Cooperative decision-making under uncertainty is ubiquitous in natural systems as well as in engineering networks. Typically in a distributed cooperative decision-making scenario, there is assimilation of information across a network followed by decision-making based on the collective information. The result is a kind of collective intelligence, which is of fundamental interest both in terms of understanding natural systems and designing efficient engineered systems. A fundamental feature of decision-making under uncertainty is the explore-exploit tradeoff. The explore-exploit tradeoff refers to the tension between learning and optimizing: the decision-making agent needs to learn the unknown system parameters (exploration), while maximizing its decision-making objective, which depends on the unknown parameters (exploitation).

UNU allows groups to chat online in a new way by forming a Swarm Intelligence that can answer questions, make predictions, and each decisions. Artificial intelligence (unanimous) UNU has not only conquered the Oscars and Super Bowl, but also the famous Kentucky derby. Its Swarm Intelligence software has reportedly made betting gamblers richer, with correct predictions owing to its successful forecasting methodologies. According to News Discovery, UNU is a software program that harnesses the collective power of horse racing professionals to correctly predict the first four horses that cross the finish line and in which order. At last weekend's Kentucky Derby, one experimental player witnessed the power of this software at first hand.

Apprehending criminals on the run proves to be a tricky task, especially in a big city. With several exit strategies readily available, criminals can escape on foot or via public transport (such as via bus or subway), or by driving a car. On top of this, there are shops, alleyways, and intersections galore. Of course, there are also a ton of other people. All of this creates a high risk of failure for police.

A century ago, more than 60,000 tigers roamed the wild. Today, that number has dwindled to around 3,200. Poaching is one of the main drivers of this steep decline. Humans have pushed tigers to near-extinction, whether for their skins, medicine or for trophy hunting. The same applies to other large animal species like elephants and rhinoceros that play unique and crucial roles in the ecosystems where they live. Human patrols serve as the most direct form of protection of endangered animals, especially in large national parks.

The concept of "task" is at the core of artificial intelligence (AI): Tasks are used for training and evaluating AI systems, which are built in order to perform and automatize tasks we deem useful. In other fields of engineering theoretical foundations allow thorough evaluation of designs by methodical manipulation of well understood parameters with a known role and importance; this allows an aeronautics engineer, for instance, to systematically assess the effects of wind speed on an airplane's performance and stability. No framework exists in AI that allows this kind of methodical manipulation: Performance results on the few tasks in current use (cf. board games, question-answering) cannot be easily compared, however similar or different. The issue is even more acute with respect to artificial *general* intelligence systems, which must handle unanticipated tasks whose specifics cannot be known beforehand. A *task theory* would enable addressing tasks at the *class* level, bypassing their specifics, providing the appropriate formalization and classification of tasks, environments, and their parameters, resulting in more rigorous ways of measuring, comparing, and evaluating intelligent behavior. Even modest improvements in this direction would surpass the current ad-hoc nature of machine learning and AI evaluation. Here we discuss the main elements of the argument for a task theory and present an outline of what it might look like for physical tasks.

If you're going down to the racetrack, you might want to have an AI by your side. An artificial intelligence program developed by Unanimous A.I. successfully predicted the Superfecta at the 142nd Kentucky Derby last Saturday, turning a 20 bet into nearly 11,000. Using'Swarm Intelligence,' the AI was able to correctly choose the winning horse, Nyquist – along with the second, third, and fourth finishers. Unanimous AI's platform works by using the knowledge a group of people online, all logged into an the same interface where they can answer a series of questions. This animation shows how UNU's swarm intelligence makes its predictions Unanimous AI's platform works by using the knowledge a group of people online, all logged into an the same interface where they can answer a series of questions.

Celaton's inSTREAM software streamlines the handling of unstructured unpredictable (and structured) content such as correspondence, claims, complaints and invoices that organizations receive by email, social media, fax and paper. This minimizes the need for human intervention and ensures that only accurate, relevant and structured data enters business systems. Unique to inSTREAM is its ability to learn through the natural consequence of processing information and collaborating with people. Capgemini's extensive knowledge and experience in business process services will also enable Celaton to accelerate and improve inSTREAM's capabilities. The cooperation will enable Capgemini to increase efficiency, shorten turnaround times and enhance quality in areas where incoming documents and queries need to be processed, improving overall customer satisfaction.

Picking the winner from the 20 horse field at the Kentucky Derby is hard. So hard, in fact, that no expert polled by SBNation was able to do it. That's why the holy grail at the racetrack is the Superfecta, where bettors are asked not only to pick the winner, but the second, third and fourth horses to finish the Derby. This is fiendishly difficult task that, not surprisingly, defeated every expert at Churchill Downs, where no one predicted the top four horses correctly, much less in the correct order. In the world of AI, even Bing Predicts blew it, picking only heavily favored Nyquist to win the race, but missing the other 3 picks entirely.

Fujitsu Laboratories Ltd. and the University of Electro-Communications today announced the development of a high-speed algorithm that uses mathematical game theory as an artificial intelligence technology to aid in the development of security planning. This will work to solve city-scale road network security problems, such as where best to position checkpoints when trying to catch a criminal. For security measures at locations where people gather, it is often not possible to completely seal off all intrusion or escape routes with limited security resources, so it is necessary to effectively deploy security personnel and to minimize anticipated damage. The formulation of security plans has relied on the experience of experts and intuition, but in recent years there has been a focus on game theory, which mathematically describes both offence and defense, as a technology to support expert decision-making. However, it has been difficult to apply game theory to a city-scale security problem of catching criminals at checkpoints in real-world cities because the processing volume expands exponentially with the scale of the road network.

An area coverage control law in cooperation with reinforcement learning techniques is proposed for deploying multiple autonomous agents in a two-dimensional planar area. A scalar field characterizes the risk density in the area to be covered yielding nonuniform placement of agents while providing optimal coverage. This problem has traditionally been addressed in the literature to date using conventional control techniques, such as proportional and proportional--derivative controllers. In most cases, agents' actuator energy required to drive them in optimal configurations in the workspace is not taken into considerations. Here the maximum coverage is achieved with minimum actuator energy required by each agent. Similar to existing coverage control techniques, the proposed algorithm takes into consideration time-varying risk density. Area coverage is modeled using Voronoi tessellations governed by agents. Theoretical results are demonstrated through a set of computer simulations where multiple agents are able to deploy themselves, thus paving the way for efficient distributed Voronoi coverage control problems.