As presented at DevDuck #5 - JavaScript meetup for developers (www.devduck.pl)
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Read more about Heuristic algorithms & Swarm intelligence
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Looking for a company to build you an electron desktop app? www.brainhub.eu
The document proposes developing an artificial intelligence-based stock trading system using particle swarm optimization. It finds that using 30 neural networks with a 100-day moving time interval to select the top 3 stock picks daily based on the highest recommendations produces the most stable and profitable results. The system uses swarm intelligence to search for the globally best-performing neural network each day to make trading decisions.
This document discusses swarm intelligence, which is inspired by natural phenomena like bird flocking and ant foraging behavior. It describes how swarms exhibit complex behavior through individuals following simple rules without centralized control. Two swarm algorithms are covered: ant colony optimization, which is applied to the traveling salesman problem, and the bee algorithm. The document compares these algorithms and discusses applications of swarm intelligence concepts.
The document discusses swarm intelligence and several algorithms inspired by it, including ant colony optimization, particle swarm optimization, and stochastic diffusion search. It provides examples of how each algorithm works, modeling the decentralized and self-organized behavior of swarms in nature. It also mentions related metaheuristic optimization techniques like genetic algorithms, simulated annealing, and tabu search.
Swarm intelligence refers to the collective behavior that emerges from decentralized, self-organized systems, both natural and artificial. In nature, it can be seen in the ability of ant colonies and bird flocks to coordinate and complete tasks through simple local interactions between individuals. Artificial swarm intelligence systems are distributed systems of interacting autonomous agents that coordinate through self-organization to solve problems through cooperation and division of labor. Examples of algorithms inspired by swarm intelligence include ant colony optimization and particle swarm optimization.
Swarm intelligence is the collective behavior of decentralized, self-organized systems, whether natural or artificial. It is used in artificial intelligence research. A swarm of robots could work similarly to an ant colony, with each following simple rules leading to self-organization and task completion without direct communication. Researchers have used swarms of simple robots to spell words and play piano through position-based algorithms. Swarm intelligence is also applied to fields like robotics, staff scheduling, and entertainment.
This Presentation were Made By BugsBusters team from faculty of Computers and information, Helwan University - Egypt
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Swarm intelligence is an artificial intelligence technique inspired by the collective behavior of decentralized and self-organized systems found in nature, such as ant colonies and bird flocks. Two common swarm intelligence algorithms are ant colony optimization and particle swarm optimization. Ant colony optimization is based on the behavior of real ant colonies and can be used to find approximate solutions to difficult optimization problems. Particle swarm optimization is a population-based stochastic optimization technique inspired by swarming behavior in nature, such as bird flocking. It searches for optimal solutions within a problem space by updating the movement of individual particles based on their own experiences and those of neighboring particles.
This document summarizes a lecture on multi-robot systems. It discusses why multi-robot systems are used, including for robustness, scalability, performance, and specialization. It covers reactive coordination algorithms inspired by ant colonies, which use indirect communication via pheromone trails. It also discusses deliberative coordination through the example of yacht racing crews. Key lessons are that multi-robot systems distribute sensing, computation and communication, and coordination algorithms are probabilistic approaches based on available capabilities.
Swarm intelligence is inspired by the collective behavior of social insects like ants and bees. It involves the decentralized control of groups of simple agents interacting locally with each other and their environment. Some key points covered:
- Particle swarm optimization and ant colony optimization are two popular swarm intelligence algorithms. They are inspired by bird flocking and ant foraging behavior respectively.
- PSO mimics social interaction to optimize problems. Each agent updates its position based on its own experience and neighboring agents. ACO uses artificial ants depositing pheromones to probabilistically construct solutions.
- Swarm intelligence systems are robust, relatively simple, and can achieve complex functions through self-organization and emergence. They have applications in routing
Swarm robotics is an approach to coordinating multi-robot systems consisting of large numbers of simple physical robots. It is based on swarm intelligence, which models the collective behavior of decentralized, self-organized systems found in nature. Key aspects of swarm robotics include agents that interact with each other and their environment based on simple rules, exhibiting emergent intelligent group behavior. Common swarm intelligence algorithms like ant colony optimization and particle swarm optimization have been applied to optimization problems.
Inspired from social behavior of insects and other creatures, artificial intelligence community has learned a series of techniques commonly known as Swarm Intelligence. Here we give a 15 minute introduction to this area.
This document discusses swarm intelligence, which is an artificial intelligence technique inspired by the collective behavior of decentralized, self-organized systems found in nature, such as bird flocking, ant colonies, bee swarms, and fish schooling. The key principles of swarm intelligence are that there is no central control, agents follow simple rules, and emergent intelligence arises from the interactions between agents. Two commonly used swarm intelligence algorithms are ant colony optimization, inspired by how ants find food sources, and particle swarm optimization, inspired by the flocking behavior of birds. Swarm intelligence techniques have various applications in areas like robotics, engineering, telecommunications, and more.
Web mining involves discovering useful information from web data through three main types: web structure mining analyzes hyperlink structures between pages, web content mining extracts information from page contents, and web usage mining analyzes patterns from user interactions on websites. Swarm intelligence is used to model collective behavior of decentralized self-organized systems and has been applied to problems like routing, robotics, and optimization through approaches like ant colony optimization and particle swarm optimization that are inspired by behaviors of social insects like ants and birds.
Swarm intelligence is a modern artificial intelligence discipline that is concerned with the design of multiagent systems with applications, e.g., in optimization and in robotics. The design paradigm for these systems is fundamentally different from more traditional approaches.
Swarm intelligence (SI) is the collective behavior of decentralized, self-organized systems, natural or artificial. The concept is employed in work on artificial intelligence. The expression was introduced by Gerardo Beni and Jing Wang in 1989, in the context of cellular robotic systems.
This document provides an overview of swarm robotics. It begins with examples of decentralized control and self-organization in natural swarms like ants and bees. It then discusses how swarm robotics takes inspiration from these systems, using local control methods, local communication, and self-organization to complete collective tasks without centralized control. The rest of the document focuses on a proposed system for gesture recognition to allow human control of swarm robots. It describes hand detection, feature extraction, and hardware implementation using three foot-bot robots. It concludes with potential applications of swarm robotics and areas for future work.
Jyotishkar dey roll 36.(swarm intelligence)Jyotishkar Dey
Swarm intelligence is inspired by collective behavior of social insects like ants and bees. It involves developing algorithms for problem solving using decentralized and self-organized agents. Some examples of swarm intelligence include ant colony optimization and bee algorithms. Ant colony optimization works by simulating the pheromone trails left by ants to find shortest paths. The bee algorithm is based on how bees communicate through waggle dances to efficiently locate pollen sources. Swarm intelligence has applications in robotics, communication networks, and mobile ad-hoc networks. While it offers advantages like scalability and robustness, it also has disadvantages such as unknown convergence times and potential for stagnation.
Swarm intelligence is the discipline that deals with natural and artificial systems composed of many individuals that coordinate using decentralized control and self-organization. In particular, the discipline focuses on the collective behaviors that result from the local interactions of the individuals with each other and with their environment. Examples of systems studied by swarm intelligence are colonies of ants and termites, schools of fish, flocks of birds, herds of land animals. Some human artifacts also fall into the domain of swarm intelligence, notably some multi-robot systems, and also certain computer programs that are written to tackle optimization and data analysis problems.
Taxonomy of Swarm Intelligence
Swarm intelligence has a marked multidisciplinary character since systems with the above mentioned characteristics can be observed in a variety of domains. Research in swarm intelligence can be classified according to different criteria.
Natural vs. Artificial: It is customary to divide swarm intelligence research into two areas according to the nature of the systems under analysis. We speak therefore of natural swarm intelligence research, where biological systems are studied; and of artificial swarm intelligence, where human artifacts are studied.
Scientific vs. Engineering: An alternative and somehow more informative classification of swarm intelligence research can be given based on the goals that are pursued: we can identify a scientific and an engineering stream. The goal of the scientific stream is to model swarm intelligence systems and to single out and understand the mechanisms that allow a system as a whole to behave in a coordinated way as a result of local individual-individual and individual-environment interactions. On the other hand, the goal of the engineering stream is to exploit the understanding developed by the scientific stream in order to design systems that are able to solve problems of practical relevance.
The two dichotomies natural/artificial and scientific/engineering are orthogonal: although the typical scientific investigation concerns natural systems and the typical engineering application concerns the development of an artificial system, a number of swarm intelligence.Natural/Scientific: Foraging Behavior of Ants
In a now classic experiment done in 1990, Deneubourg and his group showed that, when given the choice between two paths of different length joining the nest to a food source, a colony of ants has a high probability to collectively choose the shorter one. Deneubourg has shown that this behavior can be explained via a simple probabilistic model in which each ant decides where to go by taking random decisions based on the intensity of pheromone perceived on the ground, the pheromone being deposited by the ants while moving from the nest to the food source and back.
Artificial/Scientific: Clustering by a Swarm of Robots
Several ant species cluster corpses to form cemeteries.
The document discusses swarm robots, including different types defined by size, communication range and topology. It describes how swarm robotics uses large numbers of simple robots that can collectively solve problems through local interactions. Examples of tasks include transportation, search and rescue. Challenges include scalability and performing physical tasks. Modeling approaches include microscopic and macroscopic, with the latter being more computationally efficient.
This document discusses swarm intelligence and how it can be used to design algorithms. It provides examples of how ants exhibit swarm intelligence through their collective foraging behaviors without centralized control. Specifically, it mentions how ant colony algorithms have been designed and applied to solve optimization problems like the traveling salesman problem by simulating the indirect communication of ants through pheromone trails. The document also notes some potential applications of swarm intelligence in robotics and communication networks.
This document discusses communication techniques in swarm robotics. It summarizes three studies on this topic. The first two studies found that using chemical pheromones for communication between swarm robots resulted in better performance than not using pheromones. The third study used a heterogeneous swarm of footbots and eyebots that communicated via telecommunication; this approach worked well due to synchronization between the bot types. In conclusion, chemical communication works best due to the self-learning nature of the robots, but telecommunication can also be effective if different robot types are synchronized properly. Future work could explore new types of chemical trails that are not limited to land or use combinations of communication approaches.
The document describes a project that aims to develop an automated system for coordinating robots working in a swarm environment. The system uses image processing to identify nearby robots by scanning QR codes on each robot and adjusting schedules to avoid collisions without human intervention. It discusses modeling swarm robotics and compares it to other multi-agent systems. The goals are to enhance efficiency of swarms through automatic coordination and reduce human errors. Applications include defence operations, sensitive tasks requiring coordination, and medical fields.
Framsticks is a system for simulating and evolving 3D creatures made of sticks. Creatures have a physical structure and neural network that are both described in their genotype. The system uses collision detection, sensors, effectors, and an evolutionary process of selection, mutation and crossover to evolve creatures. Framsticks has been used to simulate swimming and walking creatures and in film and game development. An example shows a evolved swimming creature with muscles, gyroscopes and neurons that controls its motion through feedback and bending signals.
Artificial Intelligence Today (22 June 2017)Sabri Sansoy
This was a top level presentation on some of the 30+ subcategories of Artificial Intelligence at the Hackaday LA June Meetup - Wheels, Wings, and Walkers. Sponsored by SupplyFrame Design Labs in Pasadena CA
A brief Introduction to AI and its applications in Gaming. Talk was at "Advances & Research Challenges in the Applications of AI in Gaming, Medical Imaging and Bio-Informatics"
This document provides an introduction to the CS 188: Artificial Intelligence course at UC Berkeley. It discusses key topics that will be covered in the course, including rational decision making, computational rationality, a brief history of AI, current capabilities in areas like natural language processing, computer vision, robotics, and game playing. The course will cover general techniques for designing rational agents and making decisions under uncertainty, with applications to domains like language, vision, games, and more. Students will learn how to apply existing AI techniques to new problem types.
The document discusses recommender systems and scaling machine learning models using Apache Spark. It introduces recommender systems and collaborative filtering using matrix factorization. It then explains how to implement alternating least squares in Spark to scale recommender systems. The document provides code examples in Python using Spark and the MovieLens dataset to demonstrate an alternating least squares model for movie recommendations.
Swarm intelligence is an artificial intelligence technique inspired by the collective behavior of decentralized and self-organized systems found in nature, such as ant colonies and bird flocks. Two common swarm intelligence algorithms are ant colony optimization and particle swarm optimization. Ant colony optimization is based on the behavior of real ant colonies and can be used to find approximate solutions to difficult optimization problems. Particle swarm optimization is a population-based stochastic optimization technique inspired by swarming behavior in nature, such as bird flocking. It searches for optimal solutions within a problem space by updating the movement of individual particles based on their own experiences and those of neighboring particles.
This document summarizes a lecture on multi-robot systems. It discusses why multi-robot systems are used, including for robustness, scalability, performance, and specialization. It covers reactive coordination algorithms inspired by ant colonies, which use indirect communication via pheromone trails. It also discusses deliberative coordination through the example of yacht racing crews. Key lessons are that multi-robot systems distribute sensing, computation and communication, and coordination algorithms are probabilistic approaches based on available capabilities.
Swarm intelligence is inspired by the collective behavior of social insects like ants and bees. It involves the decentralized control of groups of simple agents interacting locally with each other and their environment. Some key points covered:
- Particle swarm optimization and ant colony optimization are two popular swarm intelligence algorithms. They are inspired by bird flocking and ant foraging behavior respectively.
- PSO mimics social interaction to optimize problems. Each agent updates its position based on its own experience and neighboring agents. ACO uses artificial ants depositing pheromones to probabilistically construct solutions.
- Swarm intelligence systems are robust, relatively simple, and can achieve complex functions through self-organization and emergence. They have applications in routing
Swarm robotics is an approach to coordinating multi-robot systems consisting of large numbers of simple physical robots. It is based on swarm intelligence, which models the collective behavior of decentralized, self-organized systems found in nature. Key aspects of swarm robotics include agents that interact with each other and their environment based on simple rules, exhibiting emergent intelligent group behavior. Common swarm intelligence algorithms like ant colony optimization and particle swarm optimization have been applied to optimization problems.
Inspired from social behavior of insects and other creatures, artificial intelligence community has learned a series of techniques commonly known as Swarm Intelligence. Here we give a 15 minute introduction to this area.
This document discusses swarm intelligence, which is an artificial intelligence technique inspired by the collective behavior of decentralized, self-organized systems found in nature, such as bird flocking, ant colonies, bee swarms, and fish schooling. The key principles of swarm intelligence are that there is no central control, agents follow simple rules, and emergent intelligence arises from the interactions between agents. Two commonly used swarm intelligence algorithms are ant colony optimization, inspired by how ants find food sources, and particle swarm optimization, inspired by the flocking behavior of birds. Swarm intelligence techniques have various applications in areas like robotics, engineering, telecommunications, and more.
Web mining involves discovering useful information from web data through three main types: web structure mining analyzes hyperlink structures between pages, web content mining extracts information from page contents, and web usage mining analyzes patterns from user interactions on websites. Swarm intelligence is used to model collective behavior of decentralized self-organized systems and has been applied to problems like routing, robotics, and optimization through approaches like ant colony optimization and particle swarm optimization that are inspired by behaviors of social insects like ants and birds.
Swarm intelligence is a modern artificial intelligence discipline that is concerned with the design of multiagent systems with applications, e.g., in optimization and in robotics. The design paradigm for these systems is fundamentally different from more traditional approaches.
Swarm intelligence (SI) is the collective behavior of decentralized, self-organized systems, natural or artificial. The concept is employed in work on artificial intelligence. The expression was introduced by Gerardo Beni and Jing Wang in 1989, in the context of cellular robotic systems.
This document provides an overview of swarm robotics. It begins with examples of decentralized control and self-organization in natural swarms like ants and bees. It then discusses how swarm robotics takes inspiration from these systems, using local control methods, local communication, and self-organization to complete collective tasks without centralized control. The rest of the document focuses on a proposed system for gesture recognition to allow human control of swarm robots. It describes hand detection, feature extraction, and hardware implementation using three foot-bot robots. It concludes with potential applications of swarm robotics and areas for future work.
Jyotishkar dey roll 36.(swarm intelligence)Jyotishkar Dey
Swarm intelligence is inspired by collective behavior of social insects like ants and bees. It involves developing algorithms for problem solving using decentralized and self-organized agents. Some examples of swarm intelligence include ant colony optimization and bee algorithms. Ant colony optimization works by simulating the pheromone trails left by ants to find shortest paths. The bee algorithm is based on how bees communicate through waggle dances to efficiently locate pollen sources. Swarm intelligence has applications in robotics, communication networks, and mobile ad-hoc networks. While it offers advantages like scalability and robustness, it also has disadvantages such as unknown convergence times and potential for stagnation.
Swarm intelligence is the discipline that deals with natural and artificial systems composed of many individuals that coordinate using decentralized control and self-organization. In particular, the discipline focuses on the collective behaviors that result from the local interactions of the individuals with each other and with their environment. Examples of systems studied by swarm intelligence are colonies of ants and termites, schools of fish, flocks of birds, herds of land animals. Some human artifacts also fall into the domain of swarm intelligence, notably some multi-robot systems, and also certain computer programs that are written to tackle optimization and data analysis problems.
Taxonomy of Swarm Intelligence
Swarm intelligence has a marked multidisciplinary character since systems with the above mentioned characteristics can be observed in a variety of domains. Research in swarm intelligence can be classified according to different criteria.
Natural vs. Artificial: It is customary to divide swarm intelligence research into two areas according to the nature of the systems under analysis. We speak therefore of natural swarm intelligence research, where biological systems are studied; and of artificial swarm intelligence, where human artifacts are studied.
Scientific vs. Engineering: An alternative and somehow more informative classification of swarm intelligence research can be given based on the goals that are pursued: we can identify a scientific and an engineering stream. The goal of the scientific stream is to model swarm intelligence systems and to single out and understand the mechanisms that allow a system as a whole to behave in a coordinated way as a result of local individual-individual and individual-environment interactions. On the other hand, the goal of the engineering stream is to exploit the understanding developed by the scientific stream in order to design systems that are able to solve problems of practical relevance.
The two dichotomies natural/artificial and scientific/engineering are orthogonal: although the typical scientific investigation concerns natural systems and the typical engineering application concerns the development of an artificial system, a number of swarm intelligence.Natural/Scientific: Foraging Behavior of Ants
In a now classic experiment done in 1990, Deneubourg and his group showed that, when given the choice between two paths of different length joining the nest to a food source, a colony of ants has a high probability to collectively choose the shorter one. Deneubourg has shown that this behavior can be explained via a simple probabilistic model in which each ant decides where to go by taking random decisions based on the intensity of pheromone perceived on the ground, the pheromone being deposited by the ants while moving from the nest to the food source and back.
Artificial/Scientific: Clustering by a Swarm of Robots
Several ant species cluster corpses to form cemeteries.
The document discusses swarm robots, including different types defined by size, communication range and topology. It describes how swarm robotics uses large numbers of simple robots that can collectively solve problems through local interactions. Examples of tasks include transportation, search and rescue. Challenges include scalability and performing physical tasks. Modeling approaches include microscopic and macroscopic, with the latter being more computationally efficient.
This document discusses swarm intelligence and how it can be used to design algorithms. It provides examples of how ants exhibit swarm intelligence through their collective foraging behaviors without centralized control. Specifically, it mentions how ant colony algorithms have been designed and applied to solve optimization problems like the traveling salesman problem by simulating the indirect communication of ants through pheromone trails. The document also notes some potential applications of swarm intelligence in robotics and communication networks.
This document discusses communication techniques in swarm robotics. It summarizes three studies on this topic. The first two studies found that using chemical pheromones for communication between swarm robots resulted in better performance than not using pheromones. The third study used a heterogeneous swarm of footbots and eyebots that communicated via telecommunication; this approach worked well due to synchronization between the bot types. In conclusion, chemical communication works best due to the self-learning nature of the robots, but telecommunication can also be effective if different robot types are synchronized properly. Future work could explore new types of chemical trails that are not limited to land or use combinations of communication approaches.
The document describes a project that aims to develop an automated system for coordinating robots working in a swarm environment. The system uses image processing to identify nearby robots by scanning QR codes on each robot and adjusting schedules to avoid collisions without human intervention. It discusses modeling swarm robotics and compares it to other multi-agent systems. The goals are to enhance efficiency of swarms through automatic coordination and reduce human errors. Applications include defence operations, sensitive tasks requiring coordination, and medical fields.
Framsticks is a system for simulating and evolving 3D creatures made of sticks. Creatures have a physical structure and neural network that are both described in their genotype. The system uses collision detection, sensors, effectors, and an evolutionary process of selection, mutation and crossover to evolve creatures. Framsticks has been used to simulate swimming and walking creatures and in film and game development. An example shows a evolved swimming creature with muscles, gyroscopes and neurons that controls its motion through feedback and bending signals.
Artificial Intelligence Today (22 June 2017)Sabri Sansoy
This was a top level presentation on some of the 30+ subcategories of Artificial Intelligence at the Hackaday LA June Meetup - Wheels, Wings, and Walkers. Sponsored by SupplyFrame Design Labs in Pasadena CA
A brief Introduction to AI and its applications in Gaming. Talk was at "Advances & Research Challenges in the Applications of AI in Gaming, Medical Imaging and Bio-Informatics"
This document provides an introduction to the CS 188: Artificial Intelligence course at UC Berkeley. It discusses key topics that will be covered in the course, including rational decision making, computational rationality, a brief history of AI, current capabilities in areas like natural language processing, computer vision, robotics, and game playing. The course will cover general techniques for designing rational agents and making decisions under uncertainty, with applications to domains like language, vision, games, and more. Students will learn how to apply existing AI techniques to new problem types.
The document discusses recommender systems and scaling machine learning models using Apache Spark. It introduces recommender systems and collaborative filtering using matrix factorization. It then explains how to implement alternating least squares in Spark to scale recommender systems. The document provides code examples in Python using Spark and the MovieLens dataset to demonstrate an alternating least squares model for movie recommendations.
Imagine there was an app that could translate our selfies into emojis!!! Well, let’s build this app together!
Join me in this talk where we have an overview of Artificial Intelligence and Machine Learning and step by step build our app with the help of Azure Cognitive Services.
masterclass de introducción a Inteligencia Artificial utilizando las APIs de Google elaborada a partir de la de Mario Ezquerro en GDG La Rioja.
Impartida dentro de las actividades de la Agenda Digital de La Rioja por AERTIC
The document discusses Sherry List's presentation on mood analysis and machine learning. It begins with introductions and provides a demo link. It then discusses key concepts like artificial intelligence, machine learning, and machine learning techniques. The remainder discusses Azure Cognitive Services and how to use them, including an example of using the Face API to detect emotions by analyzing a captured photo. Code snippets are provided for capturing a photo from the camera, calling the Face API to detect emotions, and drawing the emotions on the canvas.
Paris machine learning meetup 17 Sept. 2013agramfort
This document discusses using machine learning and functional magnetic resonance imaging (fMRI) data to predict stimuli viewed by patients. Specifically, it summarizes research by Miyawaki et al. (2008) and Nishimoto et al. (2011) that used fMRI data to predict images viewed by patients, such as faces and houses, with over 50% accuracy. It also provides an example classification task using fMRI data to predict whether a patient viewed a face or house. The document states that this example prediction can be implemented in under 250 lines of code using Scikit-Learn machine learning library.
Brains, Data, and Machine Intelligence (2014 04 14 London Meetup)Numenta
Jeff will discuss the Brains, Data, Machine Intelligence, Cortical Learning Algorithm he developed and the Numenta Platform for Intelligent Computing (NuPIC).
Deep learning techniques have achieved great success in recent years, but still lack general intelligence. Several approaches may lead to artificial general intelligence (AGI):
1. Scaling up current deep learning methods like supervised learning using vast amounts of labeled data, or unsupervised learning with huge generative models. However, it is unclear if these narrow techniques can develop general intelligence without other changes.
2. Formal approaches like AIXI aim to mathematically define optimal intelligent behavior, but suffer from computational intractability in practice.
3. Brain simulation attempts to reverse engineer the human brain, but faces challenges in modeling its high-dimensional state and dynamics at an appropriate level of abstraction.
4. Artificial life
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/sept-2017-alliance-vitf-samek
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Dr. Wojciech Samek of the Fraunhofer Heinrich Hertz Institute delivers the presentation "Methods for Understanding How Deep Neural Networks Work" at the Embedded Vision Alliance's September 2017 Vision Industry and Technology Forum. In his presentation, Dr. Samek covers the following topics:
▪ Unbeatable AI systems
▪ Deep neural network overview
▪ Opening the "black box"
▪ Summary
Architectural Tradeoff in Learning-Based SoftwarePooyan Jamshidi
In classical software development, developers write explicit instructions in a programming language to hardcode the explicit behavior of software systems. By writing each line of code, the programmer instructs the software to have the desirable behavior by exploring a specific point in program space.
Recently, however, software systems are adding learning components that, instead of hardcoding an explicit behavior, learn a behavior through data. The learning-intensive software systems are written in terms of models and their parameters that need to be adjusted based on data. In learning-enabled systems, we specify some constraints on the behavior of a desirable program (e.g., a data set of input–output pairs of examples) and use the computational resources to search through the program space to find a program that satisfies the constraints. In neural networks, we restrict the search to a continuous subset of the program space.
This talk provides experimental evidence of making tradeoffs for deep neural network models, using the Deep Neural Network Architecture system as a case study. Concrete experimental results are presented; also featured are additional case studies in big data (Storm, Cassandra), data analytics (configurable boosting algorithms), and robotics applications.
ER 2017 tutorial - On Paradoxes, Autonomous Systems and dilemmasOpher Etzion
This document discusses paradoxes, autonomous systems, and the dilemmas they present. It covers paradoxes like Russell's paradox and Zeno's paradox to illustrate logical contradictions. It then outlines how autonomous systems work by sensing their environment, making sense of inputs, decision-making, and acting. Examples of current and future applications of healthcare, industrial, and military robotics are provided. However, it also notes the dangers of causal inference from correlation and sources of uncertainty. Deep learning allows autonomous systems to self-learn but lacks transparency. This could enable remote killing and raises issues around social equality if advanced systems surpass human
A short talk at the iEvoBio (Informatics for Phylogenetics, Evolution, and Biodiversity) conference at the The University of Oklahoma, Embassy Suites Hotel and Conference Center, Norman, Oklahoma, USA. June 21-22, 2011.
Bjørnegård school visit @ Simuladagen 2015Phu H. Nguyen
The document summarizes a presentation given by Phu Hong Nguyen and Safdar Aqeel from the Software Engineering Department at Simula Research Laboratory. The presentation introduced software engineering research from robotics to biology, including projects on robotics, smart buildings, and a biology game called FightHPV to teach about cells and viruses. It advocated an approach called Model-Driven Security (MDS) to develop more secure software systems in a productive and less error-prone manner through automated code generation from security models.
Skynet? Really? How close are we to self aware, self replicating machines? In this fun session learn some of what computers can do and what they can’t. You think you know. You may be surprised.
The emerging focus on Cognitive computing, general AI, Computer Vision, Internet of Things, etc. signpost the way to new opportunities and new challenges for computers and humans alike. We decided to see how far we could get in building our own version of an all powerful controlling entity.
In this session we’ll cover how we did it, what we learned and answer those important questions like: “Can we build a Skynet yet?”, “Can my computer be my best friend?”, ”Will I ever able to program without a keyboard?”, ”Can a computer read my mind?” and the all important “will drones be able to deliver beer at the right temperature?”
Continuous Automated Testing - Cast conference workshop august 2014Noah Sussman
CAST 2014 New York: The Art and Science of Testing
The Association for Software Testing www.associationforsoftwaretesting.org
COURSE DESCRIPTION
Automated tools provide test professionals with the capability to make relevant observations even in the fastest-paced environments. Automated testing is also a powerful tool for improving communication between software engineers. This is important because good communication is a prerequisite for growing a great software engineering organization.
This workshop will explore the continuous testing of software systems. Special focus will be given to the situation where the engineering team is deploying code to production so frequently that it is not possible to perform deep regression testing before each release.
People who participate in this course will learn pragmatic automated testing strategies like:
* Data analysis on the command line with find, grep and wc.
* Network analysis with Chrome Inspector, Charles and netcat.
* Using code churn to predict hotspots where bugs may occur.
* Putting stack traces in context with automated SCM blame emails.
* Using statsd to instrument a whole application.
* Testing in production.
* Monitoring-as-testing.
Technical level: participants should have some familiarity with the command line and with editing code using a text editor or IDE. Familiarity with Git, SVN or another version control system is helpful but not required. Likewise some knowledge of Web servers is helpful but not required. It is desirable for participants to bring laptops.
BIO
From 2010 to 2012 Noah was a Test Architect at Etsy. He helped build Etsy's continuous integration system, and has helped countless other engineers develop successful automated testing strategies.These days Noah is an independent consultant in New York. He is passionate about helping engineers understand and use automated tools as they work to scale their applications more effectively.
"egg" - A stealth fine grained code analyzerFFRI, Inc.
Egg is a stealth fine-grained code analyzer developed by Fourteenforty Research Institute to analyze malware. It operates at the kernel level using techniques like page protection and trap flags to collect detailed execution information without detection. Egg can analyze kernel code, trace malware spreading across processes using taint tracking, and automatically builds call graphs and traces branch behavior. It implements taint tracking by marking suspicious elements and propagating marks to new elements that use tainted ones. Egg operates in ring-0 for fine-grained analysis without detection from anti-debugging techniques.
The document describes the development of the attention mechanism in machine translation. It presents a fictional conversation between an encoder and decoder discussing the limitations of early sequence-to-sequence models that represent the entire input with a fixed-size context vector. The decoder proposes sending each token's representation separately and calling them "keys". It then sends its own query to attend to the most relevant keys, inspired by search queries. This leads to the introduction of dot product attention to calculate similarity scores between the query and keys.
Podczas swojej prezentacji z DevDuck meetupa w Gliwicach, opierając się na swoim dwudziestoletnim doświadczeniu w branży IT, Dariusz przeanalizował korzyści i wyzwania związane z różnymi podejściami do infrastruktury: chmurą, bare metal i podejściem hybrydowym. Poruszył również aspekty regulacji prawnych i kosztów, dostarczając konkretne wskazówki na co zwrócić uwagę przy wyborze rozwiązania oraz jakie kompromisy mogą być konieczne.
Więcej informacji znajdziesz na stronie: https://career.brainhub.eu/devduck/devops-meetup
Jak zostać Dev w DevOps? O zwiększaniu niezależności zespołów developerskich ...Brainhub
Wraz z coraz szybszym tempem dostarczania aplikacji wzrasta potrzeba zwiększenia niezależności zespołów developerskich. A co jeśli zepsuje się CI/CD albo wystąpi potrzeba otrzymania informacji o wydajności naszych rozwiązań?
Podczas swojej prelekcji w ramach DevDuck meetupu Michał opowiedział o tym, o jakie elementy dziedziny DevOps możecie zadbać zarówno na początku projektu, jak i w trakcie jego trwania. Poruszył tematy standaryzacji tworzenia nowych repozytoriów, szablonów CI/CD, jak i infrastruktury. Było też trochę o bezpieczeństwie i o obserwowalności samych aplikacji. Na koniec opowiedział nam o możliwościach wczesnego planowania skalowalności oraz zabezpieczeniu aplikacji przed utknięciem u jednego dostawcy. Przygotujcie się na konkretne przykłady z jego doświadczenia.
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Rynek cloud computingu rośnie z roku na rok. Głównymi graczami na rynku są Amazon Web Services (AWS), Microsoft Azure oraz Google Cloud Platform (GCP). Małe startupy, a także ogromne korporacje decydują się na migracje do chmury. Cloud Computing może dać ogromne możliwości rozwoju programistom i pomóc w zakresie skalowalności aplikacji i elastyczności rozwiązań. To także szereg serwisów które pozwalają przyśpieszyć wdrożenia, umożliwić automatyzacje procesów, a także - szybko zareagować na awarię.
Prelekcji odpowiada na kilka kluczowych pytań związanych z chmurą: dlaczego firmy decydują się na migracje i jak wygląda rynek chmur obliczeniowych? Do czego Formuła 1 wykorzystuje AWS? Jak rozpocząć własną przygodę z chmurą? Czy certyfikacje AWS w ogóle mają sens?
Konfiguracja GitLab CI/CD pipelines od podstawBrainhub
O prezentacji:
W trakcie prelekcji pokażę jak zaimplementować proces CI/CD dla aplikacji napisanej w JavaScript, używając GitLab CI/CD Pipelines. Będzie on zawierał kroki lint (statyczna analiza kodu), unit test, API test, Docker Build i UI end-to-end test. Pokażę też jak tworzyć, parsować i wyświetlać raporty z testów w GitLabie. Powiem też co nieco o używanych w procesie Dockerfile i docker-compose.
O prelegencie:
Przygodę z profesjonalnym IT rozpoczął ponad 10 lat temu, jako Manual Junior Tester. Od tego czasu stara się w pełni zrozumieć rolę QA w projekcie i wielopoziomowo pracować nad poprawą jakości projektu, produktu i pracy.
O prezentacji:
Chcąc uzyskać type safety w projekcie możemy zdecydować się na samodzielne tworzenie, utrzymywane oraz współdzielenie typów. Inną możliwością jest skorzystanie z gotowego rozwiązania (np. generatora typów), które stworzy typy za pomocą komendy. Obie te opcje wymagają jednak dodatkowego nakładu pracy. tRPC niweluje ten problem pozwalając na natychmiastową synchronizację zmian między backendem a frontendem.
Podczas prelekcji opowiem o obecnych możliwościach i ograniczeniach tRPC, a także kiedy warto z tego narzędzia skorzystać. Dodatkowo podczas live codingu pokażę jak szybko i wygodnie można stworzyć API za pomocą tRPC i frameworku Next.js.
O prelegencie:
Karierę w IT zaczęła niecałe 3 lata temu jako programistka React Native. Szybko jednak zaciekawił ją także web dev i backend, co rozpoczęło jej drogę jako programistka full-stack. Uwielbia śledzić i wykorzystywać w projektach nowinki ze świata JavaScriptu. Poza pracą spędza czas uprawiając przeróżne sporty - od treningu siłowego i roweru, poprzez jogę, aż po narty.
Solid.js - czy rzeczywiście został tak solidnie stworzony? Na najbliższym meetupie weźmiemy na warsztat prostą apkę napisaną w React i w Solid, omówimy różnice między nimi i spróbujemy zagłębić się w szczegóły. Odpowiemy sobie też na dwa pytania: czy Solid będzie w stanie zdetronizować Reacta mając JSX i observability? Czy warto było szaleć tak? Przekonamy się na DevDucku.
Struktury algebraiczne do programowania mają się tak, jak fizyka molekularna ma się do gotowania - można się bez nich obejść, ale to nie znaczy, że ich tam nie ma. Podczas najbliższego DevDucka przyjrzymy się kilku z nich i sprawdzimy, jak mogą się przydać do pisania czystego kodu i rozwiązywania problemów w praktyce.
WebAssembly - czy dzisiaj mi się to przyda do pracy?Brainhub
Rust, Go, AssemblyScript - wszystko co chcesz wiedzieć o WebAssembly, a o co boisz się zapytać. WebAssembly jest bardzo młodą technologią i jeszcze wiele pracy czeka programistów stojących za projektem. Benedykt opowiadał już na ten temat podczas dev.js Summit 2021, ale postanowił zgłębić niektóre wątki i uzupełnić o nowości ze świata WebAssembley.
We współpracy z Mateuszem Koniecznym opowiedzą o WASM i pokażą kilka przykładów podczas live-codingu.
Ewoluowanie neuronowych mózgów w JavaScript, wielowątkowo!Brainhub
JavaScript nie słynie z wydajności. Wielowątkowy on też za bardzo nie jest i zupełnie nie nadaje się ani do symulowania wirtualnego świata z ewoluującymi "organizmami", ani do liczenia sieci neuronowych. Cooooo? Nie nadaje się? Potrzymaj mi piwo!
Prezentacja Łukasza pozwoli na obserwację tego, co wyewoluuje w zależności od stworzonych warunków z wykorzystaniem algorytmu ewolucyjnego, odpowiadającego jak najbardziej biologicznej ewolucji. Będzie również o tym, jak różni się on od algorytmów uczenia maszynowego, zazwyczaj używanego do trenowania sieci neuronowych. Spróbujemy też sprawić, by symulacja była wydajna i może nawet wielowątkowa. Pogadamy także o sieciach neuronowych oraz biologii ewolucyjnej.
The hunt of the unicorn, to capture productivityBrainhub
The document provides techniques for improving productivity and focus. It suggests limiting distractions by not checking email or talking about unimportant things first thing in the morning. It recommends optimizing for deep work by turning off notifications and practicing "mise en place". The document outlines ready to use techniques like tackling the most important task first, using creative triggers to get in a focused state of mind, starting with small blocks of focused time, and using the Pomodoro technique. It stresses the importance of monitoring your bandwidth, limiting commitments, prioritizing tasks, and ignoring some bugs. Overall, the document presents numerous evidence-based strategies for catching the productivity "unicorn" and optimizing one's focus and workflow.
This document contains advice from Marcin Dryka on test-driven development (TDD) and best practices for writing unit tests. Some of the key points made include:
1. TDD is a design process, not just a testing process. Tests should describe desired functionality before writing code.
2. Tests should have single, well-defined purposes and only test one interaction at a time. Avoid complex logic in tests.
3. Mock external dependencies but not the system under test. Tests should be deterministic and independent from each other.
WebAssembly - kolejny buzzword, czy (r)ewolucja?Brainhub
WebAssembly (WASM) is a new language that runs at near-native speed by compiling to efficient binary code. It is compiled from C/C++ using Emscripten and the LLVM compiler to WASM, which runs on the same VM as JavaScript. WASM has the potential to improve performance for applications like games that require high performance, but it is still in MVP stage so premature optimization using it is not recommended. Developers need to evaluate their specific needs to determine if learning WASM is worthwhile at this time.
React performance best practices include using the react-addons-perf module to measure wasted renders, avoiding direct state/prop mutations, ensuring connected components only re-render when needed, and using immutability to prevent unnecessary re-renders when data changes. Potential issues are components re-rendering even when props haven't changed, large portions of the tree re-rendering unnecessarily, and binding functions incorrectly.
RxJS is a library for reactive programming that allows composing asynchronous and event-based programs using observable sequences. It provides the Observable type for pushing multiple values to observers over time asynchronously. Operators allow transforming and combining observables. Key types include Observable, Observer, Subject, BehaviorSubject, and ReplaySubject. Subjects can multicast values to multiple observers. Overall, RxJS is useful for handling asynchronous events as collections in a declarative way.
As presented at DevDuck #6 - JavaScript meetup for developers (www.devduck.pl)
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Looking for a company to build your React app? - Check us out at www.brainhub.eu
Kilka praktycznych rad o budowaniu startupu i znaczeniu technologii.
#1 Dobór startup
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#3 Nie potrzebujesz CTO
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As presented at DevDuck #3 - JavaScript meetup for developers (www.devduck.pl)
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As presented at DevDuck #3 - JavaScript meetup for developers (www.devduck.pl)
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All you need to know about using React with Redux
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AgentExchange is Salesforce’s latest innovation, expanding upon the foundation of AppExchange by offering a centralized marketplace for AI-powered digital labor. Designed for Agentblazers, developers, and Salesforce admins, this platform enables the rapid development and deployment of AI agents across industries.
Email: [email protected]
Phone: +1(630) 349 2411
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Mastering Fluent Bit: Ultimate Guide to Integrating Telemetry Pipelines with ...Eric D. Schabell
It's time you stopped letting your telemetry data pressure your budgets and get in the way of solving issues with agility! No more I say! Take back control of your telemetry data as we guide you through the open source project Fluent Bit. Learn how to manage your telemetry data from source to destination using the pipeline phases covering collection, parsing, aggregation, transformation, and forwarding from any source to any destination. Buckle up for a fun ride as you learn by exploring how telemetry pipelines work, how to set up your first pipeline, and exploring several common use cases that Fluent Bit helps solve. All this backed by a self-paced, hands-on workshop that attendees can pursue at home after this session (https://o11y-workshops.gitlab.io/workshop-fluentbit).
Not So Common Memory Leaks in Java WebinarTier1 app
This SlideShare presentation is from our May webinar, “Not So Common Memory Leaks & How to Fix Them?”, where we explored lesser-known memory leak patterns in Java applications. Unlike typical leaks, subtle issues such as thread local misuse, inner class references, uncached collections, and misbehaving frameworks often go undetected and gradually degrade performance. This deck provides in-depth insights into identifying these hidden leaks using advanced heap analysis and profiling techniques, along with real-world case studies and practical solutions. Ideal for developers and performance engineers aiming to deepen their understanding of Java memory management and improve application stability.
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How to Batch Export Lotus Notes NSF Emails to Outlook PST Easily?steaveroggers
Migrating from Lotus Notes to Outlook can be a complex and time-consuming task, especially when dealing with large volumes of NSF emails. This presentation provides a complete guide on how to batch export Lotus Notes NSF emails to Outlook PST format quickly and securely. It highlights the challenges of manual methods, the benefits of using an automated tool, and introduces eSoftTools NSF to PST Converter Software — a reliable solution designed to handle bulk email migrations efficiently. Learn about the software’s key features, step-by-step export process, system requirements, and how it ensures 100% data accuracy and folder structure preservation during migration. Make your email transition smoother, safer, and faster with the right approach.
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Maxon Cinema 4D 2025 is the latest version of the Maxon's 3D software, released in September 2024, and it builds upon previous versions with new tools for procedural modeling and animation, as well as enhancements to particle, Pyro, and rigid body simulations. CG Channel also mentions that Cinema 4D 2025.2, released in April 2025, focuses on spline tools and unified simulation enhancements.
Key improvements and features of Cinema 4D 2025 include:
Procedural Modeling: New tools and workflows for creating models procedurally, including fabric weave and constellation generators.
Procedural Animation: Field Driver tag for procedural animation.
Simulation Enhancements: Improved particle, Pyro, and rigid body simulations.
Spline Tools: Enhanced spline tools for motion graphics and animation, including spline modifiers from Rocket Lasso now included for all subscribers.
Unified Simulation & Particles: Refined physics-based effects and improved particle systems.
Boolean System: Modernized boolean system for precise 3D modeling.
Particle Node Modifier: New particle node modifier for creating particle scenes.
Learning Panel: Intuitive learning panel for new users.
Redshift Integration: Maxon now includes access to the full power of Redshift rendering for all new subscriptions.
In essence, Cinema 4D 2025 is a major update that provides artists with more powerful tools and workflows for creating 3D content, particularly in the fields of motion graphics, VFX, and visualization.
Interactive Odoo Dashboard for various business needs can provide users with dynamic, visually appealing dashboards tailored to their specific requirements. such a module that could support multiple dashboards for different aspects of a business
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Proactive Vulnerability Detection in Source Code Using Graph Neural Networks:...Ranjan Baisak
As software complexity grows, traditional static analysis tools struggle to detect vulnerabilities with both precision and context—often triggering high false positive rates and developer fatigue. This article explores how Graph Neural Networks (GNNs), when applied to source code representations like Abstract Syntax Trees (ASTs), Control Flow Graphs (CFGs), and Data Flow Graphs (DFGs), can revolutionize vulnerability detection. We break down how GNNs model code semantics more effectively than flat token sequences, and how techniques like attention mechanisms, hybrid graph construction, and feedback loops significantly reduce false positives. With insights from real-world datasets and recent research, this guide shows how to build more reliable, proactive, and interpretable vulnerability detection systems using GNNs.
Scaling GraphRAG: Efficient Knowledge Retrieval for Enterprise AIdanshalev
If we were building a GenAI stack today, we'd start with one question: Can your retrieval system handle multi-hop logic?
Trick question, b/c most can’t. They treat retrieval as nearest-neighbor search.
Today, we discussed scaling #GraphRAG at AWS DevOps Day, and the takeaway is clear: VectorRAG is naive, lacks domain awareness, and can’t handle full dataset retrieval.
GraphRAG builds a knowledge graph from source documents, allowing for a deeper understanding of the data + higher accuracy.
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What Do Contribution Guidelines Say About Software Testing? (MSR 2025)Andre Hora
Software testing plays a crucial role in the contribution process of open-source projects. For example, contributions introducing new features are expected to include tests, and contributions with tests are more likely to be accepted. Although most real-world projects require contributors to write tests, the specific testing practices communicated to contributors remain unclear. In this paper, we present an empirical study to understand better how software testing is approached in contribution guidelines. We analyze the guidelines of 200 Python and JavaScript open-source software projects. We find that 78% of the projects include some form of test documentation for contributors. Test documentation is located in multiple sources, including CONTRIBUTING files (58%), external documentation (24%), and README files (8%). Furthermore, test documentation commonly explains how to run tests (83.5%), but less often provides guidance on how to write tests (37%). It frequently covers unit tests (71%), but rarely addresses integration (20.5%) and end-to-end tests (15.5%). Other key testing aspects are also less frequently discussed: test coverage (25.5%) and mocking (9.5%). We conclude by discussing implications and future research.
Exploring Wayland: A Modern Display Server for the FutureICS
Wayland is revolutionizing the way we interact with graphical interfaces, offering a modern alternative to the X Window System. In this webinar, we’ll delve into the architecture and benefits of Wayland, including its streamlined design, enhanced performance, and improved security features.
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In essence, Avast Premium Security provides a robust suite of tools to keep your devices and online activity safe and secure, according to Avast.
Exceptional Behaviors: How Frequently Are They Tested? (AST 2025)Andre Hora
Exceptions allow developers to handle error cases expected to occur infrequently. Ideally, good test suites should test both normal and exceptional behaviors to catch more bugs and avoid regressions. While current research analyzes exceptions that propagate to tests, it does not explore other exceptions that do not reach the tests. In this paper, we provide an empirical study to explore how frequently exceptional behaviors are tested in real-world systems. We consider both exceptions that propagate to tests and the ones that do not reach the tests. For this purpose, we run an instrumented version of test suites, monitor their execution, and collect information about the exceptions raised at runtime. We analyze the test suites of 25 Python systems, covering 5,372 executed methods, 17.9M calls, and 1.4M raised exceptions. We find that 21.4% of the executed methods do raise exceptions at runtime. In methods that raise exceptions, on the median, 1 in 10 calls exercise exceptional behaviors. Close to 80% of the methods that raise exceptions do so infrequently, but about 20% raise exceptions more frequently. Finally, we provide implications for researchers and practitioners. We suggest developing novel tools to support exercising exceptional behaviors and refactoring expensive try/except blocks. We also call attention to the fact that exception-raising behaviors are not necessarily “abnormal” or rare.
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7. Family of algorithms
Origin
Working
Heuristic algorithms
Swarm intelligence
What and why?
Greek heurisco
we look for not the best solution but a satisfiable one
Piotr Sroczkowski Ant colony optimization
8. Family of algorithms
Origin
Working
Heuristic algorithms
Swarm intelligence
What and why?
Greek heurisco
we look for not the best solution but a satisfiable one
like in real life ex. finding the best product to buy
Piotr Sroczkowski Ant colony optimization
9. Family of algorithms
Origin
Working
Heuristic algorithms
Swarm intelligence
What and why?
Greek heurisco
we look for not the best solution but a satisfiable one
like in real life ex. finding the best product to buy
very often it’s impossible to create an algorithm which will
find the optimum solution
Piotr Sroczkowski Ant colony optimization
10. Family of algorithms
Origin
Working
Heuristic algorithms
Swarm intelligence
What and why?
Greek heurisco
we look for not the best solution but a satisfiable one
like in real life ex. finding the best product to buy
very often it’s impossible to create an algorithm which will
find the optimum solution
... or such a program (implementation of the algorithm) will
run very long
Piotr Sroczkowski Ant colony optimization
11. Family of algorithms
Origin
Working
Heuristic algorithms
Swarm intelligence
What and why?
Greek heurisco
we look for not the best solution but a satisfiable one
like in real life ex. finding the best product to buy
very often it’s impossible to create an algorithm which will
find the optimum solution
... or such a program (implementation of the algorithm) will
run very long
... or a program which generates the program above will run
very long
Piotr Sroczkowski Ant colony optimization
12. Family of algorithms
Origin
Working
Heuristic algorithms
Swarm intelligence
What and why?
Greek heurisco
we look for not the best solution but a satisfiable one
like in real life ex. finding the best product to buy
very often it’s impossible to create an algorithm which will
find the optimum solution
... or such a program (implementation of the algorithm) will
run very long
... or a program which generates the program above will run
very long
... or running such a program will be very expensive (ex. in a
cloud like AWS, Digital Ocean, Microsoft Azure...)
Piotr Sroczkowski Ant colony optimization
13. Family of algorithms
Origin
Working
Heuristic algorithms
Swarm intelligence
What and why?
Greek heurisco
we look for not the best solution but a satisfiable one
like in real life ex. finding the best product to buy
very often it’s impossible to create an algorithm which will
find the optimum solution
... or such a program (implementation of the algorithm) will
run very long
... or a program which generates the program above will run
very long
... or running such a program will be very expensive (ex. in a
cloud like AWS, Digital Ocean, Microsoft Azure...)
... or a user will become frustrated because even 5 seconds to
run a program could be a bad UX
Piotr Sroczkowski Ant colony optimization
14. Family of algorithms
Origin
Working
Heuristic algorithms
Swarm intelligence
What and why?
Greek heurisco
we look for not the best solution but a satisfiable one
like in real life ex. finding the best product to buy
very often it’s impossible to create an algorithm which will
find the optimum solution
... or such a program (implementation of the algorithm) will
run very long
... or a program which generates the program above will run
very long
... or running such a program will be very expensive (ex. in a
cloud like AWS, Digital Ocean, Microsoft Azure...)
... or a user will become frustrated because even 5 seconds to
run a program could be a bad UX
...
Piotr Sroczkowski Ant colony optimization
16. Family of algorithms
Origin
Working
Heuristic algorithms
Swarm intelligence
Application - medicine / bioinformatics
clinical decision support system
Piotr Sroczkowski Ant colony optimization
17. Family of algorithms
Origin
Working
Heuristic algorithms
Swarm intelligence
Application - medicine / bioinformatics
clinical decision support system
MSA (multiple sequence alignment) - genetics
Piotr Sroczkowski Ant colony optimization
20. Family of algorithms
Origin
Working
Heuristic algorithms
Swarm intelligence
Application - software engineering
mutation testing
virus detection
Piotr Sroczkowski Ant colony optimization
21. Family of algorithms
Origin
Working
Heuristic algorithms
Swarm intelligence
Application - software engineering
mutation testing
virus detection
file allocation for a distributed system
Piotr Sroczkowski Ant colony optimization
22. Family of algorithms
Origin
Working
Heuristic algorithms
Swarm intelligence
Application - software engineering
mutation testing
virus detection
file allocation for a distributed system
parallelization
Piotr Sroczkowski Ant colony optimization
23. Family of algorithms
Origin
Working
Heuristic algorithms
Swarm intelligence
Application - software engineering
mutation testing
virus detection
file allocation for a distributed system
parallelization
planning database queries
Piotr Sroczkowski Ant colony optimization
24. Family of algorithms
Origin
Working
Heuristic algorithms
Swarm intelligence
Application - software engineering
mutation testing
virus detection
file allocation for a distributed system
parallelization
planning database queries
queueing
Piotr Sroczkowski Ant colony optimization
25. Family of algorithms
Origin
Working
Heuristic algorithms
Swarm intelligence
Application - software engineering
mutation testing
virus detection
file allocation for a distributed system
parallelization
planning database queries
queueing
virtual DOM in React
Piotr Sroczkowski Ant colony optimization
27. Family of algorithms
Origin
Working
Heuristic algorithms
Swarm intelligence
Application - police / security
construction of facial composites from eyewitnesses
Piotr Sroczkowski Ant colony optimization
28. Family of algorithms
Origin
Working
Heuristic algorithms
Swarm intelligence
Application - police / security
construction of facial composites from eyewitnesses
design of anti-terrorism systems
Piotr Sroczkowski Ant colony optimization
31. Family of algorithms
Origin
Working
Heuristic algorithms
Swarm intelligence
Application - others
finding the cheapest flight
time scheduling
Piotr Sroczkowski Ant colony optimization
32. Family of algorithms
Origin
Working
Heuristic algorithms
Swarm intelligence
Application - others
finding the cheapest flight
time scheduling
aircraft wing design
Piotr Sroczkowski Ant colony optimization
33. Family of algorithms
Origin
Working
Heuristic algorithms
Swarm intelligence
Application - others
finding the cheapest flight
time scheduling
aircraft wing design
pop music production
Piotr Sroczkowski Ant colony optimization
34. Family of algorithms
Origin
Working
Heuristic algorithms
Swarm intelligence
Application - others
finding the cheapest flight
time scheduling
aircraft wing design
pop music production
container loading
Piotr Sroczkowski Ant colony optimization
36. Family of algorithms
Origin
Working
Heuristic algorithms
Swarm intelligence
Application - next others
there are so many applications so this presentation cannot
contain them all
Piotr Sroczkowski Ant colony optimization
39. Family of algorithms
Origin
Working
Heuristic algorithms
Swarm intelligence
Characteristics
small agents / boids
they interact locally with one another
Piotr Sroczkowski Ant colony optimization
40. Family of algorithms
Origin
Working
Heuristic algorithms
Swarm intelligence
Characteristics
small agents / boids
they interact locally with one another
they interact locally with the environment
Piotr Sroczkowski Ant colony optimization
41. Family of algorithms
Origin
Working
Heuristic algorithms
Swarm intelligence
Characteristics
small agents / boids
they interact locally with one another
they interact locally with the environment
the inspiration comes above all from the nature
Piotr Sroczkowski Ant colony optimization
42. Family of algorithms
Origin
Working
Heuristic algorithms
Swarm intelligence
Characteristics
small agents / boids
they interact locally with one another
they interact locally with the environment
the inspiration comes above all from the nature
therefore (like in other heuristics) there is much randomness
Piotr Sroczkowski Ant colony optimization
46. Family of algorithms
Origin
Working
Heuristic algorithms
Swarm intelligence
Examples
ant colony optimization
bee colony optimization
firefly algorithm
Piotr Sroczkowski Ant colony optimization
47. Family of algorithms
Origin
Working
Heuristic algorithms
Swarm intelligence
Examples
ant colony optimization
bee colony optimization
firefly algorithm
bat algorithm
Piotr Sroczkowski Ant colony optimization
48. Family of algorithms
Origin
Working
Heuristic algorithms
Swarm intelligence
Examples
ant colony optimization
bee colony optimization
firefly algorithm
bat algorithm
self-propelled particles
Piotr Sroczkowski Ant colony optimization
49. Family of algorithms
Origin
Working
Heuristic algorithms
Swarm intelligence
Examples
ant colony optimization
bee colony optimization
firefly algorithm
bat algorithm
self-propelled particles
charged system exploration
Piotr Sroczkowski Ant colony optimization
50. Family of algorithms
Origin
Working
Heuristic algorithms
Swarm intelligence
Examples
ant colony optimization
bee colony optimization
firefly algorithm
bat algorithm
self-propelled particles
charged system exploration
multiple swarm optimization
Piotr Sroczkowski Ant colony optimization
51. Family of algorithms
Origin
Working
Heuristic algorithms
Swarm intelligence
Examples
ant colony optimization
bee colony optimization
firefly algorithm
bat algorithm
self-propelled particles
charged system exploration
multiple swarm optimization
altruism algorithm
Piotr Sroczkowski Ant colony optimization
52. Family of algorithms
Origin
Working
Heuristic algorithms
Swarm intelligence
Examples
ant colony optimization
bee colony optimization
firefly algorithm
bat algorithm
self-propelled particles
charged system exploration
multiple swarm optimization
altruism algorithm
artificial immunological systems
Piotr Sroczkowski Ant colony optimization
58. Family of algorithms
Origin
Working
When and where?
1992
Marco Dorigo
PhD thesis
Universit´e Libre de Bruxelles
to find the optimal path in a graph
Piotr Sroczkowski Ant colony optimization
62. Family of algorithms
Origin
Working
Principles
Ants wander randomly
They lay down pheromone trails
They follow pheromones (the pheromones increase probability
of going to a particular side)
Piotr Sroczkowski Ant colony optimization
63. Family of algorithms
Origin
Working
Principles
Ants wander randomly
They lay down pheromone trails
They follow pheromones (the pheromones increase probability
of going to a particular side)
The pheromones evaporate
Piotr Sroczkowski Ant colony optimization