DMG Consulting LLC (DMG) has made a series of predictions about various IT segments related to the contact center market. This blog post addresses the future of workforce management (WFM) solutions. Prediction: AI-enabled adaptive real-time forecasting and scheduling will replace traditional workforce management solutions. Intent: New algorithms are being introduced into WFM solutions to improve the accuracy of forecasting and scheduling recommendations. The more advanced WFM solutions are using a combination of mathematical modeling and simulation to optimize scheduling recommendations.

Kartik Talamadupula is a research staff member at IBM's T.J. Watson Research Center in Yorktown Heights, New York in the AI Science - Reasoning group in IBM Research AI. His research interests lie in the field of Automated Planning, within the wider umbrella of Artificial Intelligence (AI), and in examining the issues inherent in using planning and reasoning technologies as mediators in human-machine teams. He also has research interests in reinforcement learning, conversation and dialog systems, crowdsourcing/human computation, AI for IoT, and information retrieval on social media (specifically Twitter). He received his Ph.D. in Computer Science in Fall 2014 from Arizona State University, where he worked on extending the frontiers of AI planning methods and technologies. His research focused on understanding, analyzing, and extending the role that automated planners can play as part of integrated AI systems that interact directly and cooperatively with humans.

As we look ahead to 2020, it's time to embrace the fact that traditional project planning tools must evolve to meet the changing needs of planners and help them work smarter, not harder. Today's advanced planning solutions that incorporate both artificial and human intelligence enable planners to work more efficiently and create more achievable plans. The current state of project planning needs an overhaul, and I believe it should be in the form of what's called predictive planning. Think about it: You don't have to do things like manually check your Word document for spelling errors, explicitly type an entire recipient's email address, or calculate the miles of a trip every time, do you? That's because today's computers can store and, more importantly, recall knowledge.

But while there continues to be much debate over the impact of AI technology on headcount, the more pressing question for HR and workforce managers is how intelligent software can be deployed to augment work today. HR Tech News has selected the year's most ground-breaking stories of AI in the workplace. Recruitment tech specialist HireVue, however, takes candidate screening to the next level by combining facial analysis with AI. The software behind the platform purportedly relies on 25,000 data points taken from the facial expressions, movements and tone of voice of past successful candidates then uses them as a benchmark for screening new applicants. These subtle clues from their interaction with the AI reportedly help determine their suitability to the role. Amid efforts to build a more diverse workforce, organizations still struggle against the influence of unconscious bias in hiring.

But when you use that same speaker to make an online purchase, you may be less aware of the complex AI that is involved in the process of picking and packing that item in a warehouse and routing it for delivery to your doorstep. The world of business is full of similar use cases for complex AI, where the technology is transforming business performance. However, something that underpins all of these processes is the need for planning. For example, how did the aforementioned supplier know what the demand for their product was going to be in the first instance? This is where'intelligent planning' – the application of AI to business forecasting and planning processes – comes in; another example of complex AI.

The potential of Artificial Intelligence (AI) to tackle challenging problems that afflict society is enormous, particularly in the areas of healthcare, conservation and public safety and security. Many problems in these domains involve harnessing social networks of under-served communities to enable positive change, e.g., using social networks of homeless youth to raise awareness about Human Immunodeficiency Virus (HIV) and other STDs. Unfortunately, most of these real-world problems are characterized by uncertainties about social network structure and influence models, and previous research in AI fails to sufficiently address these uncertainties. This thesis addresses these shortcomings by advancing the state-of-the-art to a new generation of algorithms for interventions in social networks. In particular, this thesis describes the design and development of new influence maximization algorithms which can handle various uncertainties that commonly exist in real-world social networks. These algorithms utilize techniques from sequential planning problems and social network theory to develop new kinds of AI algorithms. Further, this thesis also demonstrates the real-world impact of these algorithms by describing their deployment in three pilot studies to spread awareness about HIV among actual homeless youth in Los Angeles. This represents one of the first-ever deployments of computer science based influence maximization algorithms in this domain. Our results show that our AI algorithms improved upon the state-of-the-art by 160% in the real-world. We discuss research and implementation challenges faced in deploying these algorithms, and lessons that can be gleaned for future deployment of such algorithms. The positive results from these deployments illustrate the enormous potential of AI in addressing societally relevant problems.

ESA is an equal opportunity employer, committed to achieving diversity within the workforce and creating an inclusive working environment. Applications from women are encouraged. This post is classified F2 on the Coordinated Organisations' salary scale. This covers all life-cycle aspects from requirements specification to development, verification, validation and maintenance. Within this division, the Software Technology Section is aiming at exploring the use of new technologies in the different areas. Planning techniques to increase the on-board autonomy, e.g.

We present the first approach capable of learning domain-independent planning heuristics entirely from scratch. The heuristics we learn map the hypergraph representation of the delete-relaxation of the planning problem at hand, to a cost estimate that approximates that of the least-cost path from the current state to the goal through the hypergraph. We generalise Graph Networks to obtain a new framework for learning over hypergraphs, which we specialise to learn planning heuristics by training over state/value pairs obtained from optimal cost plans. Our experiments show that the resulting architecture, STRIPS-HGNs, is capable of learning heuristics that are competitive with existing delete-relaxation heuristics including LM-cut. We show that the heuristics we learn are able to generalise across different problems and domains, including to domains that were not seen during training.

Mitsubishi Heavy Industries Ltd. is reviewing a test flight plan for the SpaceJet small passenger jet under development, the firm's president said Wednesday. The review, partly to see whether the first delivery of the aircraft should be moved back further, reflects delays in the production of test models for obtaining a type certificate, according to Mitsubishi Heavy Industries President Seiji Izumisawa. So far, the planned first delivery of the SpaceJet has been delayed five times, from 2013 to the middle of 2020. "We have not changed" the current schedule, Izumisawa said in an interview with media organizations. The SpaceJet, previously known as the Mitsubishi Regional Jet, is the first Japanese-made small passenger jet developed by Mitsubishi Aircraft Corp., a unit of Mitsubishi Heavy.

Hierarchical Task Network (HTN) planning is showing its power in real-world planning. Although domain experts have partial hierarchical domain knowledge, it is time-consuming to specify all HTN methods, leaving them incomplete. On the other hand, traditional HTN learning approaches focus only on declarative goals, omitting the hierarchical domain knowledge. In this paper, we propose a novel learning framework to refine HTN methods via task insertion with completely preserving the original methods. As it is difficult to identify incomplete methods without designating declarative goals for compound tasks, we introduce the notion of prioritized preference to capture the incompleteness possibility of methods. Specifically, the framework first computes the preferred completion profile w.r .t.the prioritized preference to refine the incomplete methods. Then it finds the minimal set of refined methods via a method substitution operation. Experimental analysis demonstrates that our approach is effective, especially in solving new HTN planning instances.