Don't Trust The Maze Mac OS

One of the common ways to remember what you want to say or emphasize on a slide is to add speaking notes in the Notes section below a slide in PowerPoint. Then you can use the Presenter View mode to show you the slides and your notes while you only share the slides with the audience. In a meeting room this is usually the default approach when you connect to a projector or screen.

Now that meetings have moved to Zoom and will be using this technology in the future, how can you use PowerPoint Presenter View in a Zoom meeting to present your PowerPoint with your notes? Below you will find the answer, whether you have one or two screens/monitors and whether you use Windows or a Mac. I'll also explain why you might need three screens and how to set up that option. You can also learn expert tips for using Presenter View such as a quick way to jump to any slide without the audience seeing it and how you can zoom in on a slide while presenting in this article and video (if you are presenting with just one screen, this article and video will show you how to use some of these advanced Presenter View features).

Maze is a Mac OS X implementation of the classic maze screen saver module seen on various platforms. It works with the Mac OS X built in screen saver. Behavioral Detection and Prevention of Malware on OS X Security on OSX and iOS. Slides; Thunderstrike. Video, hacking Mac's extensible firmware interface (EFI) Direct Memory Attack the Kernel Don't trust your eye, Apple graphics is compromised. Security flaws in IOKit's graphics acceleration that lead to exploitation from the browser. 2 thoughts on ' Fix persistent invalid certificate errors in OS X ' Michael Schmitt September 10, 2015 at 10:31 am. Don't forget the simple explanation: the site has made changes to the domains it uses, but the web browser has cached obsolete pages. So step 0 should be to clear the browser cache. On Safari you can do this from the Debug menu.

As a business professional, what are the biggest frustrations, challenges, or struggles you have creating & delivering virtual presentations? Let me know here so I can create articles and videos to solve the biggest issues. Thanks.

Windows – 2 screens, option A

Summary of steps

• Connect a second display (here are nine options for a second display) and extend your display
• Arrange the displays in Windows and PowerPoint to match the physical setup
• Start Presenter View in PowerPoint so the slides are on one screen and Presenter View is on the screen that has the webcam (so you are looking at the webcam while seeing your notes)
• In Zoom share the screen that has the slides on it
• Deliver your presentation

Full detailed article

I have an article with full details, including screen captures, on PowerPoint Presenter View with 2 screens in Windows.

Video

The steps are very similar to using 2 screens in Teams because sharing a screen is similar in Teams or Zoom. This video will show you how to set up a second screen in Windows using a monitor and give you the basic steps you need.

Windows – 2 screens, Option B

Why put your webcam above the screen? This article and video show you how to put the webcam between the two screens and use this setup to have your notes, Zoom videos, and chat on one screen while you slides are shared in the meeting from the other screen. The article is here and the video is below.

If you run into an issue when sharing the Slide Show window in Zoom with 2 screens, this article may help.

Windows – 1 screen

Summary of steps

Don't Trust The Maze Mac Os X

• Make sure the presentation is set to use full screen Slide Show
• Start Presenter View Preview by pressing Alt+F5
• In Zoom, share a portion of the screen from the Advanced sharing options
• Make the current slide larger in Presenter View and adjust the sharing rectangle so you just share the current slide portion of the screen in Zoom
• Deliver your presentation

Full detailed article

I have an article with full details, including screen captures, on PowerPoint Presenter View with 1 screen in Windows. I also have an article and video on how to smoothly transition from the gallery view into your presentation so the attendees don't see you adjusting what is shared on the screen.

Alternate method: Use a video capture card to create a 'virtual' second screen on your laptop and then you can use Presenter View with 2 screens. See the article and video on using a screen capture card as a 2nd screen.

Possibly the best approach: Use a Virtual Display Adapter Plug to create a ghost monitor that your laptop thinks is a real second screen. Then you can use full Presenter View by sharing the ghost monitor in the Teams meeting. This article explains how to use this plug to create a ghost monitor so you can use full Presenter View.

Video

Mac – 2 screens

Summary of steps

• Connect a second display (here are nine options for a second display) and extend your display
• Arrange the displays in the Mac OS and PowerPoint to match the physical setup
• Start Presenter View in PowerPoint so the slides are on one screen and Presenter View is on the screen that has the webcam (so you are looking at the webcam while seeing your notes)
• In Zoom share the screen that has the slides on it
• Deliver your presentation

Full detailed article

I have an article with full details, including screen captures, on PowerPoint Presenter View with 2 screens on a Mac.

Video

Mac – 1 screen

Summary of steps Teller me more mac os.

• Make sure the presentation is set to use full screen Slide Show
• Start Presenter View Preview by pressing Option+Return
• In Zoom, share a portion of the screen from the Advanced sharing options
• Make the current slide larger in Presenter View and adjust the sharing rectangle so you just share the current slide portion of the screen in Zoom
• Deliver your presentation

Full detailed article

I have an article with full details, including screen captures, on PowerPoint Presenter View with 1 screen on a Mac. I also have an article and video on how to smoothly transition from the gallery view into your presentation so the attendees don't see you adjusting what is shared on the screen.

Alternate method: Use a video capture card to create a 'virtual' second screen on your laptop and then you can use Presenter View with 2 screens. See the article and video on using a screen capture card as a 2nd screen.

Possibly the best approach: Use a Virtual Display Adapter Plug to create a ghost monitor that your laptop thinks is a real second screen. Then you can use full Presenter View by sharing the ghost monitor in the Teams meeting. This article explains how to use this plug to create a ghost monitor so you can use full Presenter View.

Video

3 screens

If you want to see the attendees videos and chat while using Presenter View in Zoom you will need three screens to do so. I have an article with a full explanation and instructions on how to set this up and use the three screens.

Windows

In this article for using Presenter View with 3 screens in Windows I use a Microsoft Wireless Display Adapter connected to a TV to create the third screen. In the video below I use another way to connect a third screen. If you have an iPad or iPhone you can use an app called Duet Display. It is a paid app and it allows you to connect your iPad or iPhone to the laptop via a USB cable and use it as another display.

Mac

In this article for using Presenter View with 3 screens on a Mac I use an Apple TV device connected to a TV to create a third screen. In the video below I use another way to connect a third screen. If you have an iPad or iPhone you can use an app called Duet Display. It is a paid app and it allows you to connect your iPad or iPhone to the laptop via a USB cable and use it as another display.

Use Google Slides Presenter View

Google Slides can read PowerPoint files and has a Presenter View that shows the slides in a browser window and your notes and slide preview in another window. This can be an alternative if you have one screen since you can share the browser window that has the slides in Zoom or Teams so the meeting attendees see just the slides while you can see the slides and your notes. This article shows you how to use Google Slides Presenter View in Zoom or Teams and the video below shows me demonstrate these steps.

Dave Paradi has over twenty years of experience delivering customized training workshops to help business professionals improve their presentations. He has written nine books and over 100 articles on the topic of effective presentations and his ideas have appeared in publications around the world. His focus is on helping corporate professionals visually communicate the messages in their data so they don't overwhelm and confuse executives. Dave is one of less than ten people in North America recognized by Microsoft with the Most Valuable Professional Award for his contributions to the Excel and PowerPoint communities. He regularly presents highly rated sessions at national and regional conferences of financial professionals.

Project 1: Search in Pacman

All those colored walls,
Mazes give Pacman the blues,
So teach him to search.

Introduction

In this project, your Pacman agent will find paths through his maze world, both to reach a particular location and to collect food efficiently. You will build general search algorithms and apply them to Pacman scenarios.

The code for this project consists of several Python files, some of which you will need to read and understand in order to complete the assignment, and some of which you can ignore. You can download all the code and supporting files (including this description) as a zip archive.

Files you'll edit:
search.py	Where all of your search algorithms will reside.
searchAgents.py	Where all of your search-based agents will reside.
Files you might want to look at:
pacman.py	The main file that runs Pacman games. This file describes a Pacman GameState type, which you use in this project.
game.py	The logic behind how the Pacman world works. This file describes several supporting types like AgentState, Agent, Direction, and Grid.
util.py	Useful data structures for implementing search algorithms.
Supporting files you can ignore:
graphicsDisplay.py	Graphics for Pacman
graphicsUtils.py	Support for Pacman graphics
textDisplay.py	ASCII graphics for Pacman
ghostAgents.py	Agents to control ghosts
keyboardAgents.py	Keyboard interfaces to control Pacman
layout.py	Code for reading layout files and storing their contents

What to submit: You will fill in portions of search.py and searchAgents.py during the assignment. These two files are the only .py files you should submit.

This assignment should be submitted with the assignment name cs343-1-search using these submission instructions.

Evaluation: Your code will be autograded for technicalcorrectness. Please do not change the names of any provided functions or classes within the code, or you will wreak havoc on the autograder. However, the correctness of your implementation -- not the autograder's output -- will be the final judge of your score. If necessary, we will review and grade assignments individually to ensure that you receive due credit for your work.

Academic Dishonesty: We will be checking your code against other submissions in the class for logical redundancy. If you copy someone else's code and submit it with minor changes, we will know. These cheat detectors are quite hard to fool, so please don't try. We trust you all to submit your own work only; please don't let us down. If you do, we will pursue the strongest consequences available to us.

Getting Help: You are not alone! If you find yourself stuck on something, contact the course staff for help. Office hours, discussion board, and the mailing list are there for your support; please use them. If you can't make our office hours, let us know and we will schedule more. We want these projects to be rewarding and instructional, not frustrating and demoralizing. But, we don't know when or how to help unless you ask. One more piece of advice: if you don't know what a variable does or what kind of values it takes, print it out.

Welcome to Pacman

After downloading the code (search.zip), unzipping it and changing to the search directory, you should be able to play a game of Pacman by typing the following at the command line:Pacman lives in a shiny blue world of twisting corridors and tasty round treats. Navigating this world efficiently will be Pacman's first step in mastering his domain.

The simplest agent in searchAgents.py is called the GoWestAgent, which always goes West (a trivial reflex agent). This agent can occasionally win:But, things get ugly for this agent when turning is required:Soon, your agent will solve not only tinyMaze, but any maze you want.Note that pacman.py supports a number of options that can each be expressed in a long way (e.g., --layout) or a short way (e.g., -l). You can see the list of all options and their default values via:Also, all of the commands that appear in this project also appear in commands.txt, for easy copying and pasting. In UNIX/Mac OS X, you can even run all these commands in order with bash commands.txt.

Finding a Fixed Food Dot using Search Algorithms

In searchAgents.py, you'll find a fully implemented SearchAgent, which plans out a path through Pacman's world and then executes that path step-by-step. The search algorithms for formulating a plan are not implemented -- that's your job. As you work through the following questions, you might need to refer to this glossary of objects in the code.First, test that the SearchAgent is working correctly by running:The command above tells the SearchAgent to use tinyMazeSearch as its search algorithm, which is implemented in search.py. Pacman should navigate the maze successfully.

Now it's time to write full-fledged generic search functions to help Pacman plan routes! Pseudocode for the search algorithms you'll write can be found in the lecture slides and textbook. Remember that a search node must contain not only a state but also the information necessary to reconstruct the path (plan) which gets to that state.

Important note: All of your search functions need to return a list of actions that will lead the agent from the start to the goal. These actions all have to be legal moves (valid directions, no moving through walls).

Hint: Each algorithm is very similar. Algorithms for DFS, BFS, UCS, and A* differ only in the details of how the fringe is managed. So, concentrate on getting DFS right and the rest should be relatively straightforward. Indeed, one possible implementation requires only a single generic search method which is configured with an algorithm-specific queuing strategy. (Your implementation need not be of this form to receive full credit).

Hint: Make sure to check out the Stack, Queue and PriorityQueue types provided to you in util.py!

Question 1 (2 points) Implement the depth-first search (DFS) algorithm in thedepthFirstSearch function in search.py. To make your algorithm complete, write the graph search version of DFS, which avoids expanding any already visited states (textbook section 3.3).

Your code should quickly find a solution for:The Pacman board will show an overlay of the states explored, and the order in which they were explored (brighter red means earlier exploration). Is the exploration order what you would have expected? Does Pacman actually go to all the explored squares on his way to the goal?

Hint: If you use a Stack as your data structure, the solution found by your DFS algorithm for mediumMaze should have a length of 130 (provided you push successors onto the fringe in the order provided by getSuccessors; you might get 244 if you push them in the reverse order). Is this a least cost solution? If not, think about what depth-first search is doing wrong.

Question 2 (1 point) Implement the breadth-first search (BFS) algorithm in thebreadthFirstSearch function in search.py. Again, write a graph search algorithm that avoids expanding any already visited states. Test your code the same way you did for depth-first search.Does BFS find a least cost solution? If not, check your implementation.

Hint: If Pacman moves too slowly for you, try the option --frameTime 0.

Note: If you've written your search code generically, your code should work equally well for the eight-puzzle search problem (textbook section 3.2) without any changes.

Varying the Cost Function

While BFS will find a fewest-actions path to the goal, we might want to find paths that are 'best' in other senses. Consider mediumDottedMaze and mediumScaryMaze. By changing the cost function, we can encourage Pacman to find different paths. For example, we can charge more for dangerous steps in ghost-ridden areas or less for steps in food-rich areas, and a rational Pacman agent should adjust its behavior in response.

Question 3 (2 points) Implement the uniform-cost graph search algorithm inthe uniformCostSearch function in search.py. You should now observe successful behavior in all three of the following layouts, where the agents below are all UCS agents that differ only in the cost function they use (the agents and cost functions are written for you):

Note: You should get very low and very high path costs for the StayEastSearchAgent and StayWestSearchAgent respectively, due to their exponential cost functions (see searchAgents.py for details).

A* search

Question 4 (3 points) Implement A* graph search in the empty function aStarSearch in search.py. A* takes a heuristic function as an argument. Heuristics take two argument: a state in the search problem (the main argument), and the problem itself (for reference information). The nullHeuristic heuristic function in search.py is a trivial example.

You can test your A* implementation on the original problem of finding a path through a maze to a fixed position using the Manhattan distance heuristic (implemented already as manhattanHeuristic in searchAgents.py). You should see that A* finds the optimal solution slightly faster than uniform cost search (about 549 vs. 620 search nodes expanded in our implementation, but ties in priority may make your numbers differ slightly). What happens on openMaze for the various search strategies?

Finding All the Corners

The real power of A* will only be apparent with a more challenging search problem. Now, it's time to formulate a new problem and design a heuristic for it. Five nightmare nights 4 mac os.

In corner mazes, there are four dots, one in each corner. Our new search problem is to find the shortest path through the maze that touches all four corners (whether the maze actually has food there or not). Note that for some mazes like tinyCorners, the shortest path does not always go to the closest food first! Hint: the shortest path through tinyCorners takes 28 steps.

Question 5 (2 points) Implement the CornersProblem search problem in searchAgents.py. You will need to choose a state representation that encodes all the information necessary to detect whether all four corners have been reached. Now, your search agent should solve: To receive full credit, you need to define an abstract state representation that does not encode irrelevant information (like the position of ghosts, where extra food is, etc.). In particular, do not use a Pacman GameState as a search state. Your code will be very, very slow if you do (and also wrong).

Hint: The only parts of the game state you need to reference in your implementation are the starting Pacman position and the location of the four corners.

Our implementation of breadthFirstSearch expands just under 2000 search nodes on mediumCorners. However, heuristics (used with A* search) can reduce the amount of searching required.

Question 6 (3 points) Implement a heuristic for the CornersProblem in cornersHeuristic. Grading: inadmissible heuristics will get no credit. 1 point for any admissible heuristic. 1 point for expanding fewer than 1600 nodes. 1 point for expanding fewer than 1200 nodes. Expand fewer than 800, and you're doing great!

Hint: Remember, heuristic functions just return numbers, which, to be admissible, must be lower bounds on the actual shortest path cost to the nearest goal.

Note:AStarCornersAgent is a shortcut for -p SearchAgent -a fn=aStarSearch,prob=CornersProblem,heuristic=cornersHeuristic.

Eating All The Dots

Now we'll solve a hard search problem: eating all the Pacman food in as few steps as possible. For this, we'll need a new search problem definition which formalizes the food-clearing problem: FoodSearchProblem in searchAgents.py (implemented for you). A solution is defined to be a path that collects all of the food in the Pacman world. For the present project, solutions do not take into account any ghosts or power pellets; solutions only depend on the placement of walls, regular food and Pacman. (Of course ghosts can ruin the execution of a solution! We'll get to that in the next project.) If you have written your general search methods correctly,

A* with a null heuristic (equivalent to uniform-cost search) should quickly find an optimal solution to testSearch with no code change on your part (total cost of 7).

Note:AStarFoodSearchAgent is a shortcut for -p SearchAgent -a fn=astar,prob=FoodSearchProblem,heuristic=foodHeuristic.

You should find that UCS starts to slow down even for the seemingly simple tinySearch. As a reference, our implementation takes 2.5 seconds to find a path of length 27 after expanding 4902 search nodes.

Question 7 (5 points) Fill in foodHeuristic

Don't Trust The Maze Mac Os Download

in searchAgents.py with a consistent heuristic for the FoodSearchProblem. Try your agent on the trickySearch board:Our UCS agent finds the optimal solution in about 13 seconds, exploring over 16,000 nodes. If your heuristic is admissible, you will receive the following score, depending on how many nodes your heuristic expands.

Fewer nodes than:	Points
15000	1
12000	2
9000	3 (medium)
7000	4 (hard)

If your heuristic is inadmissible, you will receive no credit, so be careful! Think through admissibility carefully, as inadmissible heuristics may manage to produce fast searches and even optimal paths. Can you solve mediumSearch in a short time? If so, we're either very, very impressed, or your heuristic is inadmissible.

Admissibility vs. Consistency?Technically, admissibility isn't enough to guarantee correctness in graph search -- you need the stronger condition of consistency. My first pico-8 mac os. For a heuristic to be consistent, it must hold that if an action has cost c, then taking that action can only cause a drop in heuristic of at most c. If your heuristic is not only admissible, but also consistent, you will receive 1 additional point for this question.

Almost always, admissible heuristics are also consistent, especially if they are derived from problem relaxations. Therefore it is probably easiest to start out by brainstorming admissible heuristics. Once you have an admissible heuristic that works well, you can check whether it is indeed consistent, too. Inconsistency can sometimes be detected by verifying that your returned solutions are non-decreasing in f-value. Morevoer, if UCS and A* ever return paths of different lengths, your heuristic is inconsistent (and inadmissible, too). This stuff is tricky. If you need help, don't hesitate to ask the course staff!

Suboptimal Search

Sometimes, even with A* and a good heuristic, finding the optimal path through all the dots is hard. In these cases, we'd still like to find a reasonably good path, quickly. In this section, you'll write an agent that always eats the closest dot. ClosestDotSearchAgent is implemented for you in searchAgents.py, but it's missing a key function that finds a path to the closest dot.

Question 8 (2 points) Implement the function findPathToClosestDot in searchAgents.py. Our agent solves this maze (suboptimally!) in under a second with a path cost of 350:

Hint: The quickest way to complete findPathToClosestDot is to fill in the AnyFoodSearchProblem, which is missing its goal test. Then, solve that problem with an appropriate search function. The solution should be very short!

Your ClosestDotSearchAgent won't always find the shortest possible path through the maze. (If you don't understand why, ask a TA!) In fact, you can do better if you try.

Don't Trust The Maze Mac Os Catalina

Mini Contest (2 points extra credit) Implement an ApproximateSearchAgent in searchAgents.py that finds a short path through the bigSearch layout. The three teams that find the shortest path using no more than 30 seconds of computation will receive 2 extra credit points and an in-class demonstration of their brilliant Pacman agents. We will time your agent using the no graphics option -q, and it must complete in under 30 seconds on our grading machines. Please describe what your agent is doing in a comment! We reserve the right to give additional extra credit to creative solutions, even if they don't work that well. Don't hard-code the path, of course.

Object Glossary

Here's a glossary of the key objects in the code base related to search problems, for your reference:

SearchProblem (search.py)

A SearchProblem is an abstract object that represents the state space, successor function, costs, and goal state of a problem. You will interact with any SearchProblem only through the methods defined at the top of search.py

PositionSearchProblem (searchAgents.py)

A specific type of SearchProblem that you will be working with --- it corresponds to searching for a single pellet in a maze.

CornersProblem (searchAgents.py)

A specific type of SearchProblem that you will define --- it corresponds to searching for a path through all four corners of a maze.

FoodSearchProblem (searchAgents.py)

A specific type of SearchProblem that you will be working with --- it corresponds to searching for a way to eat all the pellets in a maze.

Search Function

A search function is a function which takes an instance of SearchProblem as a parameter, runs some algorithm, and returns a sequence of actions that lead to a goal. Example of search functions are depthFirstSearch and breadthFirstSearch, which you have to write. You are provided tinyMazeSearch which is a very bad search function that only works correctly on tinyMaze

SearchAgent

SearchAgent is is a class which implements an Agent (an object that interacts with the world) and does its planning through a search function. The SearchAgent first uses the search function provided to make a plan of actions to take to reach the goal state, and then executes the actions one at a time.