This is a short talk I gave to the Strathclyde Planning Group on deficiencies I can see in the way we thing and reason about planning in non-deterministic environments. PPDDL - the accepted standard - is overly simplistic and can get us into hot water because we focus on solving the PPDDL problem, rather than the Real World problem it models.
The breakout session that followed was very useful for generating a lot of ideas about different threads we could use to attack the weaknesses of PPDDL and work being done around the edges, which I hope to summarise at some point.
How to build health video games with a purpose?Mathieu Goudot
This is the ground study I made for the Cliinic.me project. This is an inquiry into the history of health-themed video games.
The conclusion : wide-audience health games lack ambition. Research has shown games with a purpose can have a broad appeal for population AND bring a real benefit by teaching medical content and actually allowing for diagnosis of particular diseases to happen.
From this study, we know that a game teaching players the basis of diagnosis while being fun is possible. And trying to turn this game into a crowd-diagnosis platform is the ultimate goal to reach.
How to build health video games with a purpose?Mathieu Goudot
This is the ground study I made for the Cliinic.me project. This is an inquiry into the history of health-themed video games.
The conclusion : wide-audience health games lack ambition. Research has shown games with a purpose can have a broad appeal for population AND bring a real benefit by teaching medical content and actually allowing for diagnosis of particular diseases to happen.
From this study, we know that a game teaching players the basis of diagnosis while being fun is possible. And trying to turn this game into a crowd-diagnosis platform is the ultimate goal to reach.
Pecha kucha: ratios, proportions, and probabilityBrent Edward
A Pecha Kucha (20 slides in 6 minutes) presentation meant to be a useful introduction and overview for a 7th grade middle school mathunit on Ratios, Proportions, and Probability
Topic: Standard Deviation
Student Name: Shamsa
Class: B.Ed. 2.5
Project Name: “Young Teachers' Professional Development (TPD)"
"Project Founder: Prof. Dr. Amjad Ali Arain
Faculty of Education, University of Sindh, Pakistan
As part of our book reading club in eBay, I did a talk about one of my favourites book "The Art of Thinking Clearly". Here are some snapshots from the book in my own words.
Probability implies 'likelihood' or 'chance'. When an event is certain to happen then the probability of occurrence of that event is 1 and when it is certain that the event cannot happen then the probability of that event is 0.
A distribution where only two outcomes are possible, such as success or failure, gain or loss, win or lose and where the probability of success and failure is the same for all the trials is called a Binomial Distribution.
#35816 Topic Discussion5Number of Pages 1 (Double Spaced)N.docxAASTHA76
#35816 Topic: Discussion5
Number of Pages: 1 (Double Spaced)
Number of sources: 1
Writing Style: APA
Type of document: Essay
Academic Level:Master
Category: Psychology
Language Style: English (U.S.)
Order Instructions: ATTACHED
I will upload the instruction
Discussion: Discuss, elaborate and give example. Please follow the instruction carefully. No running head please.
Author: (Jackson, S.L. (2017) Statistics Plain and Simple: (4th edition) - Cengage Learning)
Please use the author or refence that I provided
Instructions:
Review this week’s course materials and learning activities, and reflect on your learning so far this week. Respond to one or more of the following prompts in one to two paragraphs:
1. Provide citation and reference to the material(s) you discuss. Describe what you found interesting regarding this topic, and why.
2. Describe how you will apply that learning in your daily life, including your work life.
3. Describe what may be unclear to you, and what you would like to learn.
Reference:
Basic Probability Concepts
The Rules of Probability
Probability and the Standard Normal Distribution
Review of Key Terms
Module Exercises
Critical Thinking Check Answers
Module 8: Hypothesis Testing and Inferential Statistics
Null and Alternative Hypotheses
Two-Tailed and One-Tailed Hypothesis Tests
Type I and Type II Errors in Hypothesis Testing
Probability, Statistical Significance, and Errors
Using Inferential Statistics
Review of Key Terms
Module Exercises
Critical Thinking Check Answers
Chapter 4 Summary and Review
In this chapter you will be introduced to the concepts of probability and hypothesis testing. Probability is the study of likelihood and uncertainty. Most decisions that we make are probabilistic in nature. Thus, probability plays a critical role in most professions and in our everyday decisions. We will discuss basic probability concepts along with how to compute probabilities and the use of the standard normal curve in making probabilistic decisions.
probability The study of likelihood and uncertainty; the number of ways a particular outcome can occur, divided by the total number of outcomes.
Hypothesis testing is the process of determining whether a hypothesis is supported by the results of a research project. Our introduction to hypothesis testing will include a discussion of the null and alternative hypotheses, Type I and Type II errors, and one- and two-tailed tests of hypotheses as well as an introduction to statistical significance and probability as they relate to inferential statistics.
hypothesis testing The process of determining whether a hypothesis is supported by the results of a research study.
MODULE 7
Probability
Learning Objectives
•Understand how probability is used in everyday life.
•Know how to compute a probability.
•Understand and be able to apply the multiplication rule.
•Understand and be able to apply the addition rule.
•Understand the relationship between the standard normal c.
35845 Topic Group AssignmentNumber of Pages 1 (Double Spaced.docxrhetttrevannion
35845 Topic: Group Assignment
Number of Pages: 1 (Double Spaced)
Number of sources: 1
Writing Style: APA
Type of document: Essay
Academic Level:Master
Category: Psychology
Language Style: English (U.S.)
Order Instructions: Attached
Please follow the instruction carefully.
I will upload the instruction
Instruction: Please fill up or answer only the last topic on the Material I attach. Fill in directly to the material I provided.
Author: Jackson, S. L. (2017). Statistics plain and simple, (4th ed.). Boston, MA: Cengage Learning.
Basic Probability Concepts
The Rules of Probability
Probability and the Standard Normal Distribution
Review of Key Terms
Module Exercises
Critical Thinking Check Answers
Module 8: Hypothesis Testing and Inferential Statistics
Null and Alternative Hypotheses
Two-Tailed and One-Tailed Hypothesis Tests
Type I and Type II Errors in Hypothesis Testing
Probability, Statistical Significance, and Errors
Using Inferential Statistics
Review of Key Terms
Module Exercises
Critical Thinking Check Answers
Chapter 4 Summary and Review
In this chapter you will be introduced to the concepts of probability and hypothesis testing. Probability is the study of likelihood and uncertainty. Most decisions that we make are probabilistic in nature. Thus, probability plays a critical role in most professions and in our everyday decisions. We will discuss basic probability concepts along with how to compute probabilities and the use of the standard normal curve in making probabilistic decisions.
probability The study of likelihood and uncertainty; the number of ways a particular outcome can occur, divided by the total number of outcomes.
Hypothesis testing is the process of determining whether a hypothesis is supported by the results of a research project. Our introduction to hypothesis testing will include a discussion of the null and alternative hypotheses, Type I and Type II errors, and one- and two-tailed tests of hypotheses as well as an introduction to statistical significance and probability as they relate to inferential statistics.
hypothesis testing The process of determining whether a hypothesis is supported by the results of a research study.
MODULE 7
Probability
Learning Objectives
•Understand how probability is used in everyday life.
•Know how to compute a probability.
•Understand and be able to apply the multiplication rule.
•Understand and be able to apply the addition rule.
•Understand the relationship between the standard normal curve and probability.
In order to better understand the nature of probabilistic decisions, consider the following court case of The People v. Collins, 1968. In this case, the robbery victim was unable to identify his assailant. All that the victim could recall was that the assailant was female with a blonde pony tail. In addition, he remembered that she fled the scene in a yellow convertible that was driven by an African American male who had a full beard. The suspect in the case fit the.
Code Fast, die() Early, Throw Structured ExceptionsJohn Anderson
Slides from a short talk given at January 2012 DC.pm. Covers "classic" exceptions in Perl as well as some libraries to make working with exceptions easier.
This talk, delivered at the Høgskolen i Bergen (Bergen College) in Norway in October 2014. It covers some recent games and deconstructs potential AI techniques that could* be used by these games to achieve this.
* Note that the author has no knowledge of the internals of these games and this is broadly educated speculation.
What's with all the zombies in games right now? In this session, I talk about some of the reasons that Zombies are a lazy AI Engineers dream come true and what we could be doing instead
More Related Content
Similar to The Ludic Fallacy Applied to Automated Planning
Pecha kucha: ratios, proportions, and probabilityBrent Edward
A Pecha Kucha (20 slides in 6 minutes) presentation meant to be a useful introduction and overview for a 7th grade middle school mathunit on Ratios, Proportions, and Probability
Topic: Standard Deviation
Student Name: Shamsa
Class: B.Ed. 2.5
Project Name: “Young Teachers' Professional Development (TPD)"
"Project Founder: Prof. Dr. Amjad Ali Arain
Faculty of Education, University of Sindh, Pakistan
As part of our book reading club in eBay, I did a talk about one of my favourites book "The Art of Thinking Clearly". Here are some snapshots from the book in my own words.
Probability implies 'likelihood' or 'chance'. When an event is certain to happen then the probability of occurrence of that event is 1 and when it is certain that the event cannot happen then the probability of that event is 0.
A distribution where only two outcomes are possible, such as success or failure, gain or loss, win or lose and where the probability of success and failure is the same for all the trials is called a Binomial Distribution.
#35816 Topic Discussion5Number of Pages 1 (Double Spaced)N.docxAASTHA76
#35816 Topic: Discussion5
Number of Pages: 1 (Double Spaced)
Number of sources: 1
Writing Style: APA
Type of document: Essay
Academic Level:Master
Category: Psychology
Language Style: English (U.S.)
Order Instructions: ATTACHED
I will upload the instruction
Discussion: Discuss, elaborate and give example. Please follow the instruction carefully. No running head please.
Author: (Jackson, S.L. (2017) Statistics Plain and Simple: (4th edition) - Cengage Learning)
Please use the author or refence that I provided
Instructions:
Review this week’s course materials and learning activities, and reflect on your learning so far this week. Respond to one or more of the following prompts in one to two paragraphs:
1. Provide citation and reference to the material(s) you discuss. Describe what you found interesting regarding this topic, and why.
2. Describe how you will apply that learning in your daily life, including your work life.
3. Describe what may be unclear to you, and what you would like to learn.
Reference:
Basic Probability Concepts
The Rules of Probability
Probability and the Standard Normal Distribution
Review of Key Terms
Module Exercises
Critical Thinking Check Answers
Module 8: Hypothesis Testing and Inferential Statistics
Null and Alternative Hypotheses
Two-Tailed and One-Tailed Hypothesis Tests
Type I and Type II Errors in Hypothesis Testing
Probability, Statistical Significance, and Errors
Using Inferential Statistics
Review of Key Terms
Module Exercises
Critical Thinking Check Answers
Chapter 4 Summary and Review
In this chapter you will be introduced to the concepts of probability and hypothesis testing. Probability is the study of likelihood and uncertainty. Most decisions that we make are probabilistic in nature. Thus, probability plays a critical role in most professions and in our everyday decisions. We will discuss basic probability concepts along with how to compute probabilities and the use of the standard normal curve in making probabilistic decisions.
probability The study of likelihood and uncertainty; the number of ways a particular outcome can occur, divided by the total number of outcomes.
Hypothesis testing is the process of determining whether a hypothesis is supported by the results of a research project. Our introduction to hypothesis testing will include a discussion of the null and alternative hypotheses, Type I and Type II errors, and one- and two-tailed tests of hypotheses as well as an introduction to statistical significance and probability as they relate to inferential statistics.
hypothesis testing The process of determining whether a hypothesis is supported by the results of a research study.
MODULE 7
Probability
Learning Objectives
•Understand how probability is used in everyday life.
•Know how to compute a probability.
•Understand and be able to apply the multiplication rule.
•Understand and be able to apply the addition rule.
•Understand the relationship between the standard normal c.
35845 Topic Group AssignmentNumber of Pages 1 (Double Spaced.docxrhetttrevannion
35845 Topic: Group Assignment
Number of Pages: 1 (Double Spaced)
Number of sources: 1
Writing Style: APA
Type of document: Essay
Academic Level:Master
Category: Psychology
Language Style: English (U.S.)
Order Instructions: Attached
Please follow the instruction carefully.
I will upload the instruction
Instruction: Please fill up or answer only the last topic on the Material I attach. Fill in directly to the material I provided.
Author: Jackson, S. L. (2017). Statistics plain and simple, (4th ed.). Boston, MA: Cengage Learning.
Basic Probability Concepts
The Rules of Probability
Probability and the Standard Normal Distribution
Review of Key Terms
Module Exercises
Critical Thinking Check Answers
Module 8: Hypothesis Testing and Inferential Statistics
Null and Alternative Hypotheses
Two-Tailed and One-Tailed Hypothesis Tests
Type I and Type II Errors in Hypothesis Testing
Probability, Statistical Significance, and Errors
Using Inferential Statistics
Review of Key Terms
Module Exercises
Critical Thinking Check Answers
Chapter 4 Summary and Review
In this chapter you will be introduced to the concepts of probability and hypothesis testing. Probability is the study of likelihood and uncertainty. Most decisions that we make are probabilistic in nature. Thus, probability plays a critical role in most professions and in our everyday decisions. We will discuss basic probability concepts along with how to compute probabilities and the use of the standard normal curve in making probabilistic decisions.
probability The study of likelihood and uncertainty; the number of ways a particular outcome can occur, divided by the total number of outcomes.
Hypothesis testing is the process of determining whether a hypothesis is supported by the results of a research project. Our introduction to hypothesis testing will include a discussion of the null and alternative hypotheses, Type I and Type II errors, and one- and two-tailed tests of hypotheses as well as an introduction to statistical significance and probability as they relate to inferential statistics.
hypothesis testing The process of determining whether a hypothesis is supported by the results of a research study.
MODULE 7
Probability
Learning Objectives
•Understand how probability is used in everyday life.
•Know how to compute a probability.
•Understand and be able to apply the multiplication rule.
•Understand and be able to apply the addition rule.
•Understand the relationship between the standard normal curve and probability.
In order to better understand the nature of probabilistic decisions, consider the following court case of The People v. Collins, 1968. In this case, the robbery victim was unable to identify his assailant. All that the victim could recall was that the assailant was female with a blonde pony tail. In addition, he remembered that she fled the scene in a yellow convertible that was driven by an African American male who had a full beard. The suspect in the case fit the.
Code Fast, die() Early, Throw Structured ExceptionsJohn Anderson
Slides from a short talk given at January 2012 DC.pm. Covers "classic" exceptions in Perl as well as some libraries to make working with exceptions easier.
Similar to The Ludic Fallacy Applied to Automated Planning (20)
This talk, delivered at the Høgskolen i Bergen (Bergen College) in Norway in October 2014. It covers some recent games and deconstructs potential AI techniques that could* be used by these games to achieve this.
* Note that the author has no knowledge of the internals of these games and this is broadly educated speculation.
What's with all the zombies in games right now? In this session, I talk about some of the reasons that Zombies are a lazy AI Engineers dream come true and what we could be doing instead
Around three years ago I took my first steps into the games industry. Now I'm reasonably well known, recognised as an expert in my area and get to present at conferences around the world. The things that have helped me achieve that though aren't all that hard, and in this talk I discuss some of the tools I've used to become who I am, as well as talking a lot about my own insecurities and those that many other developers were able to share with me.
Procedural Processes - Lessons Learnt from Automated Content Generation in "E...Luke Dicken
In this talk, given at the 2012 No Show Conference, and alongside long-term partner in crime Heather Decker-Davis, we talk about our game "Easy Money?" and our approach to content generation - along with the challenges they provided and the way it affected our workflow.
This session was given to the small group of students at University of Strathclyde participating in the after-hours Game Development program. It covers different roles within the industry and a number of different aspects of the kinds of teams you will need in order to make a game.
Game AI 101 - NPCs and Agents and Algorithms... Oh My!Luke Dicken
This is a session originally written for students at Bradley University (Peoria, IL).
It covers a very high level introduction to the concepts behind Game AI, and includes some examples of how we can begin to make characters in a game world perform actions and appear to be making intelligent decisions.
This session was the first in a series given to a group of University students of differing year groups and abilities. In this lecture, I try to highlight some of the many different aspects that need to be decided when thinking about how to make a game, and demonstrate that simply picking a genre is insufficient.
This is the 7th of an 8 lecture series that I presented at University of Strathclyde in 2011/2012 as part of the final year AI course.
This lecture covers ways that we can use AI to manage the experience that the player receives. Topics include Immersive Worlds, Player/Game Interactions, Interactive Fiction and "AI Directors" such as that found in Left4Dead
Lecture 6 - Procedural Content and Player ModelsLuke Dicken
This is the 6th of an 8 lecture series that I presented at University of Strathclyde in 2011/2012 as part of the final year AI course.
In this lecture I link together the material presented in lectures 3 and 4 on profiling players and show how this can be used to good effect with Procedural Content Generation (lecture 5). I use Silent Hill : Shattered Memories as a specific example, and discuss research using Tomb Raider, and the standard Bartle Player Types.
This is the 5th of an 8 lecture series that I presented at University of Strathclyde in 2011/2012 as part of the final year AI course.
In this lecture I outline some approaches that use AI techniques to automate the creation of content within game world. I make specific reference to assets such as rocks and plants, to interaction mechanisms such as weapons and to quest generating systems, in particular Skyrim's Radiant engine.
Lecture 8 - What is Game AI? Final ThoughtsLuke Dicken
This is the last lecture in the series series that I presented at University of Strathclyde in 2011/2012 as part of the final year AI course.
In this lecture I rehash the fundamental differences between Game AI and the traditional AI that has been taught in previous courses. It also includes a (frankly time-filling) section called the "Brain Dump" where I briefly touch on a bunch of things I was thinking about at the time.
This is the 3rd of an 8 lecture series that I presented at University of Strathclyde in 2011/2012 as part of the final year AI course.
This lecture moves beyond the Game Theoretic definition of a game, and demonstrates how algorithms can be used not only to find a single good choice, but a sequence of choices that will eventually reach a winning state.
This is the 2nd of an 8 lecture series that I presented at University of Strathclyde in 2011/2012 as part of the final year AI course.
This lecture covers the fundamentals of probability theory, and is relatively basic to ensure that all students have a good grasp on the concept.
This is the first of an 8 lecture series that I presented at University of Strathclyde in 2011/2012 as part of the final year AI course.
This lecture introduces the concept of a game, and the branch of mathematics known as Game Theory.
This is the 4th of an 8 lecture series that I presented at University of Strathclyde in 2011/2012 as part of the final year AI course.
This lecture shows how we can use mathematical analysis to classify players into stereotypes and leverage this classification into generating more successful decisions.
(Some content appears to be missing from the end of this one - I'll fix this as soon as I can)
Influence Landscapes - From Spatial to Conceptual RepresentationsLuke Dicken
These slides are from a presentation of a paper from AISB 2011. They lay out the concept of the Influence Landscape, a technique which uses Automated Planning tools to apply Influence Map-style representations to conceptual as well as spatial representations
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.
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/
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
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.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
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 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
6. Example
• Suppose you flip a coin, what is the chance it comes
up heads?
• 50/50
• Suppose you flip the coin 100 times and the first 99
were tails. What is the chance of the final flip giving
heads?
7. Example
• Suppose you flip a coin, what is the chance it comes
up heads?
• 50/50
• Suppose you flip the coin 100 times and the first 99
were tails. What is the chance of the final flip giving
heads?
• Independent variables, still 50/50.
8. Example
• Suppose you flip a coin, what is the chance it comes
up heads?
• 50/50
• Suppose you flip the coin 100 times and the first 99
were tails. What is the chance of the final flip giving
heads?
• Independent variables, still 50/50.
• ...or is it?
11. Origins
• Originally postulated by Nassim Nicholas
Taleb in "The Black Swan".
• Broadly, the ability to describe the outcomes
of events gives an impression of control. It
does not give ACTUAL control of the events.
12. Origins
• Originally postulated by Nassim Nicholas
Taleb in "The Black Swan".
• Broadly, the ability to describe the outcomes
of events gives an impression of control. It
does not give ACTUAL control of the events.
• A complex but inaccurate model is most
importantly inaccurate.
15. "Gambling With the
Wrong Dice"
• Case Study based on Las Vegas casino.
• Extensive and sophisticated systems and models
to account for potential cheating.
16. "Gambling With the
Wrong Dice"
• Case Study based on Las Vegas casino.
• Extensive and sophisticated systems and models
to account for potential cheating.
• Aim was to manage risk.
17. "Gambling With the
Wrong Dice"
• Case Study based on Las Vegas casino.
• Extensive and sophisticated systems and models
to account for potential cheating.
• Aim was to manage risk.
• But the vast majority of losses came from non-
gambling activity : a disgruntled ex-employee,
onstage accidents, failure to file correct paperwork
and a kidnap ransom.
20. Blinded By Probability
• Because we see numbers as solvable, we
focus on solving them.
• Lose sight of the broader picture.
21. Blinded By Probability
• Because we see numbers as solvable, we
focus on solving them.
• Lose sight of the broader picture.
• The "game" becomes our main focus rather
than the world it represents.
24. Back to Coins
• We flip 99 times, all tails.
• 0.5^99 = 1.8x10^-30
25. Back to Coins
• We flip 99 times, all tails.
• 0.5^99 = 1.8x10^-30
• Which is more likely, this highly improbable event is
happening, or the assumptions that we used to build
the model don't hold true?
26. Back to Coins
• We flip 99 times, all tails.
• 0.5^99 = 1.8x10^-30
• Which is more likely, this highly improbable event is
happening, or the assumptions that we used to build
the model don't hold true?
• Is the coin fair?
27. Back to Coins
• We flip 99 times, all tails.
• 0.5^99 = 1.8x10^-30
• Which is more likely, this highly improbable event is
happening, or the assumptions that we used to build
the model don't hold true?
• Is the coin fair?
• What actually is the probability of getting heads next?
29. Off-model
Consequences
• When we have a model, we risk getting blinkered
into thinking about the model instead of the world.
30. Off-model
Consequences
• When we have a model, we risk getting blinkered
into thinking about the model instead of the world.
• But models are abstract representations.
31. Off-model
Consequences
• When we have a model, we risk getting blinkered
into thinking about the model instead of the world.
• But models are abstract representations.
• No PDDL model describes the effect of a meteorite
hitting a robot, yet it is an (unlikely) possibility.
32. Off-model
Consequences
• When we have a model, we risk getting blinkered
into thinking about the model instead of the world.
• But models are abstract representations.
• No PDDL model describes the effect of a meteorite
hitting a robot, yet it is an (unlikely) possibility.
• Outcomes of actions, or events, cannot be fully
enumerated. There exist "off-model consequences"
34. Coins Again
• We talk about coins having a head and a tail side and
50/50 chance of either.
35. Coins Again
• We talk about coins having a head and a tail side and
50/50 chance of either.
• This isn't strictly true - there's a third possibility we don't
model :
36. Coins Again
• We talk about coins having a head and a tail side and
50/50 chance of either.
• This isn't strictly true - there's a third possibility we don't
model :
• Edge
37. Coins Again
• We talk about coins having a head and a tail side and
50/50 chance of either.
• This isn't strictly true - there's a third possibility we don't
model :
• Edge
• This is Taleb's "Black Swan", highly unlikely but
theoretically possible events that are ignored.
38. Coins Again
• We talk about coins having a head and a tail side and
50/50 chance of either.
• This isn't strictly true - there's a third possibility we don't
model :
• Edge
• This is Taleb's "Black Swan", highly unlikely but
theoretically possible events that are ignored.
• A true Black Swan must also be "high impact"
42. Probabilistic Planning
• PPDDL is a prime example of "doing it wrong"
• Extends PDDL by applying probabilities to
sets of effects. P(X=i) I occurs, P(X=j) J
occurs etc.
43. Probabilistic Planning
• PPDDL is a prime example of "doing it wrong"
• Extends PDDL by applying probabilities to
sets of effects. P(X=i) I occurs, P(X=j) J
occurs etc.
• Is the world really so cut and dry? Or is this
simply shoehorning probabilities into PDDL in
the most obvious way possible.
47. Summary
• Models are typically incomplete.
• Models are frequently wrong.
• Probabilistic models make even more assumptions!
48. Summary
• Models are typically incomplete.
• Models are frequently wrong.
• Probabilistic models make even more assumptions!
• We allow ourselves to be deceived by numbers into
believing we can quantify the unquantifiable.
49. Summary
• Models are typically incomplete.
• Models are frequently wrong.
• Probabilistic models make even more assumptions!
• We allow ourselves to be deceived by numbers into
believing we can quantify the unquantifiable.
• As a result, we get bogged down solving a problem
that isn't necessarily reflective of the real world.
53. Introduce Noise
• Most basic approach is to add noise to
probabilistic models.
• If the model has P(x) = 0.2, test generated
plans at say P(x) = 0.2+-0.05
54. Introduce Noise
• Most basic approach is to add noise to
probabilistic models.
• If the model has P(x) = 0.2, test generated
plans at say P(x) = 0.2+-0.05
• Allows for a rudimentary "what happens if
these values are not spot on" check
57. Epsilon-separation of
states
• Similar concept to that used in temporal actions.
• In this case epsilon denotes a marginal probability
of transitioning between any pair of states.
58. Epsilon-separation of
states
• Similar concept to that used in temporal actions.
• In this case epsilon denotes a marginal probability
of transitioning between any pair of states.
• Still not ideal, but at least captures the possibility
of events changing the state in an undetermined
way.
59. Epsilon-separation of
states
• Similar concept to that used in temporal actions.
• In this case epsilon denotes a marginal probability
of transitioning between any pair of states.
• Still not ideal, but at least captures the possibility
of events changing the state in an undetermined
way.
• Somewhat analogous to Van Der Waals forces.
61. State Charts
• In the FSM family, State Charts frequently
used to represent interruptible processes e.g.
Embedded Systems
62. State Charts
• In the FSM family, State Charts frequently
used to represent interruptible processes e.g.
Embedded Systems
• One process interrupts the other, acts and the
the first can resume from its previous state.
63. State Charts
• In the FSM family, State Charts frequently
used to represent interruptible processes e.g.
Embedded Systems
• One process interrupts the other, acts and the
the first can resume from its previous state.
• Can we use this model to capture the
consequences of unmodelled events?
66. Abstract / Anonymous
Actions
• In Prolog _ represents the anonymous variable.
• Nothing analogous to this in PDDL.
67. Abstract / Anonymous
Actions
• In Prolog _ represents the anonymous variable.
• Nothing analogous to this in PDDL.
• Would introducing this give us flexibility to
patch plans when off-model events occur?
68. Abstract / Anonymous
Actions
• In Prolog _ represents the anonymous variable.
• Nothing analogous to this in PDDL.
• Would introducing this give us flexibility to
patch plans when off-model events occur?
• Could this be used for actions (perhaps based
on DTG clusterings) be useful for this?