"Planning is the process of generating (possibly partial) representations of future behavior prior to the use of such plans to constrain or control that behavior. The outcome is usually a set of actions, with temporal and other constraints on them, for execution by some agent or agents. As a core aspect of human intelligence, planning has been studied since the earliest days of AI and cognitive science. Planning research has led to many useful tools for real-world applications, and has yielded significant insights into the organization of behavior and the nature of reasoning about actions."
– Planning entry by Austin Tate in the MIT Encyclopedia of Cognitive Science.
MELVILLE, N.Y., November 18, 2019 – Effectively managing today's workforce is crucial for improving customer experience, operational efficiency, and compliance. Yet currently, rising expectations of both customers and employees have made forecasting and scheduling contact center agents and customer engagement resources exponentially more challenging. To give companies a simpler way to manage work across the enterprise, Verint Systems Inc. (Nasdaq: VRNT), The Customer Engagement Company, today announced the newest release of its market-leading Workforce Management (WFM) solution, which leverages artificial intelligence-infused automation and new mobile tools to streamline forecasting and scheduling and improve employee engagement, all easily accessible via the Verint Cloud. "The workforce represents up to 80 percent of overall contact center budgets so accurate and cost-effective scheduling is vital," says Verint's John Goodson, SVP and general manager, Products. "At the same time, today's employees demand easier flex scheduling options, so organizations must balance flexibility and cost to provide superior service. As a pioneer in WFM, we view this new release as one that can invigorate the market to meet the ever-changing demands of today's contact centers and throughout the enterprise."
CPG executives are painfully aware that they're investing billions of dollars in trade promotions each year, but as many as 72% fail to break even¹. It's clear that promotions have become more complex and harder to manage as CPGs must respond to changing consumer behavior, increasing demands from retailers and blurring of physical and online channels. Traditional forecasting and promotion-planning systems are unable to provide real-time, accurate insights to help managers understand the big picture. Below, we'll explore seven ways in which AI can help CPG companies more effectively plan promotional events, measure outcomes and make adjustments. You can read more in the companion paper on how AI transforms promotional trade funds management.
About 87% of marketing organizations have already started using some level of personalization. By 2024, AI identification of emotions is expected to influence more than 50% of online advertisements globally. Gartner has confirmed that AEI is among the key technology trends that are expected to witness tremendous growth in the next five years. Computer vision allows AI to interpret and manipulate physical environments, which is one of the key technologies used for emotion recognition. Artificial Emotional Intelligence (AEI) will sense customer emotions, based on which companies can influence buying decisions.
Connected devices and the subsequent birth of the Internet of Things (IoT) have provided businesses a veritable treasure trove of data to mine, understand, and reapply to their own processes. And thus, the Industrial Internet of Things (IIoT) was born. Most enterprises today have only scratched the surface of what their connected device data can do for them. This is especially true for manufacturers, who have endless pieces of equipment, product and supply chains to manage on a day-to-day basis. In today's competitive landscape, there is a great opportunity for manufacturing companies to use the data from their connected devices and adopt and apply machine learning solutions in order to help solve their longstanding pain points of resource planning, machine maintenance, and supply chain management.
Q: Peter, for those who haven't heard of it, what is the best way to describe BoldIQ? A: At BoldIQ, we work towards an entirely demand-driven world, uninhibited by the constraints of supply, timing and disruption. This is a world where everything is available when and where customers want, and our role is to provide organizations with state-of-the-art technology to successfully deliver on that demand. BoldIQ's Solver is planning and scheduling optimization software that was initially developed by a private air charter company, and BoldIQ subsequently acquired that expertise. With Solver's success over the last ten years in the private air charter industry, this next-generation solution now also delivers advanced planning and scheduling for other mission-critical, cost-sensitive industries such as logistics, healthcare, modern transportation, construction and others.
The International Planning Competition Series began in 1998 and has been running biennially since that date. The competitions have been an important driver for research in the field. In particular, they have resulted in an evolving language for describing planning domains and problems (PDDL), a body of benchmark domains and problems in that language, and the ability to directly compare different generative planning techniques. All of this has contributed to significant advances in both the character and difficulty of problems that can be represented and solved by generative planning techniques. The papers in this JAIR Special Issue cover the 3rd International Planning Competition (IPC-3), held in conjunction with the 6th International Conference on AI Planning and Scheduling (AIPS-02).
The International Planning Competition Series began in 1998 and has been running biennially since that date. The competitions have been an important driver for research in the field. In particular, they have resulted in an evolving language for describing planning domains and problems (PDDL), a body of benchmark domains and problems in that language, and the ability to directly compare different generative planning techniques. All of this has contributed to significant advances in both the character and difficulty of problems that can be represented and solved by generative planning techniques. The papers in this JAIR Special Track cover the 4th International Planning Competition (IPC-4), held in conjunction with the 14th International Conference on Planning and Scheduling (ICAPS-04).
Finding the optimum path for a robot for moving from start to the goal position through obstacles is still a challenging issue. Thi s paper presents a novel path planning method, named D - point trigonometric, based on Q - learning algorithm for dynamic and uncertain environments, in which all the obstacles and the target are moving. We define a new state, action and reward functions for t he Q - learning by which the agent can find the best action in every state to reach the goal in the most appropriate path. Moreover, the experiment s in Unity3D confirmed the high convergence speed, the high hit rate, as well as the low dependency on environmental parameters of the proposed method compared with an opponent approach. The planning has been considered as a challenging concern in video games , transportation systems , and mobile robots   . A s the most important path planning issues, w e can refer to the dynamics and the uncertainty of the environment, the smoothness and the length of the path, obstacle avoidance, and the computation al cost . In the last few decades, researchers have done numerous research efforts to present new approaches to solve them     . Generally, most of the path planning approaches are categorized to one of the following methods   : ( 1) Classical methods (a) Computational geometry (CG) (b) Probabilistic r oadmap (PRM) (c) Potential fields method (PFM) ( 2) Heuristic and meta heuristic methods (a) Soft computing (b) Hybrid algorithms Since the complexity and the execution time of CG methods were high , PRMs were proposed to red uce the search space using techniques like milestones  .
Artificial intelligence (AI) continues to draw a lot of attention as companies and technology vendors look at how machine learning could improve supply chain operations. In particular demand planning, understood here as the process of developing forecasts that will drive operational supply chain decisions, is being touted as the next potential field for innovation. Technology giants like Amazon and Microsoft have announced AI tools for improving demand planning, and several consulting companies are promoting their skills to bring AI to companies' demand planning processes. In fact, a recent survey by the Institute of Business Forecasting and Planning (IBF) identified AI as the technology that will have the largest impact on demand planning in the next seven years.1 It's not hard to see the fit between AI and demand planning. Demand planning involves lots of number crunching and data analytics, and it is repeated cycle after cycle.
Task-Motion Planning for Navigation in Belief Space Antony Thomas, Fulvio Mastrogiovanni, and Marco Baglietto Abstract We present an integrated Task-Motion Planning (TMP) framework for navigation in large-scale environment. Autonomous robots operating in real world complex scenarios require planning in the discrete (task) space and the continuous (motion) space. In knowledge intensive domains, on the one hand, a robot has to reason at the highest-level, for example the regions to navigate to; on the other hand, the feasibility of the respective navigation tasks have to be checked at the execution level. This presents a need for motion-planning-aware task planners. We discuss a probabilistically complete approach that leverages this task-motion interaction for navigating in indoor domains, returning a plan that is optimal at the task-level. Furthermore, our framework is intended for motion planning under motion and sensing uncertainty, which is formally known as belief space planning. The underlying methodology is validated with a simulated office environment in Gazebo. In addition, we discuss the limitations and provide suggestions for improvements and future work. 1 Introduction Autonomous robots operating in complex real world scenarios require different levels of planning to execute their tasks. High-level (task) planning helps break down a given set of tasks into a sequence of sub-tasks. Actual execution of each of these sub-tasks would require low-level control actions to generate appropriate robot motions. In fact, the dependency between logical and geometrical aspects is pervasive in both task planning and execution.