This work has been submitted to the IEEE for possible publication. Abstract --Reconfigurable multi-robot cells offer a promising approach to meet fluctuating assembly demands. However, the recurrent planning of their configurations introduces new challenges, particularly in generating optimized, coordinated multi-robot motion sequences that minimize the assembly duration. This work presents a simulation-based method for generating such optimized sequences. While core operations are constrained and predetermined, traverse operations offer substantial optimization potential. Scheduling the core operations is formulated as an optimization problem, requiring feasible traverse operations to be integrated using a decomposition-based motion planning strategy. Several solution techniques are explored, including a sampling heuristic, tree-based search and gradient-free optimization. For motion planning, a decomposition method is proposed that identifies specific areas in the schedule, which can be solved independently with modified centralized path planning algorithms. The proposed method generates efficient and collision-free multi-robot assembly procedures that outperform a baseline relying on decentralized, robot-individual motion planning. Its effectiveness is demonstrated through simulation experiments. Note to Practitioners-- In practice, robotic motions in multi-robot assembly cells are often handcrafted for specific tasks, requiring significant effort and lacking scalability. This paper presents a novel method for optimizing robotic motion sequences and their execution schedules with the goal of minimizing the assembly duration. Existing approaches for combined task and motion planning often rely on high-level heuristics, impose restrictive constraints on planning, or demand excessive computational resources. . The proposed method separates task-related motions, which perform essential transformations on the assembly product, from connecting traverse motions.

Growing concerns about the AI alignment problem have emerged in recent years, with previous work focusing mainly on (1) qualitative descriptions of the alignment problem; (2) attempting to align AI actions with human interests by focusing on value specification and learning; and/or (3) focusing on a single agent or on humanity as a singular unit. Recent work in sociotechnical AI alignment has made some progress in defining alignment inclusively, but the field as a whole still lacks a systematic understanding of how to specify, describe, and analyze misalignment among entities, which may include individual humans, AI agents, and complex compositional entities such as corporations, nation-states, and so forth. Previous work on controversy in computational social science offers a mathematical model of contention among populations (of humans). In this paper, we adapt this contention model to the alignment problem, and show how misalignment can vary depending on the population of agents (human or otherwise) being observed, the domain in question, and the agents' probability-weighted preferences between possible outcomes. Our model departs from value specification approaches and focuses instead on the morass of complex, interlocking, sometimes contradictory goals that agents may have in practice. We apply our model by analyzing several case studies ranging from social media moderation to autonomous vehicle behavior. By applying our model with appropriately representative value data, AI engineers can ensure that their systems learn values maximally aligned with diverse human interests.

This paper is a scoping study to identify current methods used in handling sparse data with contextual bandits in web settings. The area is highly current and state of the art methods are identified. The years 2017-2020 are investigated, and 19 method articles are identified, and two review articles. Five categories of methods are described, making it easy to choose how to address sparse data using contextual bandits with a method available for modification in the specific setting of concern. In addition, each method has multiple techniques to choose from for future evaluation. The problem areas are also mentioned that each article covers. An overall updated understanding of sparse data problems using contextual bandits in web settings is given. The identified methods are policy evaluation (off-line and on-line) , hybrid-method, model representation (clusters and deep neural networks), dimensionality reduction, and simulation.

Availability of powerful computation and communication technology as well as advances in artificial intelligence enable a new generation of complex, AI-intense systems and applications. Such systems and applications promise exciting improvements on a societal level, yet they also bring with them new challenges for their development. In this paper we argue that significant challenges relate to defining and ensuring behaviour and quality attributes of such systems and applications. We specifically derive four challenge areas from relevant use cases of complex, AI-intense systems and applications related to industry, transportation, and home automation: understanding, determining, and specifying (i) contextual definitions and requirements, (ii) data attributes and requirements, (iii) performance definition and monitoring, and (iv) the impact of human factors on system acceptance and success. Solving these challenges will imply process support that integrates new requirements engineering methods into development approaches for complex, AI-intense systems and applications. We present these challenges in detail and propose a research roadmap.

One of the most frequently-used phrases at business events these days is "the future of work." It's increasingly clear that artificial intelligence and other new technologies will bring substantial changes in work tasks and business processes. But while these changes are predicted for the future, they're already present in many organizations for many different jobs. The situation brings to mind the William Gibson comment, "The future is already here--it's just not evenly distributed." The jobs and work processes described below are an example of this phenomenon.

This article was written by Dr. Matt Turek. Dramatic success in machine learning has led to a torrent of Artificial Intelligence (AI) applications. Continued advances promise to produce autonomous systems that will perceive, learn, decide, and act on their own. However, the effectiveness of these systems is limited by the machine's current inability to explain their decisions and actions to human users (Figure 1). The Department of Defense (DoD) is facing challenges that demand more intelligent, autonomous, and symbiotic systems.

In the U.S. alone, the construction industry creates around $1.3 trillion worth of structures every year, and employs around 7 million people. We spend much of our lives surrounded by the fruits of this labor, usually without thinking about what it takes to produce, and without a real awareness of how much we rely on those structures to be safe. We rarely enter buildings worrying that they are going to collapse, for example, or drive across a bridge fearful that it will crumble beneath our tires. That safety and public trust is important to maintain, and as the numbers of structures increase every year, that means regular inspection of more and more structures every year. A large number of the structures build rely upon concrete either in part or for the vast majority of their construction.

One of the challenges for those tracking the artificial intelligence industry is that, surprisingly, there's no accepted, standard definition of what artificial intelligence really is. AI luminaries all have slightly different definitions of what AI is. Rodney Brooks says that "artificial intelligence doesn't mean one thing… it's a collection of practices and pieces that people put together". Of course, that's not particularly settling for companies that need to understand the breadth of what AI technologies are and how to apply them to their specific needs. In general, most people would agree that the fundamental goals of AI are to enable machines to have cognition, perception, and decision-making capabilities that previously only humans or other intelligent creatures have.

One of the challenges for those tracking the artificial intelligence industry is that, surprisingly, there's no accepted, standard definition of what artificial intelligence really is. AI luminaries all have slightly different definitions of what AI is. Rodney Brooks says that "artificial intelligence doesn't mean one thing… it's a collection of practices and pieces that people put together". Of course, that's not particularly settling for companies that need to understand the breadth of what AI technologies are and how to apply them to their specific needs. In general, most people would agree that the fundamental goals of AI are to enable machines to have cognition, perception, and decision-making capabilities that previously only humans or other intelligent creatures have.

In the last two months, i.e. in April and May 2019, both the EU Commission and the OECD published guidelines for trustworthy and ethical Artificial Intelligence (AI). In both cases, these are only guidelines and, as such, are not legally binding. Both sets of guidelines were compiled by experts in the field. Let's have a closer look. "Why do we need guidelines for trustworthy, ethical AI?" you may ask.