Planning & Scheduling

During 15-17 September, RaySearch will exhibit its latest advances in oncology software at the American Society for Radiation Oncology (ASTRO) 2019 annual meeting in Chicago, USA. On show will be new development in machine learning technology and automation in the RayStation* treatment planning system and the RayCare * oncology information system. Machine learning capabilities were added already in RayStation 8B and are being continuously improved. RaySearch has now been granted FDA 510(k) clearance for deep learning organ segmentation and for machine learning automated planning for a key model. Several planning models are being validated for future FDA 510(k) clearance.

During 15-17 September, RaySearch will exhibit its latest advances in oncology software at the American Society for Radiation Oncology (ASTRO) 2019 annual meeting in Chicago, USA. On show will be new development in machine learning technology and automation in the RayStation* treatment planning system and the RayCare * oncology information system. Machine learning capabilities were added already in RayStation 8B and are being continuously improved. RaySearch has now been granted FDA 510(k) clearance for deep learning organ segmentation and for machine learning automated planning for a key model. Several planning models are being validated for future FDA 510(k) clearance.

Invitae is a healthcare technology company that leverages genetic information to empower doctors and patients to make informed medical decisions. Our software engineers work on a variety of projects ranging from innovations in healthcare systems to taming the chaos of biology. We're constantly improving our tools and technologies to deliver the highest quality actionable information to doctors and patients. Invitae AI is seeking a computer scientist with expertise in AI planning and reinforcement learning to develop state of the art solutions to problems in process automation, recommender systems, and human-in-the-loop user interfaces. The desired candidate is a strong coder with professional software engineering experience as well as a scientist interested in developing and publishing state of the art methods with the Invitae AI team.

With Artificial Intelligence By Example, develop your own method for future AI solutions. Acquire advanced AI, machine learning, and deep learning design skills. Description Topics included: Become an Adaptive Thinker • Think like a Machine • Apply Machine Thinking to a Human Problem • Become an Unconventional Innovator • Manage the Power of Machine Learning and Deep Learning • Don't Get Lost in Techniques – Focus on Optimizing • Your Solutions • When and How to Use Artificial Intelligence • Revolutions Designed for Some Corporations and Disruptive • Innovations for Small to Large Companies • Getting Your Neurons to Work • Applying Biomimicking to Artificial Intelligence • Conceptual Representation Learning • Automated Planning and Scheduling • AI and the Internet of Things (IoT) • Optimizing Blockchains with AI • Cognitive NLP Chatbots • Improve the Emotional Intelligence Deficiencies of Chatbots • Quantum Computers That Think

Use the AI Planner package to create agents that generate and execute plans. For example, use AI Planner to create an NPC, generate storylines, or validate game/simulation mechanics. The AI Planner package also includes authoring tools and a plan visualizer. To install this package, follow the instructions in the Package Manager documentation. During execution, it is also useful to view an agent's plan through the plan visualizer.

At IHG we employ people who apply the same amount of care and passion to their jobs as they do their hobbies – people who put our guests at the heart of everything they do. And we're looking for more people like this to join our friendly and professional team. Key responsibilities of the role include: design and implementation of real time applications for use in a multi-platform environment; develop strategies for the continued planning, scheduling, and coordination of system tests for reliability, scalability, and maintainability and monitor test results; ensure that departmental standards are documented, distributed and updated on a regular basis for assigned systems development planning, product performance, support and monitoring; provide technical consultation in new systems development and enhancement of existing systems; participate in structured walkthroughs and technical reviews and act as advisor to Sr. level IT management concerning strategic decisions concerning legacy and new technology. Bachelor's or Master's Degree in a relevant field of work or an equivalent combination of education and work related experience. Typically reports to Manager, Information Technology.

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Continuous Relaxation of Symbolic Planner for One-Shot Imitation Learning De-An Huang 1, Danfei Xu 1, Y uke Zhu 1, Animesh Garg 1, 2, Silvio Savarese 1, Li Fei-Fei 1, Juan Carlos Niebles 1 Abstract -- We address one-shot imitation learning, where the goal is to execute a previously unseen task based on a single demonstration. While there has been exciting progress in this direction, most of the approaches still require a few hundred tasks for meta-training, which limits the scalability of the approaches. Our main contribution is to formulate one-shot imitation learning as a symbolic planning problem along with the symbol grounding problem. This formulation disentangles the policy execution from the inter-task generalization and leads to better data efficiency. The key technical challenge is that the symbol grounding is prone to error with limited training data and leads to subsequent symbolic planning failures. We address this challenge by proposing a continuous relaxation of the discrete symbolic planner that directly plans on the probabilistic outputs of the symbol grounding model. Our continuous relaxation of the planner can still leverage the information contained in the probabilistic symbol grounding and significantly improve over the baseline planner for the one-shot imitation learning tasks without using large training data. I NTRODUCTION We are interested in robots that can learn a wide variety of tasks efficiently. Recently, there has been an increasing interest in the one-shot imitation learning problem [1-7], where the goal is to learn to execute a previously unseen task from only a single demonstration of the task. This setting is also referred to as meta-learning [3, 8], where the meta-training stage uses a set of tasks in a given domain to simulate the one-shot testing scenario. This allows the learned model to generalize to previously unseen tasks with a single demonstration in the meta-testing stage. The main shortcoming of these one-shot approaches is that they typically require a large amount of data for meta-training (400 meta-training tasks in [4] and 1000 in [6] for the Block Stacking task [6]) to generalize reliably to unseen tasks.

Chief Design Officer InEight Dan Patterson founded BASIS, a company that developed an artificial intelligence (AI) planning software tool that was acquired by InEight in 2018. Following the acquisition, Dan became a member of InEight's executive leadership team. He now focuses on expanding upon his vision of creating next generation planning and scheduling software solutions for the construction industry. As a globally recognized project analytics thought leader and software entrepreneur, Dan has more than 20 years of experience building project management software companies, including Pertmaster and Acumen. Throughout his career, Dan has focused on solution innovation and project management, including advanced scheduling, risk management, project analytics and AI.

Explainable AI is an important area of research within which Explainable Planning is an emerging topic. In this paper, we argue that Explainable Planning can be designed as a service -- that is, as a wrapper around an existing planning system that utilises the existing planner to assist in answering contrastive questions. We introduce a prototype framework to facilitate this, along with some examples of how a planner can be used to address certain types of contrastive questions. We discuss the main advantages and limitations of such an approach and we identify open questions for Explainable Planning as a service that identify several possible research directions.