If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
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.
Olis Robotics, a leader in next-generation AI-driven software for remote robotics in dynamic environments in subsea, terrestrial, and space applications, today announced that it has been selected by Maxar Technologies to provide robotic operator planning software for Maxar's Sample Acquisition, Morphology Filtering, and Probing of Lunar Regolith (SAMPLR) robotic arm. The arm will be mounted to a yet-to-be-named lander as one of 12 payloads that NASA selected as part of its Artemis program to send the first woman and the next man to the Moon by 2024 in preparation for a human mission to Mars. Olis Robotics' operator planning software will solve for the extreme latency experienced while operating robotics on the lunar surface by enabling operators to simulate and plan movements from the ground. Olis' software will provide a 3D visualization of the lunar environment and intuitive controls for operators on Earth, providing enhanced control during exploration missions. "The moon provides an excellent proving ground for our robotic operator planning software, allowing operators on Earth to successfully complete more complex missions faster and safer than ever before," explained Olis Robotics CEO Don Pickering.
Underground penetration and exploration technologies have a long history and can be exploited in many sectors, such as agriculture, for example, to define soil water content1; geology, for example, for terrain seismic profiling2 and underground characterization3; and the oil and gas industry4 or construction, for example, for mapping and maintenance of underground utility service infrastructures5 and tunneling.6 Autonomous solutions, which can monitor the surrounding environment, make decisions, and adjust their behavior for improving penetration and exploration, could help make the process faster, more reliable, cheaper, and safer for humans and underground infrastructures.7 However, robotic solutions for such applications are still very limited,8–13 due to the strong constraints imposed on the movement of autonomous systems below ground by the physics of such a cluttered environment (i.e., high pressure and friction, stratifications with different soil impedance, and rocks). Ideally, a robotic system moving in soil should be able to adapt its actions to unpredictable constraints, avoiding or navigating around obstacles or sensitive objects, for example, to prevent damaging underground pipes or objects of the cultural heritage. However, they have a limited possibility of perception compared to aboveground robots, which for instance can take advantage of vision. Thus, within the soil, a possible strategy for movement and exploration is for the morphology of the body to adapt itself to the soil structure. Morphological adaptation in artificial solutions has been particularly exploited in the field of soft-bodied robotic systems,14,15 where soft materials are adopted for the deformation of soft artificial bodies, for moving through small gates16,17 or navigating cluttered environments, for example, by exploiting the passive buckling ability of soft inflatable structures in a robot, without the use of a sensory perception or bending control.18 Material properties or soft actuators are used for enhancing robot abilities.19 In fact, the adaptation provided by soft materials and actuators can effectively improve robot behaviors while decreasing the control complexity.20,21