizer. The initial value of beta
is +∞.
24. Depth first search
only considers nodes along a single path from root at any time
a = highest-value choice found at any choice point of path for MAX
(initially, a = −infinity)
b = lowest-value choice found at any choice point of path for MIN
(initially, b = +infinity)
Pass current values of a and b down to child nodes during
search.
Update values of a and b during search:
MAX updates a at MAX nodes
MIN updates b at MIN nodes
Prune remaining branches at a node when a ≥ b
25. Prune whenever a ≥ b.
Prune below a Max node whose alpha value becomes greater
than or equal to the beta value of its ancestors.
Max nodes update alpha based on children’s returned
values.
Prune below a Min node whose beta value becomes less than or
equal to the alpha value of its ancestors.
Min nodes update beta based on children’s returned values.
26. a, b, initial values
Do DF-search until first leaf
a=−
b =+
a=−
b =+
a, b, passed to kids
39. Worst-Case
branches are ordered so that no pruning takes place. In this case
alpha-beta gives no improvement over exhaustive search
Best-Case
each player’s best move is the left-most child (i.e., evaluated first)
in practice, performance is closer to best rather than worst-case
E.g., sort moves by the remembered move values found last time.
E.g., expand captures first, then threats, then forward moves, etc.
E.g., run Iterative Deepening search, sort by value last iteration.
In practice often get O(b(d/2)) rather than O(bd)
this is the same as having a branching factor of sqrt(b),
(sqrt(b))d = b(d/2),i.e., we effectively go from b to square root of b
e.g., in chess go from b ~ 35 to b ~ 6
this permits much deeper search in the same amount of time
40. Pruning does not affect final results
Entire subtrees can be pruned.
Good move ordering improves effectiveness of pruning
Repeated states are again possible.
Store them in memory = transposition table
41.
42. Monte Carlo Tree Search (MCTS) is a search
technique in the field of Artificial Intelligence
(AI).
It is a probabilistic and heuristic driven search
algorithm that combines the classic tree
search implementations alongside machine
learning principles of reinforcement learning.
43. MCTS algorithm becomes useful as it
continues to evaluate other alternatives
periodically during the learning phase by
executing them, instead of the current
perceived optimal strategy. This is known as
the ” exploration-exploitation trade-off “.
Search can be broken down into four distinct steps, viz.,
1. selection,
2.expansion,
3.simulation and
4. backpropagation.
44.
45. •the MCTS algorithm traverses the current
tree from the root node using a specific
strategy.
•The strategy uses an evaluation function to
optimally select nodes with the highest
estimated value.
•MCTS uses the Upper Confidence Bound
(UCB) formula applied to trees as the
strategy in the selection process to traverse
the tree.
46. where;
Si = value of a node i
xi = empirical mean of a node i
C = a constant
t = total number of simulations
When traversing a tree during the selection
process, the child node that returns the greatest
value from the above equation will be one that
will get selected.
47. Expansion: In this process, a new child node is
added to the tree to that node which was optimally
reached during the selection process.
Simulation: In this process, a simulation is
performed by choosing moves or strategies until a
result or predefined state is achieved.
Backpropagation: After determining the value of
the newly added node, the remaining tree must be
updated. So, the backpropagation process is
performed, where it backpropagates from the new
node to the root node.
48.
49.
50. These types of algorithms are particularly useful
in turn based games where there is no element of
chance in the game mechanics, such as Tic Tac
Toe, Connect 4, Checkers, Chess, Go, etc.
51.
52.
53. Many games mirror this unpredictability by
including a random element, such as the
throwing of dice. We call these stochastic
games.
Backgammon is a typical game that combines luck and skill.
Dice are rolled at the beginning of a player’s turn to determine
the legal moves.
In the backgammon ,for example, White has rolled a 6–5 and has
four possible moves.
P(1,1)=1/36 (36 are ways to roll two dice.)
15 distinct roll each have 1/18 probability
54.
55.
56. Checkers:
Chinook ended 40-year-reign of human world champion Marion
Tinsley in 1994.
Chess:
Deep Blue defeated human world champion Garry Kasparov in a
six-game match in 1997.
Othello:
human champions refuse to compete against computers: they are
too good.
Go:
human champions refuse to compete against computers: they are
too bad
b > 300 (!)
See (e.g.) http://www.cs.ualberta.ca/~games/ for more information
57.
58. 1957: Herbert Simon
“within 10 years a computer will beat the world chess
champion”
1997: Deep Blue beats Kasparov
Parallel machine with 30 processors for “software” and 480 VLSI
processors for “hardware search”
Searched 126 million nodes per second on average
Generated up to 30 billion positions per move
Reached depth 14 routinely
Uses iterative-deepening alpha-beta search with transpositioning
Can explore beyond depth-limit for interesting moves
59.
60. Many problems in AI can be considered as problems
of constraint satisfaction, in which the goal state
satisfies a given set of constraint.
constraint satisfaction problems can be solved by
using any of the search strategies.
A constraint satisfaction problem (CSP) is
a problem that requires its solution to be within
some limitations or conditions, also known
as constraints, consisting of a finite variable set, a
domain set and a finite constraint set. ... The
optimal solution should satisfy all constraints.
61. 63
Variables WA, NT, Q, NSW, V, SA, T
Domains Di = {red,green,blue}
Constraints: adjacent regions must have different colors
e.g., WA ≠ NT
62. 64
Solutions are complete and consistent
assignments, e.g., WA = red, NT = green,Q =
red,NSW = green,V = red,SA = blue,T = green
63. 65
Binary CSP: each constraint relates two variables
Constraint graph: nodes are variables, arcs are
constraints
68. 70
General-purpose methods can give huge
gains in speed:
Which variable should be assigned next?
In what order should its values be tried?
Can we detect inevitable failure early?
69. 71
Most constrained variable:
choose the variable with the fewest legal values
a.k.a. minimum remaining values (MRV)
heuristic
Picks a variable which will cause failure as
soon as possible, allowing the tree to be
pruned.
70. 72
Tie-breaker among most constrained
variables
Most constraining variable:
choose the variable with the most constraints on
remaining variables (most edges in graph)
71. 73
Given a variable, choose the least
constraining value:
the one that rules out the fewest values in the
remaining variables
Leaves maximal flexibility for a solution.
Combining these heuristics makes 1000
queens feasible
72. 74
Idea:
Keep track of remaining legal values for unassigned
variables
Terminate search when any variable has no legal values
73. 75
Idea:
Keep track of remaining legal values for unassigned
variables
Terminate search when any variable has no legal values
74. 76
Idea:
Keep track of remaining legal values for unassigned
variables
Terminate search when any variable has no legal values
75. 77
Idea:
Keep track of remaining legal values for unassigned
variables
Terminate search when any variable has no legal values
76. 78
Forward checking propagates information from
assigned to unassigned variables, but doesn't
provide early detection for all failures:
NT and SA cannot both be blue!
Constraint propagation repeatedly enforces
constraints locally
77.
78. 80
Simplest form of propagation makes each arc
consistent
X Y is consistent iff
for every value x of X there is some allowed y
constraint propagation propagates arc consistency on the graph.
79. 81
Simplest form of propagation makes each arc
consistent
X Y is consistent iff
for every value x of X there is some allowed y
80. 82
Simplest form of propagation makes each arc
consistent
X Y is consistent iff
for every value x of X there is some allowed y
If X loses a value, neighbors of X need to be
rechecked
81. 83
Simplest form of propagation makes each arc consistent
X Y is consistent iff
for every value x of X there is some allowed y
If X loses a value, neighbors of X need to be rechecked
Arc consistency detects failure earlier than forward
checking
Can be run as a preprocessor or after each assignment
Time complexity: O(n2d3)
84. 86
Note: The path to the solution is unimportant, so
we can
apply local search!
To apply to CSPs:
allow states with unsatisfied constraints
operators reassign variable values
Variable selection: randomly select any
conflicted variable
Value selection by min-conflicts heuristic:
choose value that violates the fewest constraints
i.e., hill-climb with h(n) = total number of violated
constraints
85. Cryptarithmetic Problem is a type of
constraint satisfaction problem where the
game is about digits and its unique
replacement either with alphabets or other
symbols. In cryptarithmetic problem, the
digits (0-9) get substituted by some possible
alphabets or symbols.
86. The rules or constraints on a crypt arithmetic
problem are as follows:
There should be a unique digit to be replaced
with a unique alphabet.
The result should satisfy the predefined
arithmetic rules, i.e., 2+2 =4, nothing else.
Digits should be from 0-9 only.
There should be only one carry forward, while
performing the addition operation on a problem.
The problem can be solved from both sides,
i.e., lefthand side (L.H.S), or righthand side
(R.H.S)
87. Given a cryptarithmetic problem, i.e.,
Starting from the left hand side (L.H.S) , the terms
are S and M. Assign a digit which could give a
satisfactory result. Let’s assign S->9 and M->1.
88. Now, move ahead to the next
terms E and O to get N as its output
Adding E and O, which means 5+0=0, which is not possible
because according to cryptarithmetic constraints, we cannot assign the
same digit to two letters. So, we need to think more and assign some
other value.
89. Further, adding the next two
terms N and R we get,
But, we have already assigned E->5. Thus, the above result does not
satisfy the values
90. where 1 will be carry forward to the above
term
Let’s move ahead.
Again, on adding the last two terms, i.e., the
rightmost terms D and E, we get Y as its
result.
94. We decided to look at the value of O again.
If O = 0, then R would also be 0 so that doesn’t work
and O can’t be 1 because F = 1.
If O = 2,
TW2
+TW2
−−−−−−−
12UR
then R = 4 and T = 6 and we also know that W < 5
because there can’t be anything carried to the
hundreds column. The only possible value of W that
hasn’t already been used is 3 but this would mean
that U is 6 which is the same as T.
95. If O = 3,
TW3
+TW3
−−−−−−−
13UR
then R = 6 and T = 6 which doesn’t work.
96. If O = 4,
TW4
+TW4
−−−−−−−
14UR
then R = 8 and T = 7 and we also know that W < 5
because there can’t be anything carried to the
hundreds column. So W could be 0, 2 or 3.
W can’t be 0 because then U would be 0 and it
can’t be 2 because U would be 4.
If W = 3, U = 6 which works: 734 + 734 = 1468.
97. If O = 5,
TW5
+TW5−−−−−−−
15UR−−−−−−−
11
then R = 0 and T = 7 and we also know that W ≥ 5
because there has to be 1 carried to the
hundreds column.
W can’t be 5 because O = 5.
If W = 6, U = 3 which works: 765 + 765 = 1530.
98. So there are seven possible answers:
938+938=1876
928+928=1856
867+867=1734
846+846=1692
836+836=1672
765+765=1530
734+734=1468
99. Game playing is best modeled as a search problem
Game trees represent alternate computer/opponent moves
Evaluation functions estimate the quality of a given board
configuration for the Max player.
Minimax is a procedure which chooses moves by assuming that the
opponent will always choose the move which is best for them
Alpha-Beta is a procedure which can prune large parts of the search
tree and allow search to go deeper
For many well-known games, computer algorithms based on heuristic
search match or out-perform human world experts.
100. Comment on Backtracking and look ahead
strategies (forward)in constraint
satisfaction problems. [6]
Apply crypt arithmetic to solve the problem
and represent the state search space to solve
,TWO+TWO=FOUR (OCT2019)