These magnificent rays are at risk of disappearing due to targeted fishing, being caught as bycatch, and climate change. Scientists at the research collaboration Mobula Conservation are teaming up with artisanal and industrial fishermen to protect them. Also known as "Devil Rays," mobulas are elasmobranchs: a subclass of fish--including sharks, skates, and sawfish--that are distinguished by having skeletons primarily made from cartilage. More than a third of the species in this group are threatened with extinction. Of the nine species of mobulas, seven are endangered and two are vulnerable according to the International Union for Conservation of Nature.

Can you imagine walking through a store and being served by a robot? Something like this will happen in the electronics department of the Palacio de Hierro located in Polanco, Mexico City. A robot developed by Intel will be the department store's new advisor, it will help users choose computers and other electronic devices. The humanoid combines artificial intelligence with the internet of things and cloud services. The robot has the ability to answer common questions through its voice interaction, as well as profile what each user will need and move to the correct counter to show the customer the product.

When the coronavirus outbreak first hit the Plaza Minorista market, Edison Palacio knew that it would take more than disinfectant and face masks to contain it. So he decided to use artificial intelligence. Mr Palacio is the director of the densely packed market which sits in the heart of the Colombian city of Medellín. Every day, up to 15,000 people flood into the giant building where more than 3,300 vendors sell fruits, vegetables, meats, eggs, spices, grains and clothes. They are a crucial link bringing food grown on farms to a metropolitan area of nearly four million people.

The main focus of our work is the use of classical planning algorithms in service of more complex problems of planning under uncertainty. In particular, we are exploring compilation techniques that allow us to reduce some probabilistic planning problems into variants of classical planning, such as metric planning,resource-bounded planning, and cost-bounded suboptimal planning. Currently, our initial work focuses on \emph{conformant probabilistic planning}. We intend toimprove our current methods by improving our compilation methods, but also by improving the ability of current planners to handle the special features ofour compiled problems. Then, we hope to extend these techniques to handle more complex probabilistic settings, such as problems with stochastic actions andpartial observability.

We describe a new sound and complete method for compiling contingentplanning problems with sensing actions into classical planning.Our method encodes conditional plans within a linear, classical plan.This allows our planner, MPSR, to reason about multiple future outcomes of sensingactions, and makes it less susceptible to dead-ends.MPRS, however, generates very large classical planningproblems. To overcome this, we use an incomplete variantof the method, based on state sampling, within an online replanner. On most current domains, MPSR finds plans faster, although its plans are often longer. But on a new challenging variant of Wumpus with dead-ends,it finds smaller plans, faster, and scales better.

We describe a new sound and complete method forcompiling contingent planning problems with sensingactions into classical planning. Our method encodesconditional plans within a linear, classicalplan. This allows our planner, MPSR, to reasonabout multiple future outcomes of sensing actions,and makes it less susceptible to dead-ends. MPRS,however, generates very large classical planningproblems. To overcome this, we use an incompletevariant of the method, based on state sampling,within an online replanner. On most currentdomains, MPSR finds plans faster, although itsplans are often longer. But on a new challengingvariant of Wumpus with dead-ends, it finds smallerplans, faster, and scales much better.

Finite-state controllers represent an effective action selection mechanisms widely used in domains such as video-games and mobile robotics. In contrast to the policies obtained from MDPs and POMDPs, finite-state controllers have two advantages: they are often extremely compact, and they are general, applying to many problems and not just one. A limitation of finite-state controllers, on the other hand, is that they are written by hand. In this paper, we address this limitation, presenting a method for deriving controllers automatically from models. The models represent a class of contingent problems where actions are deterministic and some fluents are observable. The problem of deriving a controller is converted into a conformant problem that is solved using classical planners, taking advantage of a complete translation into classical planning introduced recently. The controllers derived are ‘general’ in the sense that they do not solve the original problem only, but many variations as well, including changes in the size of the problem or in the uncertainty of the initial situation and action effects. Several experiments illustrating the automatic derivation of controllers are presented.

The selection of the action to do next is one of the central problems faced by autonomous agents. In AI, three approaches have been used to address this problem: the programming-based approach, where the agent controller is given by the programmer, the learning-based approach, where the controller is induced from experience via a learning algorithm, and the model-based approach, where the controller is derived from a model of the problem. Planning in AI is best conceived as the model-based approach to action selection. The models represent the initial situation, actions, sensors, and goals. The main challenge in planning is computational, as all the models, whether accommodating feedback and uncertainty or not, are intractable in the worst case. In this article, I review some of the models considered in current planning research, the progress achieved in solving these models, and some of the open problems.