An NBER conference on Economics of Artificial Intelligence took place in Toronto on September 13-14, 2018. Research Associates Ajay K. Agrawal, Joshua S. Gans and Avi Goldfarb of University of Toronto and Catherine Tucker of MIT organized the meeting, sponsored by the Alfred P. Sloan Foundation, CIFAR, and the Creative Destruction Lab. These researchers' papers were presented and discussed: Emilio Calvano, Vencenzo Denicolò, and Sergio Pastorello, University of Bologna, and Giacomo Calzolari, European University Institute Q-Learning to Cooperate AI algorithms are increasingly replacing human decision making in real marketplaces. To inform the debate on potential consequences, Calvano, Calzolari, Denicolò, and Pastorello run experiments with AI agents powered by reinforcement learning in controlled environments (computer simulations). In particular, the researchers study multi-agent interaction in the context of a workhorse oligopoly model: price competition with Logit demand and constant marginal costs.

Cranfield University has announced plans for a world-leading Professorship in Autonomous Systems and Artificial Intelligence at the University, sponsored by BAE Systems, a technology leader in this field. The Professorship will be a research leadership role at the University, bringing together research in UAV's, Space and artificial intelligence (AI) adding to Cranfield's leading reputation in the fields of autonomous systems and AI. Advances in machine learning, high-performance computing, data science, multimodal sensing, and control are merging together to create enormous opportunities for intelligent, autonomous, or semi-autonomous systems. Such artificial intelligence systems are starting to achieve cognitive abilities such as language, attention, and creativity, promising to improve the safety and efficiency of systems for space technology and increasingly autonomous systems in aerospace and aviation. Julia Sutcliffe, Chief Technology Officer, BAE Systems Air, said: "Autonomous systems and artificial intelligence have the potential to provide a substantial positive impact upon product, service, and industrial capabilities. This prestigious appointment, in a growing and highly disruptive field, will enable BAE Systems to exploit the latest technologies in these areas to continuously improve our engineering and manufacturing processes, and give our customers a differentiating capability in the field."

Artificial perception is traditionally handled by hand-designing task specific algorithms. However, a truly autonomous robot should develop perceptive abilities on its own, by interacting with its environment, and adapting to new situations. The sensorimotor contingencies theory proposes to ground the development of those perceptive abilities in the way the agent can actively transform its sensory inputs. We propose a sensorimotor approach, inspired by this theory, in which the agent explores the world and discovers its properties by capturing the sensorimotor regularities they induce. This work presents an application of this approach to the discovery of a so-called visual field as the set of regularities that a visual sensor imposes on a naive agent's experience. A formalism is proposed to describe how those regularities can be captured in a sensorimotor predictive model. Finally, the approach is evaluated on a simulated system coarsely inspired from the human retina. Keywords: autonomous systems, developmental robotics, sensorimotor contingencies, predictive processing, sensorimotor learning, humanlike vision 1. Introduction Autonomy in robotics relies on sensory data processing to capture information about the world and adapt to it. Although the influence of machine learning has been growing more important in the last decades, traditional approaches to this problem of data processing involve significant manual design from engineers that build the robot. Consequently the resulting techniques for artificial perception appear rigid and constrained for tractability. Each of these specialized algorithms is applicable to only a small set of tasks, with potentially limiting inbuilt biases from the designer. Corresponding author Email address: alaflaquiere@aldebaran.com (Alban Laflaquière) Preprint submitted to Neurocomputing October 5, 2018 autonomy in a robot. Instead, an autonomous robot must be able to cope with the complexity of its world, build its own way to perceive it and adapt to its variations. To address this issue, the field of developmental robotics takes inspiration from biological and cognitive development in children [4]. It proposes that an agent learns to interact with its environment, autonomously and on an ontogenic timescale.

To date, no clear formalism for those mechanisms has arisen in the developmental robotics community. We propose predictive modeling [16], [17] as such a computational mechanism to learn sensorimotor contingencies, and thus acquire perceptive skills. In the context of SMCT, predictive models can be autonomously estimated by the agent to capture structure in the way motor commands actively transform sensory inputs, namely sensorimotor contingencies. Predictive modeling allows the incremental acquisition of skills required in developmental robotics, while providing a computational implementation of the concept of sensorimotor contingencies. Our current implementation of the formalism proposed in this paper uses a method to cluster state transition graphs, to discover densely connected subgraphs. Note that similar methods have already been proposed by others, for example in navigation tasks for the segmentation of location data into rooms [18], or for sub-goal discovery in hierarchical reinforcement learning (e.g.

Debates about rights are frequently framed around the concept of legal personhood, which is granted not just to human beings but also to some nonhuman entities, such as firms, corporations or governments. Legal entities, aka legal persons are granted certain privileges and responsibilities by the jurisdictions in which they are recognized, and many such rights are not available to nonperson agents. Attempting to secure legal personhood is often seen as a potential pathway to get certain rights and protections for animals [1], fetuses [2], trees, rivers [3] and artificially intelligent (AI) agents [4]. It is commonly believed that a court ruling or a legislative action is necessary to grant personhood to a new type of entity, but recent legal literature [5-8] suggests that loopholes in the current law may permit granting of legal personhood to currently existing AI/software without having to change the law or persuade any court.

Successful candidates will have a Doctoral degree (Ph.D.), publications, and demonstrated research competencies and capabilities commensurate with appointment levels in the department(s) of interest, as well as demonstrated interest in and experience with collaborative teaming and/or transdisciplinary efforts Successful candidates will be expected to develop and maintain externally funded research programs (individual and collaborative), engage in both undergraduate and graduate education, and contribute their leadership, partnering and innovative thinking towards global prominence in their respective discipline. Teaching opportunities will vary by department and teaching qualifications will be considered for fit within respective department(s).

The research described in this paper is motivated by the development of applications for the behaviour analysis of handwriting and sketch input. Our goal is to provide other researchers with a reproducible, categorised set of features that can be used for behaviour characterisation in different scenarios. We use the term feature to describe properties of strokes and gestures which can be calculated based on the raw sensor input from capture devices, such as digital pens or tablets. In this paper, a large number of features known from the literature are presented and categorised into different subsets.

Software defined networking (SDN) represents a promising networking architecture that combines central management and network programmability. SDN separates the control plane from the data plane and moves the network management to a central point, called the controller, that can be programmed and used as the brain of the network. Recently, the research community has showed an increased tendency to benefit from the recent advancements in the artificial intelligence (AI) field to provide learning abilities and better decision making in SDN. In this study, we provide a detailed overview of the recent efforts to include AI in SDN. Our study showed that the research efforts focused on three main sub-fields of AI namely: machine learning, meta-heuristics and fuzzy inference systems. Accordingly, in this work we investigate their different application areas and potential use, as well as the improvements achieved by including AI-based techniques in the SDN paradigm.

The ability of modeling the other agents, such as understanding their intentions and skills, is essential to an agent's interactions with other agents. Conventional agent modeling relies on passive observation from demonstrations. In this work, we propose an interactive agent modeling scheme enabled by encouraging an agent to learn to probe. In particular, the probing agent (i.e. a learner) learns to interact with the environment and with a target agent (i.e., a demonstrator) to maximize the change in the observed behaviors of that agent. Through probing, rich behaviors can be observed and are used for enhancing the agent modeling to learn a more accurate mind model of the target agent. Our framework consists of two learning processes: i) imitation learning for an approximated agent model and ii) pure curiosity-driven reinforcement learning for an efficient probing policy to discover new behaviors that otherwise can not be observed. We have validated our approach in four different tasks. The experimental results suggest that the agent model learned by our approach i) generalizes better in novel scenarios than the ones learned by passive observation, random probing, and other curiosity-driven approaches do, and ii) can be used for enhancing performance in multiple applications including distilling optimal planning to a policy net, collaboration, and competition. A video demo is available at https://www.dropbox.com/s/8mz6rd3349tso67/Probing_Demo.mov?dl=0

Most of the prior work on multi-agent reinforcement learning (MARL) achieves optimal collaboration by directly controlling the agents to maximize a common reward. In this paper, we aim to address this from a different angle. In particular, we consider scenarios where there are self-interested agents (i.e., worker agents) which have their own minds (preferences, intentions, skills, etc.) and can not be dictated to perform tasks they do not wish to do. For achieving optimal coordination among these agents, we train a super agent (i.e., the manager) to manage them by first inferring their minds based on both current and past observations and then initiating contracts to assign suitable tasks to workers and promise to reward them with corresponding bonuses so that they will agree to work together. The objective of the manager is maximizing the overall productivity as well as minimizing payments made to the workers for ad-hoc worker teaming. RL), which consists of agent modeling and policy learning. We have evaluated our approach in two environments, Resource Collection and Crafting, to simulate multi-agent management problems with various task settings and multiple designs for the worker agents. The experimental results have validated the effectiveness of our approach in modeling worker agents' minds online, and in achieving optimal ad-hoc teaming with good generalization and fast adaptation. As the main assumption and building block in economy, self-interested agents play a central roles in our daily life. Selfish agents, with their private beliefs, preferences, intentions, and skills, could collaborate effectively to make great achievement with proper incentives and contracts, an amazing phenomenon that happens every day in every corner of the world.