Decision making in the Agriculture domain can be a complex task. The land area allocated to each crop should be fixed every season according to several parameters: prices, demand, harvesting periods, seeds, ground, season etc... The decision to make becomes more difficult when a group of farmers must fix the price and all parameters all together. Generally, optimization models are useful for farmers to find no dominated solutions, but it remains difficult if the farmers have to agree on one solution. We combine two approaches in order to support a group of farmers engaged in this kind of decision making process. We firstly generate a set of no dominated solutions thanks to a centralized optimization model. Based on this set of solution we then used a Group Decision Support System called GRUS for choosing the best solution for the group of farmers. The combined approach allows us to determine the best solution for the group in a consensual way. This combination of approaches is very innovative for the Agriculture. This approach has been tested in laboratory in a previous work. In the current work the same experiment has been conducted with real business (farmers) in order to benefit from their expertise. The two experiments are compared.

Machine common sense remains a broad, potentially unbounded problem in artificial intelligence (AI). There is a wide range of strategies that can be employed to make progress on this challenge. This article deals with the aspects of modeling commonsense reasoning focusing on such domain as interpersonal interactions. The basic idea is that there are several types of commonsense reasoning: one is manifested at the logical level of physical actions, the other deals with the understanding of the essence of human-human interactions. Existing approaches, based on formal logic and artificial neural networks, allow for modeling only the first type of common sense. To model the second type, it is vital to understand the motives and rules of human behavior. This model is based on real-life heuristics, i.e., the rules of thumb, developed through knowledge and experience of different generations. Such knowledge base allows for development of an expert system with inference and explanatory mechanisms (commonsense reasoning algorithms and personal models). Algorithms provide tools for a situation analysis, while personal models make it possible to identify personality traits. The system so designed should perform the function of amplified intelligence for interactions, including human-machine.

The agent-based modeling and simulation (ABMS) paradigm has been used to analyze, reproduce, and predict phenomena related to many application areas. Although there are many agent-based platforms that support simulation development, they rely on programming languages that require extensive programming knowledge. Model-driven development (MDD) has been explored to facilitate simulation modeling, by means of high-level modeling languages that provide reusable building blocks that hide computational complexity, and code generation. However, there is still limited knowledge of how MDD approaches to ABMS contribute to increasing development productivity and quality. We thus in this paper present an empirical study that quantitatively compares the use of MDD and ABMS platforms mainly in terms of effort and developer mistakes. Our evaluation was performed using MDD4ABMS-an MDD approach with a core and extensions to two application areas, one of which developed for this study-and NetLogo, a widely used platform. The obtained results show that MDD4ABMS requires less effort to develop simulations with similar (sometimes better) design quality than NetLogo, giving evidence of the benefits that MDD can provide to ABMS.

We propose answer-set programs that specify and compute counterfactual interventions as a basis for causality-based explanations to decisions produced by classification models. They can be applied with black-box models and models that can be specified as logic programs, such as rule-based classifiers. The main focus is on the specification and computation of maximum responsibility causal explanations. The use of additional semantic knowledge is investigated.

Choosing what they will work on in the future is a difficult decision that adolescents often make considering their skills and the needs of a job market that in a matter of 5 years can be radically transformed. Beyond the aptitude tests, a Colombian venture states that using artificial intelligence it is not only possible to find out what someone will be good at, but also to bring them closer to their life purpose. That ambitious mission is the dream of Life Design, a professional guidance software that is postulated as a tool of self-knowledge through technology. The company, which started in January 2018, closed the first round of investment of $ 150,000 and has worked with 2,000 active users since October last year. According to Felipe Rojas, one of the co-founders, unlike traditional methods, Life Design's philosophy is not to create only the professionals that the industry needs, but to identify what the student is interested in, their tastes, preferences and skills and then connect them.

We were promised robots everywhere -- fully autonomous robots that will drive our cars end-to-end, clean our dishes, drive our freight, make our food, pipette and do our lab work, write our legal documents, mow the lawn, balance our books and even clean our houses. And yet instead of Terminator or WALL-E or HAL 9000 or R2-D2, all we got is Facebook serving us ads we don't want to click on, Netflix recommending us another movie that we probably shouldn't stay up to watch, and iRobot's Roomba. Where are all the robots? This is the question I've been trying to investigate while building my own robotics company (a currently stealth company named Chef Robotics in the food robotics space) as well as investing in many robotics/AI companies through my venture capital fund Prototype Capital. Industrial six degrees of freedom (read as six motors serially attached to each other) robot arms were actually developed around 1973 and there are hundreds of thousands of them out there -- it's just that up to this point, almost all of these robots have been in the extremely controlled environment of factory automation doing the same thing over and over again millions of times. And we've formed many multibillion dollar companies through these factory automation robots including FANUC, KUKA, ABB and Foxconn (yes they make their own robots). Go to any automotive manufacturing plant and you'll see hundreds (or in Tesla's case, thousands). They work insanely well and can pick up massive payloads -- a full car -- and have precision sometimes up to a millimeter.

Mass public quarantining, colloquially known as a lock-down, is a non-pharmaceutical intervention to check spread of disease. This paper presents ESOP (Epidemiologically and Socio-economically Optimal Policies), a novel application of active machine learning techniques using Bayesian optimization, that interacts with an epidemiological model to arrive at lock-down schedules that optimally balance public health benefits and socio-economic downsides of reduced economic activity during lock-down periods. The utility of ESOP is demonstrated using case studies with VIPER (Virus-Individual-Policy-EnviRonment), a stochastic agent-based simulator that this paper also proposes. However, ESOP is flexible enough to interact with arbitrary epidemiological simulators in a black-box manner, and produce schedules that involve multiple phases of lock-downs.

I've been working in the financial services space for close to 30 years now. I've seen many trends and technologies emerge. Some take hold, several are just a flash in the pan. Regardless of how long a concept sticks around, one thing remains: terminology plays a material role in shaping perceptions. In a world where messaging tends to over complicate things, too many acronyms and too many buzzwords all work against what should be the primary objective: clearly illustrating value.

Washington – The Trump administration plans to reinterpret a Cold War-era arms agreement between 34 nations with the goal of allowing U.S. defense contractors to sell more American-made drones to a wide array of nations, three defense industry executives and a U.S. official told Reuters. The policy change, which has not been previously reported, could open up sales of armed U.S. drones to less stable governments such as Jordan and the United Arab Emirates that in the past have been forbidden from buying them under the 33-year-old Missile Technology Control Regime (MTCR), said the U.S. official, a former U.S. official and one of the executives. It could also undermine longstanding MTCR compliance from countries such as Russia, said the U.S. official, who has direct knowledge of the policy shift. Reinterpreting the MTCR is part of a broader Trump administration effort to sell more weapons overseas. It has overhauled a broad range of arms export regulations and removed the U.S. from international arms treaties including the Intermediate-Range Nuclear Forces Treaty and the Open Skies Treaty.

Rainbow fish behave like matadors by darting away from their predators at the last moment to avoid being eaten, a new study reveals. The tiny fish, also known as a Trinidadian guppy, spans less than an inch in length. It initially draws the attention of its most common predator – the much larger pike cichlid – by turning its irises black, which makes its eyes very conspicuous. According to a British team of scientists who performed experiments in water tanks using robots, the rainbow fish then uses quick reflexes to whip its head out of the way, causing the predator to miss, before swimming away. The speed of the whole interaction is around three hundredths of a second, meaning it's only fully observable using a high-speed camera.