Since the first conference held in Marseille in 1982, ICLP has been the premier international event for presenting research in logic programming. Contributions are sought in all areas of logic programming, including but not restricted to: Foundations: Semantics, Formalisms, Nonmonotonic reasoning, Knowledge representation. Languages: Concurrency, Objects, Coordination, Mobility, Higher Order, Types, Modes, Assertions, Modules, Meta-programming, Logic-based domain-specific languages, Programming Techniques. Declarative programming: Declarative program development, Analysis, Type and mode inference, Partial evaluation, Abstract interpretation, Transformation, Validation, Verification, Debugging, Profiling, Testing, Execution visualization Implementation: Virtual machines, Compilation, Memory management, Parallel/distributed execution, Constraint handling rules, Tabling, Foreign interfaces, User interfaces. Related Paradigms and Synergies: Inductive and Co-inductive Logic Programming, Constraint Logic Programming, Answer Set Programming, Interaction with SAT, SMT and CSP solvers, Logic programming techniques for type inference and theorem proving, Argumentation, Probabilistic Logic Programming, Relations to object-oriented and Functional programming. Applications: Databases, Big Data, Data integration and federation, Software engineering, Natural language processing, Web and Semantic Web, Agents, Artificial intelligence, Computational life sciences, Education, Cybersecurity, and Robotics.

Real-time multi-target path planning is a key issue in the field of autonomous driving. Although multiple paths can be generated in real-time with polynomial curves, the generated paths are not flexible enough to deal with complex road scenes such as S-shaped road and unstructured scenes such as parking lots. Search and sampling-based methods, such as A* and RRT and their derived methods, are flexible in generating paths for these complex road environments. However, the existing algorithms require significant time to plan to multiple targets, which greatly limits their application in autonomous driving. In this paper, a real-time path planning method for multi-targets is proposed. We train a fully convolutional neural network (FCN) to predict a path region for the target at first. By taking the predicted path region as soft constraints, the A* algorithm is then applied to search the exact path to the target. Experiments show that FCN can make multiple predictions in a very short time (50 times in 40ms), and the predicted path region effectively restrict the searching space for the following A* search. Therefore, the A* can search much faster so that the multi-target path planning can be achieved in real-time (3 targets in less than 100ms).

Explainable Artificial Intelligence (XAI) methods are typically deployed to explain and debug black-box machine learning models. However, most proposed XAI methods are black-boxes themselves and designed for images. Thus, they rely on visual interpretability to evaluate and prove explanations. In this work, we apply XAI methods previously used in the image and text-domain on time series. We present a methodology to test and evaluate various XAI methods on time series by introducing new verification techniques to incorporate the temporal dimension. We further conduct preliminary experiments to assess the quality of selected XAI method explanations with various verification methods on a range of datasets and inspecting quality metrics on it. We demonstrate that in our initial experiments, SHAP works robust for all models, but others like DeepLIFT, LRP, and Saliency Maps work better with specific architectures.

Professors Jia Di, left, and Trent Roberts inspect a prototype corn sensor set up in a test plot at the Arkansas Agricultural Research and Extension Center. FAYETTEVILLE, Ark. – A team of researchers from the University of Arkansas System Division of Agriculture and the University of Arkansas College of Engineering is designing tiny sensors that can be placed in corn stalks to monitor water, nitrogen and potassium needs in real time. The data collected from those sensors -- matched with geographic, weather and other environmental data -- will feed machine learning software to develop models that will be able to predict when a crop will need those inputs before the conditions exist. Those predictive models can help corn growers give their crops exactly the water and nutrients they need, before they experience stress, to achieve the best possible yields without wasting resources. The collaborative research by the division's Arkansas Agricultural Experiment Station and the university's College of Engineering is supported by the Chancellor's Discovery, Creativity, Innovation and Collaboration Fund.

Artificial Intelligence is aimed at simplifying and automating an array of business processes. Recently, AI made its entrance in the field of recruiting and instantly got close attention. The thing is the use of AI for recruiting resulted in significant benefits for both the companies and the candidates. But, as with any other technology, there are certain hidden rocks to keep in mind when implementing AI in your processes. So what exactly does it do and what kind of benefits it may bring?

Many readers will remember The Jetsons – a futuristic world in which sophisticated robots in both the home and the workplace had the ability to do, think, learn, and interact with humans. While The Jetsons' rendering of the "future" has not come to fruition, robots and artificial intelligence (AI) have made and continue to make their way into the modern workplace at breakneck speed, creating unprecedented opportunities and challenges for employers in nearly every sector of the economy. This series will explore those challenges, a topic of considerable importance to employers but one that has been overshadowed by the cost-savings and potentially positive economic impact that robots and AI can bring to a workplace. As the use of robots and AI in the workplace have increased and will continue to do so, employers must be proactive about identifying, understanding, and mitigating risks and areas of potential exposure. The future is coming, and in many ways is already here.

MOUNTAIN VIEW, Calif. and TALLINN, Estonia, September 16, 2019 -- Launching today, Pactum is an AI-based system that helps global companies to autonomously offer personalized, commercial negotiations on a massive scale. The Mountain View, California company, with engineering and operations in Estonia, has raised an initial $1.15 Million in pre-seed funding to augment negotiation and AI capabilities as well as scale operations. Pactum has also filed the patent this week related to its technology IP. Inefficient contracting has been estimated to cause firms to lose between 17% to 40% of the value on a given deal, depending on circumstances, according to research by KPMG. Pactum's AI helps companies improve their bottom line by implementing bespoke negotiation services for large volumes of incremental partners in every market, that might have previously been unmanaged.

Artificial intelligence dominates the headlines, part promise and part specter, as society grapples with how technology is changing the way we work and live. Both hype about AI's immediate potential and fear about its effects are exaggerated, according to MIT Sloan professorThomas W. Malone,director of the MIT Center for Collective Intelligence. A realistic understanding of artificial intelligence and the promise of robotics, machine learning, and natural language processing is increasingly important for businesses. The 2018 AI Index report found increased interest in the topic around the world, including substantial increases in AI research, investment in AI startups, enrollment in AI college courses, and jobs that require deep learning skills. "A lot of senior executives and business leaders today are almost desperate to understand how AI may affect their businesses," said Malone, who teaches a popular executive education course on artificial intelligence and business strategy.

This article is part of Fast Company's editorial series The New Rules of AI. More than 60 years into the era of artificial intelligence, the world's largest technology companies are just beginning to crack open what's possible with AI--and grapple with how it might change our future. Click here to read all the stories in the series. Artificial intelligence is still in its youth. But some very big things have already happened.

From winning at Go to passing eighth grade level multiple choice tests, AI is making rapid advances. But its creativity still leaves much to be desired. On September 4, 2019, Peter Clark, along with several other researchers, published "From'F' to'A' on the N.Y. Regents Science Exams: An Overview of the Aristo Project " The Aristo project named in the title is hailed for the rapid improvement it has demonstrated when it tested the way eighth-grade human students in New York State are tested for their knowledge of science. The researchers concluded that this is an important milestone for AI: "Although Aristo only answers multiple choice questions without diagrams, and operates only in the domain of science, it nevertheless represents an important milestone towards systems that can read and understand. The momentum on this task has been remarkable, with accuracy moving from roughly 60% to over 90% in just three years."