Collective animal behaviors are paradigmatic examples of fully decentralized operations involving complex collective computations such as collective turns in flocks of birds or collective harvesting by ants. These systems offer a unique source of inspiration for the development of fault-tolerant and self-healing multi-robot systems capable of operating in dynamic environments. Specifically, swarm robotics emerged and is significantly growing on these premises. However, to date, most swarm robotics systems reported in the literature involve basic computational tasks---averages and other algebraic operations. In this paper, we introduce a novel Collective computing framework based on the swarming paradigm, which exhibits the key innate features of swarms: robustness, scalability and flexibility. Unlike Edge computing, the proposed Collective computing framework is truly decentralized and does not require user intervention or additional servers to sustain its operations. This Collective computing framework is applied to the complex task of collective mapping, in which multiple robots aim at cooperatively map a large area. Our results confirm the effectiveness of the cooperative strategy, its robustness to the loss of multiple units, as well as its scalability. Furthermore, the topology of the interconnecting network is found to greatly influence the performance of the collective action.

Urbanism is no longer planned on paper thanks to powerful models and 3D simulation platforms. However, current work is not open to the public and lacks an optimisation agent that could help in decision making. This paper describes the creation of an open-source simulation based on an existing Dutch liveability score with a built-in AI module. Features are selected using feature engineering and Random Forests. Then, a modified scoring function is built based on the former liveability classes. The score is predicted using Random Forest for regression and achieved a recall of 0.83 with 10-fold cross-validation. Afterwards, Exploratory Factor Analysis is applied to select the actions present in the model. The resulting indicators are divided into 5 groups, and 12 actions are generated. The performance of four optimisation algorithms is compared, namely NSGA-II, PAES, SPEA2 and eps-MOEA, on three established criteria of quality: cardinality, the spread of the solutions, spacing, and the resulting score and number of turns. Although all four algorithms show different strengths, eps-MOEA is selected to be the most suitable for this problem. Ultimately, the simulation incorporates the model and the selected AI module in a GUI written in the Kivy framework for Python. Tests performed on users show positive responses and encourage further initiatives towards joining technology and public applications.

Abstract--In this paper, we propose hybrid building/floor classification and floor-level two-dimensional location coordinates regression using a single-input and multi-output (SIMO) deep neural network (DNN) for large-scale indoor localization based on Wi-Fi fingerprinting. The proposed scheme exploits the different nature of the estimation of building/floor and floor-level location coordinates and uses a different estimation framework for each task with a dedicated output and hidden layers enabled by SIMO DNN architecture. We carry out preliminary evaluation of the performance of the hybrid floor classification and floorlevel two-dimensional location coordinates regression using new Wi-Fi crowdsourced fingerprinting datasets provided by Tampere University of Technology (TUT), Finland, covering a single building with five floors. Experimental results demonstrate that the proposed SIMO-DNN-based hybrid classification/regression scheme outperforms existing schemes in terms of both floor detection rate and mean positioning errors. Of many localization techniques available nowadays, the location fingerprinting is one of the most popular and promising technologies for indoor localization [1].

We briefly introduce herein a new form of distributed, multi-agent artificial intelligence, which we refer to as "tentacular." Tentacular AI is distinguished by six attributes, which among other things entail a capacity for reasoning and planning based in highly expressive calculi (logics), and which enlists subsidiary agents across distances circumscribed only by the reach of one or more given networks.

Parameter learning is the technique for obtaining the probabilistic parameters in conditional probability tables in Bayesian networks from tables with (observed) data --- where it is assumed that the underlying graphical structure is known. There are basically two ways of doing so, referred to as maximal likelihood estimation (MLE) and as Bayesian learning. This paper provides a categorical analysis of these two techniques and describes them in terms of basic properties of the multiset monad M, the distribution monad D and the Giry monad G. In essence, learning is about the reltionships between multisets (used for counting) on the one hand and probability distributions on the other. These relationsips will be described as suitable natural transformations.

You've seen him hop on boxes, run across a field and execute backflips with the precision of a professional gymnast. Perhaps it seems only natural that Atlas ---- the humanoid robot and YouTube sensation created and periodically updated on video by tech company Boston Dynamics ---- has begun mastering another sophisticated form of human movement: parkour. In the company's latest 29-second teaser, Atlas can be seen jumping over a log using one leg before nimbly bounding up increasingly high wooden boxes, his mechanical limbs adjusting midair to maintain balance in a fashion that seems unmistakably human. "The control software uses the whole body including legs, arms and torso, to marshal the energy and strength for jumping over the log and leaping up the steps without breaking its pace," the company said in a statement posted on YouTube. "Atlas uses computer vision to locate itself with respect to visible markers on the approach to hit the terrain accurately."

Vehicle-to-everything communications technology, more commonly referred to as V2X, is poised to change the way cars operate in the very near future. Numerous automakers are working on V2X systems, some of which are already available. Among those automakers is Ford which thinks the tech could one day eliminate traffic lights. The automaker announced Wednesday it will trial an Intersection Priority Management (IPM) system on the streets of Milton Keynes, United Kingdom. The system is Ford's way of demonstrating that cars may not always have to stop for an intersection or traffic sign.

Assassin's Creed Odyssey is aptly named. It is an enormous, meandering journey through ancient Greece at the beginning of the Peloponnesian war as the struggle between Sparta and Athens begins to reshape the Greek world. It will shock you with its breadth and depth: the sea hides sunken ruins, the detail of temple paintings is impeccable, authentically clothed characters wander enormous cities whilst chatting in Greek, soldiers clash on roads as citizens scatter. You play a mercenary, choosing between the equally statuesque and self-assured Kassandra or Alexios. There is an element of family drama that propels the story forward in counterpart to the overarching historical drama of the setting.

Artur Garcez gave a lecture on Relational Neuro-Symbolic AI at the EurAI Advanced Course on AI, 2018, which took place in beautiful Ferrara, Italy. All the lectures, with overarching theme Statistical Relational AI, are available from the University of Ferrara's YouTube channel: https://youtu.be/KeFhKi-tOTs?list Artur Garcez gave two talks: Part 1 gives an overview of two decades of research on neuro-symbolic AI. Part 2 describes in some detail two neuro-symbolic systems for relational learning: Connectionist ILP and the Logic Tensor Networks framework.

As attention shifts from factory floors to knowledge-based, highly specialised occupations, a panel of experts discuss the impact of AI on financial and professional services.