"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.
Farshad Kheir is the Head of AI and Data Science at Legion Technologies, an industry leader for AI-powered, machine-learning workforce management products. The company uses advanced technology to solve some of the biggest WFM business challenges while creating an employee experience that helps to attract and retain employees. What initially attracted you to computer science and engineering? I learned programming through online courses, as well as some on-campus classes. My background is in electrical engineering, but I have a minor in math, stochastic processes, and probability.
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To the right, search space, where all states with the same initial-state distance (g) and estimated goal distance (h) are represented by a single binary decision diagram (to the left), and only those whose g h solution cost need to be considered. Daniel Fišer, Álvaro Torralba and Joerg Hoffmann won an outstanding paper runners-up award at AAAI 2022 for their paper Operator-potential heuristics for symbolic search. Here, Álvaro tells us more about the field of optical planning, their methodology, and how potential heuristics can be used in symbolic search with very positive results. At a very general level, the research is on automated planning. This is a sub-area of AI where we try to answer the question: what is the best way to act given our knowledge of the world?
Check out what's clicking on Foxnews.com. Vice President Kamala Harris is being considered a "close contact" to President Biden, who tested positive for COVID on Thursday morning, according to a White House official. A White House official told Fox News there are no changes being made to Harris' schedule. She tested negative for COVID Thursday morning. Harris was at the 2022 international meeting of the Omega Psi Phi fraternity in Charlotte, North Carolina, on Thursday.
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Transit app Moovit is aiming to be more helpful when it comes to helping users get to their destination. Starting today, the app is rolling out more personalized trip-planning features in 3,500 cities across 112 countries to build on its existing route suggestions. One of new functions is called Smart Cards. Intel-owned Moovit will populate travel suggestions on the home screen based on factors such as your location, the time of day and week, your previous activity and items you mark as favorites. For instance, if you're out and about and you have your home set as a favorite destination, Moovit will automatically suggest the best transit options to get back there.
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Knowing how much and which kind of inventory your brand needs involves a complex web of data that companies often keep up with via spreadsheets or legacy systems that don't provide a full picture of the business. Syrup Tech, now armed with $6.3 million in new funding, is feeding all that data, like transactions, marketing and inventory, and combining it with other data, like social media trends and even the weather, to spit out predictive inventory recommendations using artificial intelligence and machine learning. This way, merchandisers and planners have better information on what they need and can reduce some of the waste. "I was at McKinsey previously, and was shocked to see merchandisers spend hours on spreadsheets," James Theuerkauf, co-founder and CEO of Syrup Tech, told TechCrunch. "My thought was to let the AI do the number-crunching and let the merchandiser make the creative decisions using the AI as support."
A declarative action model is a compact representation of the state transitions of dynamic systems that generalizes over world objects. The specification of declarative action models is often a complex hand-crafted task. In this paper we formulate declarative action models via state constraints, and present the learning of such models as a combinatorial search. The comprehensive framework presented here allows us to connect the learning of declarative action models to well-known problem solving tasks. In addition, our framework allows us to characterize the existing work in the literature according to four dimensions: (1) the target action models, in terms of the state transitions they define; (2) the available learning examples; (3) the functions used to guide the learning process, and to evaluate the quality of the learned action models; (4) the learning algorithm. Last, the paper lists relevant successful applications of the learning of declarative actions models and discusses some open challenges with the aim of encouraging future research work.
The last decade has seen companies operating under increasing levels of disruption. Quickly changing customer preferences, as well as demand uncertainty and disruptions, are challenging planning systems to unprecedented degrees. National security interests, trade barriers, and logistics disruptions are pushing businesses to find alternatives to globalized supply chains. Major swings in demand are calling for drastic operational and capital cost reduction in some areas and rapid growth in others. Physical distancing and remote work are forcing manufacturers to reconfigure manufacturing flows and management.