Artificial Intelligence involves representing problems as state spaces and using algorithms to search the state space to solve the problem. The document discusses key concepts in problem solving using search including representing the problem as states, defining state transitions with successor functions, and exploring the resulting state space to find a solution. It provides examples of representing common problems like the 8-puzzle and n-queens as state spaces. The document also summarizes uninformed search strategies like breadth-first, depth-first, and iterative deepening search that use the problem definition to search the state space without using heuristics.
Problem solving
Problem formulation
Search Techniques for Artificial Intelligence
Classification of AI searching Strategies
What is Search strategy ?
Defining a Search Problem
State Space Graph versus Search Trees
Graph vs. Tree
Problem Solving by Search
Intelligent Agent PPT ON SLIDESHARE IN ARTIFICIAL INTELLIGENCEKhushboo Pal
n artificial intelligence, an intelligent agent (IA) is an autonomous entity which acts, directing its activity towards achieving goals (i.e. it is an agent), upon an environment using observation through sensors and consequent actuators (i.e. it is intelligent).An intelligent agent is a program that can make decisions or perform a service based on its environment, user input and experiences. These programs can be used to autonomously gather information on a regular, programmed schedule or when prompted by the user in real time. Intelligent agents may also be referred to as a bot, which is short for robot.Examples of intelligent agents
AI assistants, like Alexa and Siri, are examples of intelligent agents as they use sensors to perceive a request made by the user and the automatically collect data from the internet without the user's help. They can be used to gather information about its perceived environment such as weather and time.
Infogate is another example of an intelligent agent, which alerts users about news based on specified topics of interest.
Autonomous vehicles could also be considered intelligent agents as they use sensors, GPS and cameras to make reactive decisions based on the environment to maneuver through traffic.
Examples of intelligent agents
AI assistants, like Alexa and Siri, are examples of intelligent agents as they use sensors to perceive a request made by the user and the automatically collect data from the internet without the user's help. They can be used to gather information about its perceived environment such as weather and time.
Infogate is another example of an intelligent agent, which alerts users about news based on specified topics of interest.
Autonomous vehicles could also be considered intelligent agents as they use sensors, GPS and cameras to make reactive decisions based on the environment to maneuver through traffic.
A Heuristic is a technique to solve a problem faster than classic methods, or to find an approximate solution when classic methods cannot. This is a kind of a shortcut as we often trade one of optimality, completeness, accuracy, or precision for speed. A Heuristic (or a heuristic function) takes a look at search algorithms. At each branching step, it evaluates the available information and makes a decision on which branch to follow.
Problem solving
Problem formulation
Search Techniques for Artificial Intelligence
Classification of AI searching Strategies
What is Search strategy ?
Defining a Search Problem
State Space Graph versus Search Trees
Graph vs. Tree
Problem Solving by Search
Intelligent Agent PPT ON SLIDESHARE IN ARTIFICIAL INTELLIGENCEKhushboo Pal
n artificial intelligence, an intelligent agent (IA) is an autonomous entity which acts, directing its activity towards achieving goals (i.e. it is an agent), upon an environment using observation through sensors and consequent actuators (i.e. it is intelligent).An intelligent agent is a program that can make decisions or perform a service based on its environment, user input and experiences. These programs can be used to autonomously gather information on a regular, programmed schedule or when prompted by the user in real time. Intelligent agents may also be referred to as a bot, which is short for robot.Examples of intelligent agents
AI assistants, like Alexa and Siri, are examples of intelligent agents as they use sensors to perceive a request made by the user and the automatically collect data from the internet without the user's help. They can be used to gather information about its perceived environment such as weather and time.
Infogate is another example of an intelligent agent, which alerts users about news based on specified topics of interest.
Autonomous vehicles could also be considered intelligent agents as they use sensors, GPS and cameras to make reactive decisions based on the environment to maneuver through traffic.
Examples of intelligent agents
AI assistants, like Alexa and Siri, are examples of intelligent agents as they use sensors to perceive a request made by the user and the automatically collect data from the internet without the user's help. They can be used to gather information about its perceived environment such as weather and time.
Infogate is another example of an intelligent agent, which alerts users about news based on specified topics of interest.
Autonomous vehicles could also be considered intelligent agents as they use sensors, GPS and cameras to make reactive decisions based on the environment to maneuver through traffic.
A Heuristic is a technique to solve a problem faster than classic methods, or to find an approximate solution when classic methods cannot. This is a kind of a shortcut as we often trade one of optimality, completeness, accuracy, or precision for speed. A Heuristic (or a heuristic function) takes a look at search algorithms. At each branching step, it evaluates the available information and makes a decision on which branch to follow.
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
FellowBuddy.com is an innovative platform that brings students together to share notes, exam papers, study guides, project reports and presentation for upcoming exams.
We connect Students who have an understanding of course material with Students who need help.
Benefits:-
# Students can catch up on notes they missed because of an absence.
# Underachievers can find peer developed notes that break down lecture and study material in a way that they can understand
# Students can earn better grades, save time and study effectively
Our Vision & Mission – Simplifying Students Life
Our Belief – “The great breakthrough in your life comes when you realize it, that you can learn anything you need to learn; to accomplish any goal that you have set for yourself. This means there are no limits on what you can be, have or do.”
Like Us - https://www.facebook.com/FellowBuddycom
Problem Characteristics in Artificial Intelligence,
Unit -2 Problem Solving and Searching Techniques
o choose an appropriate method for a particular problem first we need to categorize the problem based on the following characteristics.
Is the problem decomposable into small sub-problems which are easy to solve?
Can solution steps be ignored or undone?
Is the universe of the problem is predictable?
Is a good solution to the problem is absolute or relative?
Is the solution to the problem a state or a path?
What is the role of knowledge in solving a problem using artificial intelligence?
Does the task of solving a problem require human interaction?
1. Is the problem decomposable into small sub-problems which are easy to solve?
Can the problem be broken down into smaller problems to be solved independently?
See also Water Jug Problem in Artificial Intelligence
The decomposable problem can be solved easily.
Example: In this case, the problem is divided into smaller problems. The smaller problems are solved independently. Finally, the result is merged to get the final result.
Is the problem decomposable
2. Can solution steps be ignored or undone?
In the Theorem Proving problem, a lemma that has been proved can be ignored for the next steps.
Such problems are called Ignorable problems.
In the 8-Puzzle, Moves can be undone and backtracked.
Such problems are called Recoverable problems.
In Playing Chess, moves can be retracted.
Such problems are called Irrecoverable problems.
Ignorable problems can be solved using a simple control structure that never backtracks. Recoverable problems can be solved using backtracking. Irrecoverable problems can be solved by recoverable style methods via planning.
3. Is the universe of the problem is predictable?
In Playing Bridge, We cannot know exactly where all the cards are or what the other players will do on their turns.
Uncertain outcome!
For certain-outcome problems, planning can be used to generate a sequence of operators that is guaranteed to lead to a solution.
For uncertain-outcome problems, a sequence of generated operators can only have a good probability of leading to a solution. Plan revision is made as the plan is carried out and the necessary feedback is provided.
4. Is a good solution to the problem is absolute or relative?
The Travelling Salesman Problem, we have to try all paths to find the shortest one.
See also Generate and Test Heuristic Search - Artificial Intelligence
Any path problem can be solved using heuristics that suggest good paths to explore.
For best-path problems, a much more exhaustive search will be performed.
5. Is the solution to the problem a state or a path
The Water Jug Problem, the path that leads to the goal must be reported.
In which we see how an agent can find a sequence of actions that achieves its goals, when no single action will do.
The method of solving problem through AI involves the process of defining the search space, deciding start and goal states and then finding the path from start state to goal state through search space.
State space search is a process used in the field of computer science, including artificial intelligence(AI), in which successive configurations or states of an instance are considered, with the goal of finding a goal state with a desired property.
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
UNIT - I PROBLEM SOLVING AGENTS and EXAMPLES.pptx.pdfJenishaR1
Replicate human intelligence
Solve Knowledge-intensive tasks
An intelligent connection of perception and action
Building a machine which can perform tasks that requires human intelligence such as:
Proving a theorem
Playing chess
Plan some surgical operation
Driving a car in traffic
Creating some system which can exhibit intelligent behavior, learn new things by itself, demonstrate, explain, and can advise to its user.
What Comprises to Artificial Intelligence?
Artificial Intelligence is not just a part of computer science even it's so vast and requires lots of other factors which can contribute to it. To create the AI first we should know that how intelligence is composed, so the Intelligence is an intangible part of our brain which is a combination of Reasoning, learning, problem-solving perception, language understanding, etc.
To achieve the above factors for a machine or software Artificial Intelligence requires the following discipline:
Mathematics
Biology
Psychology
Sociology
Computer Science
Neurons Study
Statistics Advantages of Artificial Intelligence
Following are some main advantages of Artificial Intelligence:
High Accuracy with less errors: AI machines or systems are prone to less errors and high accuracy as it takes decisions as per pre-experience or information.
High-Speed: AI systems can be of very high-speed and fast-decision making, because of that AI systems can beat a chess champion in the Chess game.
High reliability: AI machines are highly reliable and can perform the same action multiple times with high accuracy.
Useful for risky areas: AI machines can be helpful in situations such as defusing a bomb, exploring the ocean floor, where to employ a human can be risky.
Digital Assistant: AI can be very useful to provide digital assistant to the users such as AI technology is currently used by various E-commerce websites to show the products as per customer requirement.
Useful as a public utility: AI can be very useful for public utilities such as a self-driving car which can make our journey safer and hassle-free, facial recognition for security purpose, Natural language processing to communicate with the human in human-language, etc.
This presentation discusses the state space problem formulation and different search techniques to solve these. Techniques such as Breadth First, Depth First, Uniform Cost and A star algorithms are covered with examples. We also discuss where such techniques are useful and the limitations.
This Presentation discusses he following topics:
Introduction
Need for Problem formulation
Problem Solving Components
Definition of Problem
Problem Limitation
Goal or Solution
Solution Space
Operators
Examples of Problem Formulation
Well-defined Problems and Solution
Examples of Well-Defined Problems
Constraint satisfaction problems (CSPs)
Examples of constraint satisfaction problem
Decision problem
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
FellowBuddy.com is an innovative platform that brings students together to share notes, exam papers, study guides, project reports and presentation for upcoming exams.
We connect Students who have an understanding of course material with Students who need help.
Benefits:-
# Students can catch up on notes they missed because of an absence.
# Underachievers can find peer developed notes that break down lecture and study material in a way that they can understand
# Students can earn better grades, save time and study effectively
Our Vision & Mission – Simplifying Students Life
Our Belief – “The great breakthrough in your life comes when you realize it, that you can learn anything you need to learn; to accomplish any goal that you have set for yourself. This means there are no limits on what you can be, have or do.”
Like Us - https://www.facebook.com/FellowBuddycom
Problem Characteristics in Artificial Intelligence,
Unit -2 Problem Solving and Searching Techniques
o choose an appropriate method for a particular problem first we need to categorize the problem based on the following characteristics.
Is the problem decomposable into small sub-problems which are easy to solve?
Can solution steps be ignored or undone?
Is the universe of the problem is predictable?
Is a good solution to the problem is absolute or relative?
Is the solution to the problem a state or a path?
What is the role of knowledge in solving a problem using artificial intelligence?
Does the task of solving a problem require human interaction?
1. Is the problem decomposable into small sub-problems which are easy to solve?
Can the problem be broken down into smaller problems to be solved independently?
See also Water Jug Problem in Artificial Intelligence
The decomposable problem can be solved easily.
Example: In this case, the problem is divided into smaller problems. The smaller problems are solved independently. Finally, the result is merged to get the final result.
Is the problem decomposable
2. Can solution steps be ignored or undone?
In the Theorem Proving problem, a lemma that has been proved can be ignored for the next steps.
Such problems are called Ignorable problems.
In the 8-Puzzle, Moves can be undone and backtracked.
Such problems are called Recoverable problems.
In Playing Chess, moves can be retracted.
Such problems are called Irrecoverable problems.
Ignorable problems can be solved using a simple control structure that never backtracks. Recoverable problems can be solved using backtracking. Irrecoverable problems can be solved by recoverable style methods via planning.
3. Is the universe of the problem is predictable?
In Playing Bridge, We cannot know exactly where all the cards are or what the other players will do on their turns.
Uncertain outcome!
For certain-outcome problems, planning can be used to generate a sequence of operators that is guaranteed to lead to a solution.
For uncertain-outcome problems, a sequence of generated operators can only have a good probability of leading to a solution. Plan revision is made as the plan is carried out and the necessary feedback is provided.
4. Is a good solution to the problem is absolute or relative?
The Travelling Salesman Problem, we have to try all paths to find the shortest one.
See also Generate and Test Heuristic Search - Artificial Intelligence
Any path problem can be solved using heuristics that suggest good paths to explore.
For best-path problems, a much more exhaustive search will be performed.
5. Is the solution to the problem a state or a path
The Water Jug Problem, the path that leads to the goal must be reported.
In which we see how an agent can find a sequence of actions that achieves its goals, when no single action will do.
The method of solving problem through AI involves the process of defining the search space, deciding start and goal states and then finding the path from start state to goal state through search space.
State space search is a process used in the field of computer science, including artificial intelligence(AI), in which successive configurations or states of an instance are considered, with the goal of finding a goal state with a desired property.
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
UNIT - I PROBLEM SOLVING AGENTS and EXAMPLES.pptx.pdfJenishaR1
Replicate human intelligence
Solve Knowledge-intensive tasks
An intelligent connection of perception and action
Building a machine which can perform tasks that requires human intelligence such as:
Proving a theorem
Playing chess
Plan some surgical operation
Driving a car in traffic
Creating some system which can exhibit intelligent behavior, learn new things by itself, demonstrate, explain, and can advise to its user.
What Comprises to Artificial Intelligence?
Artificial Intelligence is not just a part of computer science even it's so vast and requires lots of other factors which can contribute to it. To create the AI first we should know that how intelligence is composed, so the Intelligence is an intangible part of our brain which is a combination of Reasoning, learning, problem-solving perception, language understanding, etc.
To achieve the above factors for a machine or software Artificial Intelligence requires the following discipline:
Mathematics
Biology
Psychology
Sociology
Computer Science
Neurons Study
Statistics Advantages of Artificial Intelligence
Following are some main advantages of Artificial Intelligence:
High Accuracy with less errors: AI machines or systems are prone to less errors and high accuracy as it takes decisions as per pre-experience or information.
High-Speed: AI systems can be of very high-speed and fast-decision making, because of that AI systems can beat a chess champion in the Chess game.
High reliability: AI machines are highly reliable and can perform the same action multiple times with high accuracy.
Useful for risky areas: AI machines can be helpful in situations such as defusing a bomb, exploring the ocean floor, where to employ a human can be risky.
Digital Assistant: AI can be very useful to provide digital assistant to the users such as AI technology is currently used by various E-commerce websites to show the products as per customer requirement.
Useful as a public utility: AI can be very useful for public utilities such as a self-driving car which can make our journey safer and hassle-free, facial recognition for security purpose, Natural language processing to communicate with the human in human-language, etc.
This presentation discusses the state space problem formulation and different search techniques to solve these. Techniques such as Breadth First, Depth First, Uniform Cost and A star algorithms are covered with examples. We also discuss where such techniques are useful and the limitations.
This Presentation discusses he following topics:
Introduction
Need for Problem formulation
Problem Solving Components
Definition of Problem
Problem Limitation
Goal or Solution
Solution Space
Operators
Examples of Problem Formulation
Well-defined Problems and Solution
Examples of Well-Defined Problems
Constraint satisfaction problems (CSPs)
Examples of constraint satisfaction problem
Decision problem
The first step in the problem space theory is to identify the problem. The problem cannot be solved if the initial problem is never identified or if the problem is incorrectly defined. This initial stage should focus on the problem itself and not the symptoms of the problem. Some methods involved in identifying and then defining the problem are:.........................
The purpose of the problem space is to identify the problem and develop steps to figure out the problem. This process can be used to help individuals work through problems. The problem space theory can also be used by businesses to enact ways to identify problem areas and make corrections. Businesses and individuals use the problem space to find answers to issues they encounter. Trial-and-error methods are a common practice in problem space theory. Problem space theory can be known as an inside-out solution where a breakdown of the process can solve the issue that a person or business may be facing.
Lecture slides by Mustafa Jarrar at Birzeit University, Palestine.
See the course webpage at: http://jarrar-courses.blogspot.com/2012/04/aai-spring-jan-may-2012.html and http://www.jarrar.info
The lecture covers: Un-informed Search
this is ppt on the topic of heuristic search techniques or we can also known it by the name of informed search techniques.
in this presentation we only disscuss about three search techniques there are lot of them by the most important once are in this presentation.
Problem-Solving Strategies in Artificial Intelligence" delves into the core techniques and methods employed by AI systems to address complex problems. This exploration covers the two main categories of search strategies: uninformed and informed, revealing how they navigate the solution space. It also investigates the use of heuristics, which provide a shortcut for guiding the search, and local search algorithms' role in tackling optimization problems. The description offers insights into the critical concepts and strategies that power AI's ability to find solutions efficiently and effectively in various domains.
In "Problem-Solving Strategies in Artificial Intelligence," we dive deeper into the foundational techniques and methodologies that AI systems rely on to tackle challenging problems. This comprehensive exploration begins with an in-depth examination of search strategies. Uninformed search strategies, often referred to as blind searches, are dissected, along with informed search strategies that harness domain-specific knowledge and heuristics to guide the search process more intelligently.
The role of heuristics in AI problem-solving is thoroughly investigated. These problem-solving techniques employ domain-specific rules of thumb to estimate the quality of potential solutions, aiding in decision-making and prioritization. The famous A* search algorithm, which combines actual cost and heuristic estimation, is highlighted as a prime example of informed search.
Local search algorithms, another critical component, are discussed in the context of optimization problems. These algorithms excel in finding the best solution within a local neighborhood of the current solution and are particularly valuable for various optimization challenges. You'll explore methods like hill climbing and simulated annealing, which are vital for optimizing solutions in constrained problem spaces.
This insightful exploration provides a comprehensive understanding of the problem-solving strategies employed in AI, offering a solid foundation for those seeking to apply AI techniques to real-world challenges and further the field of artificial intelligence.
Harnessing the power of citizen science for environmental stewardship and wat...Luigi Ceccaroni
Environmental degradation poses a significant challenge to Africa's sustainable development, demanding transformative approaches to conservation efforts.
The MoRe4nature project emerges as an opportunity, integrating citizen-science initiatives as key activities in environmental compliance assurance (ECA). This innovative approach empowers citizens to contribute meaningfully to sustainable natural-resource management, fostering a collaborative data and knowledge production platform, particularly in the realm of water monitoring and water literacy. MoRe4nature's socio-technical approach addresses the barriers to the uptake and utilisation of citizen-generated data in ECA, ensuring the long-term sustainability and impact of citizen science initiatives in Africa. Specifically, MoRe4nature will work with 40 cases across Europe, Latin America, Asia and Africa, including two FreshWater Watch cases in Sierra Leone and Zambia.
FreshWater Watch in Africa (FWW), an exemplary citizen science initiative, empowers communities in Africa to monitor the health of their precious freshwater resources, providing valuable data for water quality assessments and environmental management. By harnessing the power of citizen science, FWW directly contributes to the achievement of the UN's Sustainable Development Goals, promoting access to safe water and sanitation for all. FWW is currently working with partners in Zambia, Sierra Leone, South Africa, Tanzania and Kenya and is looking to support work in other African countries in the future.
The ProBleu project complements MoRe4nature's and FWW’s efforts by fostering ocean and water literacy among students and teachers across and beyond Europe, including Africa. Through a comprehensive set of activities, the ProBleu project promotes ocean and water literacy, engages students in real-world ocean and water research, and enhances the sense of stewardship towards the value and challenges of oceans and waters. This initiative empowers individuals and schools to become active advocates for environmental protection and water literacy, influencing policy decisions and driving sustainable practices at local and national levels.
By strengthening existing citizen science, fostering collaboration and partnerships, synergising citizen science with living labs and fab labs, and developing data validation tools, MoRe4nature, ProBleu and FWW empower citizens to become active partners in environmental protection and water literacy, safeguarding our planet for generations to come.
Citizen science, training, data quality and interoperabilityLuigi Ceccaroni
Citizen science, training, data quality and interoperability
More and more people are interested in participating in citizen-science projects, and the technology is becoming more accessible.
Data quality is essential for citizen-science projects. Without quantified-quality data, the results of citizen science projects cannot be trusted.
There are several challenges to ensuring data quality in citizen-science projects, such as participant motivation and training, data-entry errors, and environmental factors.
These challenges can be addressed by using innovative technologies, such as artificial intelligence, and by developing better training methods.
Mobile devices are becoming increasingly powerful and sophisticated, and they are making it easier for participants to collect data anywhere and anytime.
Artificial intelligence is being used to develop new tools that can automatically analyse data and identify patterns. This makes it easier to identify and correct data errors.
Online communities are providing a space for citizen scientists to connect with each other, share data, and learn from each other. This is helping to improve the quality of data collected by citizen-science projects.
Citizen-science projects are increasingly aware of the importance of data ethics. This is leading to the development of new standards and guidelines for collecting and using citizen science data.
Metrics and instruments to evaluate the impacts of citizen scienceLuigi Ceccaroni
MICS project: Developing metrics and instruments to evaluate the impacts of citizen science on society, governance, the economy, the environment, and science
Citclops/EyeOnWater @ Barcelona - Citizen science day 2016Luigi Ceccaroni
Citclops/EyeOnWater @ Vendée Globe is a cooperation among organizations involved in the European project Citclops, organizations involved in the Vendée Globe sailing race, skippers, scientists, and citizens, to observe the color of the ocean on the path of the Vendée Globe. It uses tools and sensors developed by the European-Commission–funded project Citclops (Citizens’ Observatory for Coast and Ocean Optical Monitoring), which introduced an innovative concept for water-quality monitoring, to help oceanographers and limnologists in monitoring natural waters, with a strong focus on long-term data series related to environmental sciences.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
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.
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
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
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.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
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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/
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2. Problem solving
• We want:
– To automatically solve a problem
• We need:
– A representation of the problem
– Algorithms that use some strategy to solve
the problem defined in that representation
3. Problem representation
• General:
– State space: a problem is divided into a set
of resolution steps from the initial state to the
goal state
– Reduction to sub-problems: a problem is
arranged into a hierarchy of sub-problems
• Specific:
– Game resolution
– Constraints satisfaction
4. States
• A problem is defined by its elements and their
relations.
• In each instant of the resolution of a problem,
those elements have specific descriptors (How
to select them?) and relations.
• A state is a representation of those elements in
a given moment.
• Two special states are defined:
– Initial state (starting point)
– Final state (goal state)
5. State modification:
successor function
• A successor function is needed to move
between different states.
• A successor function is a description of
possible actions, a set of operators. It is a
transformation function on a state
representation, which convert it into another
state.
• The successor function defines a relation of
accessibility among states.
• Representation of the successor function:
– Conditions of applicability
– Transformation function
6. State space
• The state space is the set of all states
reachable from the initial state.
• It forms a graph (or map) in which the nodes are
states and the arcs between nodes are actions.
• A path in the state space is a sequence of
states connected by a sequence of actions.
• The solution of the problem is part of the map
formed by the state space.
7. Problem solution
• A solution in the state space is a path from the
initial state to a goal state or, sometimes, just a
goal state.
• Path/solution cost: function that assigns a
numeric cost to each path, the cost of applying
the operators to the states
• Solution quality is measured by the path cost
function, and an optimal solution has the
lowest path cost among all solutions.
• Solutions: any, an optimal one, all. Cost is
important depending on the problem and the
type of solution sought.
8. Problem description
• Components:
– State space (explicitly or implicitly defined)
– Initial state
– Goal state (or the conditions it has to fulfill)
– Available actions (operators to change state)
– Restrictions (e.g., cost)
– Elements of the domain which are relevant to the
problem (e.g., incomplete knowledge of the starting
point)
– Type of solution:
• Sequence of operators or goal state
• Any, an optimal one (cost definition needed), all
10. Example: 8-puzzle
• State space: configuration of the eight
tiles on the board
• Initial state: any configuration
• Goal state: tiles in a specific order
• Operators or actions: “blank moves”
– Condition: the move is within the board
– Transformation: blank moves Left, Right, Up,
or Down
• Solution: optimal sequence of operators
12. Example: n queens
(n = 4, n = 8)
• State space: configurations from 0 to n queens
on the board with only one queen per row and
column
• Initial state: configuration without queens on the
board
• Goal state: configuration with n queens such that
no queen attacks any other
• Operators or actions: place a queen on the
board
Condition: the new queen is not attacked by any
other already placed
Transformation: place a new queen in a particular
square of the board
• Solution: one solution (cost is not considered)
13. Structure of the state space
• Data structures:
– Trees: only one path to a given node
– Graphs: several paths to a given node
• Operators: directed arcs between nodes
• The search process explores the state
space.
• In the worst case all possible paths
between the initial state and the goal state
are explored.
14. Search as goal satisfaction
• Satisfying a goal
– Agent knows what the goal is
– Agent cannot evaluate intermediate solutions
(uninformed)
– The environment is:
• Static
• Observable
• Deterministic
15. Example: holiday in Romania
• On holiday in Romania; currently in Arad
• Flight leaves tomorrow from Bucharest at
13:00
• Let’s configure this to be an AI problem
16. Romania
• What’s the problem?
– Accomplish a goal
• Reach Bucharest by 13:00
• So this is a goal-based problem
17. Romania
• What’s an example of a non-goal-based
problem?
– Live long and prosper
– Maximize the happiness of your trip to
Romania
– Don’t get hurt too much
18. Romania
• What qualifies as a solution?
– You can/cannot reach Bucharest by 13:00
– The actions one takes to travel from Arad to
Bucharest along the shortest (in time) path
20. More concrete problem
definition
Which cities could you be
A state space in?
Which city do you start
An initial state from?
Which city do you aim to
A goal state reach?
When in city foo, the
A function defining state following cities can be
transitions reached
A function defining the How long does it take to
“cost” of a state travel through a city
sequence?
sequence
21. More concrete problem
definition
A state space Choose a representation
Choose an element from the
An initial state representation
Create goal_function(state) such
A goal state that TRUE is returned upon reaching
goal
successor_function(statei) =
A function defining state
{<actiona, statea>, <actionb, stateb>,
transitions …}
A function defining the “cost” of a
cost (sequence) = number
state sequence
22. Important notes about this
example
– Static environment (available states,
successor function, and cost functions don’t
change)
– Observable (the agent knows where it is)
– Discrete (the actions are discrete)
– Deterministic (successor function is always
the same)
23. Tree search algorithms
• Basic idea:
– Simulated
exploration of
state space by
generating
successors of
already explored
states (AKA
expanding
states) Sweep out from start (breadth)
24. Tree search algorithms
• Basic idea:
– Simulated
exploration of
state space by
generating
successors of
already explored
states (AKA
expanding Go East, young man! (depth)
states)
25. Implementation: general search
algorithm
Algorithm General Search
Open_states.insert (Initial_state)
Current= Open_states.first()
while not is_final?(Current) and not Open_states.empty?() do
Open_states.delete_first()
Closed_states.insert(Current)
Successors= generate_successors(Current)
Successors= process_repeated(Successors, Closed_states,
Open_states)
Open_states.insert(Successors)
Current= Open_states.first()
eWhile
eAlgorithm
30. Example: Arad Bucharest
while not is_final?(Current) and not Open_states.empty?() do
Open_states.delete_first()
Closed_states.insert(Current)
Successors= generate_successors(Current)
Successors= process_repeated(Successors, Closed_states,
Open_states)
Open_states.insert(Successors)
Arad
Zerind (75) Timisoara (118) Sibiu (140)
Dradea (151) Faragas (99) Rimnicu Vilcea (80)
31. Implementation: states vs.
nodes
• State
– (Representation of) a physical configuration
• Node
– Data structure constituting part of a search
tree
• Includes parent, children, depth, path cost g(x)
• States do not have parents, children,
depth, or path cost!
32. Search strategies
• A strategy is defined by picking the order of node
expansion
• Strategies are evaluated along the following
dimensions:
– Completeness – does it always find a solution if
one exists?
– Time complexity – number of nodes
generated/expanded
– Space complexity – maximum nodes in memory
– Optimality – does it always find a least-cost
solution?
33. Search strategies
• Time and space complexity are measured in
terms of:
– b – maximum branching factor of the search tree
(may be infinite)
– d – depth of the least-cost solution
– m – maximum depth of the state space (may be
infinite)
34. Uninformed Search Strategies
• Uninformed strategies use only the
information available in the problem
definition
– Breadth-first search
– Uniform-cost search
– Depth-first search
– Depth-limited search
– Iterative deepening search
35. Nodes
• Open nodes:
– Generated, but not yet explored
– Explored, but not yet expanded
• Closed nodes:
– Explored and expanded
35
36. Breadth-first search
• Expand shallowest unexpanded node
• Implementation:
– A FIFO queue, i.e., new successors go at end
37. Space cost of BFS
• Because you must be able to generate the path upon
finding the goal state, all visited nodes must be stored
• O (bd+1)
38. Properties of breadth-first
search
• Complete?
– Yes (if b (max branch factor) is finite)
• Time?
– 1 + b + b2 + … + bd + b(bd-1) = O(bd+1), i.e., exponential in d
• Space?
– O(bd+1) (keeps every node in memory)
• Optimal?
– Only if cost = 1 per step, otherwise not optimal in general
• Space is the big problem; it can easily generate nodes at
10 MB/s, so 24 hrs = 860GB!
40. Depth-first search
• Complete?
– No: fails in infinite-depth spaces, spaces with loops.
– Can be modified to avoid repeated states along path
complete in finite spaces
• Time?
– O(bm): terrible if m is much larger than d, but if solutions are
dense, may be much faster than breadth-first
• Space?
– O(bm), i.e., linear space!
• Optimal?
– No
41. Depth-limited search
• It is depth-first search with an imposed
limit on the depth of exploration, to
guarantee that the algorithm ends.
41
42. Treatment of repeated states
• Breadth-first:
– If the repeated state is in the structure of closed or open
nodes, the actual path has equal or greater depth than the
repeated state and can be forgotten.
42
43. Treatment of repeated states
• Depth-first:
– If the repeated state is in the structure of closed nodes, the
actual path is kept if its depth is less than the repeated state.
– If the repeated state is in the structure of open nodes, the
actual path has always greater depth than the repeated state
and can be forgotten.
43
45. Iterative deepening search
• The algorithm consists of iterative, depth-first
searches, with a maximum depth that increases at
each iteration. Maximum depth at the beginning is 1.
• Behavior similar to BFS, but without the spatial
complexity.
• Only the actual path is kept in memory; nodes are
regenerated at each iteration.
• DFS problems related to infinite branches are
avoided.
• To guarantee that the algorithm ends if there is no
solution, a general maximum depth of exploration can
be defined. 45
47. Summary
– All uninformed searching techniques are more alike
than different.
– Breadth-first has space issues, and possibly optimality
issues.
– Depth-first has time and optimality issues, and possibly
completeness issues.
– Depth-limited search has optimality and completeness
issues.
– Iterative deepening is the best uninformed search we
have explored.
48. Uninformed vs. informed
• Blind (or uninformed) search algorithms:
– Solution cost is not taken into account.
• Heuristic (or informed) search algorithms:
– A solution cost estimation is used to guide the
search.
– The optimal solution, or even a solution, are
not guaranteed.
48