Planning & Scheduling


3 Ways AI Simplifies Workforce Management And Improves Team Morale

#artificialintelligence

In today's digital world, most enterprises are handling huge volumes of enterprise data. Sifting through it to locate the one nugget of information you need can be so onerous, many managers don't even try. They're already busy coordinating hectic employee time-off requests, making last-minute schedules, sorting out performance reviews, and completing hundreds of other tasks to keep the business running day-to-day. They simply don't have time. To help a company's data work for -- rather than against -- them, teams are increasingly turning to artificial intelligence (AI).


Rocket Failure Astronauts Will Go Back Into Space: Russian Official

U.S. News

NASA has relied on Russian rockets to ferry astronauts to the space station since the United States retired its Space Shuttle program in 2011, although the agency has announced plans for a test flight carrying two astronauts on a SpaceX commercial rocket next April.


Technique could enable robots to navigate pushy crowds, congested streets

ZDNet

Robots are great at dealing with predictable environments, but human pedestrian behavior can be difficult to anticipate. That's especially true in the frenzy to catch the D train at rush hour. A group of MIT researchers is on the case and adding to a growing body of academic work aiming to give robots some of the tools we (at least those of us living in overcrowded cities) take for granted: Street intuition. In a paper entitled "Deep sequential models for sampling-based planning," the researchers outline a method of robot navigation that utilizes traditional path planning algorithms, which analyze a number of options in real time and select the optimal choice, with a neural network that learns over time by observing and interacting with people. The addition of a neural network remedies a problem with traditional path planning, which relies on a branching decision tree that evaluates environmental conditions.


Construction Scheduling: An Infusion of AI

#artificialintelligence

AI (artificial intelligence) is perhaps one of the biggest trends to watch in the months to come, with many analysts predicting growth and technology providers making big moves in this area. PwC even suggests that global GDP will be 14% higher in 2030 as a result of AI, which is the equivalent of an additional $15.7 trillion. One big area in construction that is set to change is scheduling, with a new acquisition that happened this week. InEight announced it acquired BASIS, a company that purpose-built an AI planning software tool for the construction industry. The software captures insights and learnings from prior projects and uses the knowledge to make informed suggestions during the planning process.


Construction Scheduling: An Infusion of AI

#artificialintelligence

AI (artificial intelligence) is perhaps one of the biggest trends to watch in the months to come, with many analysts predicting growth and technology providers making big moves in this area. PwC even suggests that global GDP will be 14% higher in 2030 as a result of AI, which is the equivalent of an additional $15.7 trillion. One big area in construction that is set to change is scheduling, with a new acquisition that happened this week. InEight announced it acquired BASIS, a company that purpose-built an AI planning software tool for the construction industry. The software captures insights and learnings from prior projects and uses the knowledge to make informed suggestions during the planning process.


A New Way to Accelerate Your AI Plans - THINK Blog

#artificialintelligence

Building artificial intelligence (AI) systems involves more than learning how to perform a specific task from data; it requires a strong data foundation and infrastructure architecture. This foundation, as my colleagues have said on this blog many times, assists organizations large and small as they scale the Ladder to AI. As CDO of this great company, I spend a lot of time architecting data plans for our expansive global enterprise that spans 170 countries. As we grow, and as our data volumes grow, it was only natural that we would increasingly rely on the predictive, automated, and truly cognitive capabilities of AI to help manage and extract as much value from these volumes as we could. And we're getting more recognized for it.


Procedural Puzzle Challenge Generation in Fujisan

arXiv.org Artificial Intelligence

Challenges for physical solitaire puzzle games are typically designed in advance by humans and limited in number. Alternately, some games incorporate stochastic setup rules, where the human solver randomly sets up the game board before solving the challenge, which can greatly increase the number of possible challenges. However, these setup rules can often generate unsolvable or uninteresting challenges. To better understand these setup processes, we apply a taxonomy for procedural content generation algorithms to solitaire puzzle games. In particular, for the game Fujisan, we examine how different stochastic challenge generation algorithms attempt to minimize undesirable challenges, and we report their affect on ease of physical setup, challenge solvability, and challenge difficulty. We find that algorithms can be simple for the solver yet generate solvable and difficult challenges, by constraining randomness through embedding sub-elements of the puzzle mechanics into the physical pieces of the game.


Refining Manually-Designed Symbol Grounding and High-Level Planning by Policy Gradients

arXiv.org Artificial Intelligence

Hierarchical planners that produce interpretable and appropriate plans are desired, especially in its application to supporting human decision making. In the typical development of the hierarchical planners, higher-level planners and symbol grounding functions are manually created, and this manual creation requires much human effort. In this paper, we propose a framework that can automatically refine symbol grounding functions and a high-level planner to reduce human effort for designing these modules. In our framework, symbol grounding and high-level planning, which are based on manually-designed knowledge bases, are modeled with semi-Markov decision processes. A policy gradient method is then applied to refine the modules, in which two terms for updating the modules are considered. The first term, called a reinforcement term, contributes to updating the modules to improve the overall performance of a hierarchical planner to produce appropriate plans. The second term, called a penalty term, contributes to keeping refined modules consistent with the manually-designed original modules. Namely, it keeps the planner, which uses the refined modules, producing interpretable plans. We perform preliminary experiments to solve the Mountain car problem, and its results show that a manually-designed high-level planner and symbol grounding function were successfully refined by our framework.


Google: Your hotel, flight planning just got much easier with mobile search

ZDNet

Following Google's updates to make search more visual and faster on mobile, the company will soon roll out new time-saving features in mobile search for planning travel. The company is aiming for Search to be the go-to app for all travel-planning needs by cutting down time spent on preparing for trips using mobile. Even before anything's booked, Search will offer suggestions, such as things to do, and then as flight and hotel confirmations roll into Gmail, it will use these to personalize trip recommendations in search results. Google is also building on the travel-planning features it introduced to mobile search earlier this year, which added a new hotel search tab next to flight search, as well as a Your Trips feature that contains a summary of future and past travel reservations. Your Trips can be accessed in Search by typing in'my trips' or tapping the Your Trips tab from Google Flights or Hotels.


How Machine Learning and IoT Transforms Field Workforce Management

#artificialintelligence

Machine learning has begun to transform the world as we know it. Everywhere around you, you are'connected' to something. Now with Google Echo and Alexa, you are'connected' even when you are relaxing on your couch. An average person is connected to at least 3 devices and multiple accounts through them, at any given time. This network of connectivity is denser than ever and is doubling every year.