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planning & scheduling


Allen School News » Taskar Center launches first mobile version of AccessMap pedestrian trip planning tool for Android and iOS

University of Washington Computer Science

There are many options for mapping and route planning on a smartphone, but one thing they all have in common is their car-centric nature. Those apps that do support pedestrian navigation tend to make assumptions about a user that are at best inaccurate, and at worst dangerous. The app, which was developed by the Taskar Center for Accessible Technology housed at the Paul G. Allen Center of Computer Science & Engineering at the University of Washington and is based off of the web-based tool of the same name, enables users of Android and iOS in the cities of Seattle, Bellingham and Mount Vernon to generate customized walking directions on the go based on their own mobility needs and preferences. The app's release coincides with the International Day of Persons with Disabilities, an annual observance initiated by the United Nations to promote the rights and well-being of persons with disabilities in all spheres of society, including political, social, economic and cultural life. "Many apps offer some semblance of pedestrian directions, but those directions assume a user profile that ignores the lived experience of a vast number of people," explained Anat Caspi, director of the Taskar Center.


Exploring ROS2 with a wheeled robot – #4 – Obstacle avoidance

Robohub

In this post you'll learn how to program a robot to avoid obstacles using ROS2 and C . Before anything else, make sure you have the rosject from the previous post, you can copy it from here. Launch the simulation in one webshell and in a different tab, checkout the topics we have available. The obstacle avoidance intelligence goes inside the method calculateVelMsg. This is where decisions are made based on the laser readings.


'Pink-ball Tests favour England' - Johnson worry over possible Ashes schedule change

BBC News

Changing the Ashes schedule to include a second pink-ball Test would give England an advantage, says former Australia bowler Mitchell Johnson.


Goal Setting : Ultimate Story Based Course

#artificialintelligence

Goal setting seems like a no brainer to achieve any Great Goal! Yet it is seldom effectively practiced. The Key is not about learning complex concepts but rather developing DEEP understanding about simple yet effective methods. What better way to learn this other than through a story? In this 6 Episode series, let us learn various aspects of Goal setting in an extremely effective manner by walking along with Billy as he learns powerful techniques that truly revolutionize his life.


Application of Fuzzy Set Theory to Setup Planning

#artificialintelligence

Computer-aided process planning and computer-aided fixture planning have been widely researched in the last two decades. Most of these computer-aided systems are, however, either dealing only with process planning or fixture design. A set-up planning system for the machining of prismatic parts on a 3-axis vertical machining centre is proposed. This system formulates set-up plans based on the initial, intermediate and final states of a part. The system uses the fuzzy set representation, along with production rules and object representation.


Quinyx helps you get the most out of your workforce with a scheduling and engagement management solution.

#artificialintelligence

The struggle is real when it comes to workforce scheduling. It involves juggling many considerations from staff availability, to dealing with last-minute unforeseen circumstances such as sudden illness or shift swap requests, and then there's the ever-present spectre of labour law compliance to factor in. As countries are hesitantly re-opening, specific guidelines related to crowd control and hygiene may be implemented to minimize the chance of re-outbreak – adding to already hefty considerations when planning the staff duty roster. Timetabling and timetable-replotting is a nightmare if rotas are done with pen and paper, but a dream when done with the right piece of technology. Our focus today is on just such a platform – workforce management software Quinyx. "Organizations need to be flexible and adapt to the latest central and local guidelines," Quinyx CEO and founder Erik Fjellborg told the Swedish Chamber of Commerce for the UK last year.


The human side of IT automation - The AI Journal

#artificialintelligence

IT automation is the new normal. With the market for automation technologies ready to exceed $20 billion in 2022, automation is already playing a considerable role in business operations from invoice processing to customer support, as well as IT operations like deploying systems and automating recovery. But the area continues to grow, unlocking new opportunities to automate that depend on previous initiatives. According to Gartner, by 2023 most organisations will be able to automate an additional 25% of their tasks on top of those they have already automated. Until relatively recently, automation was relegated to the most mundane of tasks and used only by companies with extensive IT capabilities.


sbp-env: Sampling-based Motion Planners' Testing Environment

arXiv.org Artificial Intelligence

Sampling-based motion planners' testing environment (sbp-env) is a full feature framework to quickly test different sampling-based algorithms for motion planning. sbp-env focuses on the flexibility of tinkering with different aspects of the framework, and had divided the main planning components into two categories (i) samplers and (ii) planners. The focus of motion planning research had been mainly on (i) improving the sampling efficiency (with methods such as heuristic or learned distribution) and (ii) the algorithmic aspect of the planner using different routines to build a connected graph. Therefore, by separating the two components one can quickly swap out different components to test novel ideas.


A Preliminary Case Study of Planning With Complex Transitions: Plotting

arXiv.org Artificial Intelligence

Plotting is a tile-matching puzzle video game published by Taito in 1989. Its objective is to reduce a given grid of coloured blocks down to a goal number or fewer. This is achieved by the avatar character repeatedly shooting the block it holds into the grid. Plotting is an example of a planning problem: given a model of the environment, a planning problem asks us to find a sequence of actions that can lead from an initial state of the environment to a given goal state while respecting some constraints. The key difficulty in modelling Plotting is in capturing the way the puzzle state changes after each shot. A single shot can affect multiple tiles directly, and the grid is affected by gravity so numerous other tiles can be affected indirectly. We present and evaluate a constraint model of the Plotting problem that captures this complexity. We also discuss the difficulties and inefficiencies of modelling Plotting in PDDL, the standard language used for input to specialised AI planners. We conclude by arguing that AI planning could benefit from a richer modelling language.


Width-based Lookaheads with Learnt Base Policies and Heuristics Over the Atari-2600 Benchmark

arXiv.org Artificial Intelligence

We propose new width-based planning and learning algorithms inspired from a careful analysis of the design decisions made by previous width-based planners. The algorithms are applied over the Atari-2600 games and our best performing algorithm, Novelty guided Critical Path Learning (N-CPL), outperforms the previously introduced width-based planning and learning algorithms $\pi$-IW(1), $\pi$-IW(1)+ and $\pi$-HIW(n, 1). Furthermore, we present a taxonomy of the Atari-2600 games according to some of their defining characteristics. This analysis of the games provides further insight into the behaviour and performance of the algorithms introduced. Namely, for games with large branching factors, and games with sparse meaningful rewards, N-CPL outperforms $\pi$-IW, $\pi$-IW(1)+ and $\pi$-HIW(n, 1).