Collaborating Authors


New framework for cooperative bots aims to mimic high-performing human teams


A Georgia Institute of Technology research group in the School of Interactive Computing has developed a robotics system for collaborative bots that work independently to achieve a shared goal. The system intelligently increases the information shared among the bots and allows for improved cooperation. The aim is to model high-functioning human teams. It also creates resiliency against bad or unreliable team bots that may hinder the overall programmed goal. "Intuitively, the idea behind our new framework -- InfoPG -- is that a robot agent goes back-and-forth on what it thinks it should do with their teammates, and then the teammates will update on what they think is best to do," said Esmaeil Seraj, Ph.D. student in the CORE Robotics Lab and researcher on the project.

Researchers use artificial intelligence to predict road user behavior - Actu IA


For an autonomous car to drive safely, being able to predict the behavior of other road users is essential. A research team at the Massachusetts Institute of Technology's CSAIL, along with researchers at the Institute for Interdisciplinary Information Sciences (IIIS) at Tsinghua University in Beijing, have developed a new ML system that could one day help driverless cars predict in real time the upcoming movements of nearby drivers, cyclists and pedestrians. They titled their study, " M2I: From Factored Marginal Path Prediction to Interactive Prediction." Qiao Sun, Junru Gu, Hang Zhao are the IIIS members who participated in this study while Xin Huang and Brian Williams represented MIT. Humans are unpredictable, which makes predicting road user behavior in urban environments de facto very difficult.

Emergent bartering behaviour in multi-agent reinforcement learning


Advances in artificial intelligence often stem from the development of new environments that abstract real-world situations into a form where research can be done conveniently. This paper contributes such an environment based on ideas inspired by elementary Microeconomics. Agents learn to produce resources in a spatially complex world, trade them with one another, and consume those that they prefer. We show that the emergent production, consumption, and pricing behaviours respond to environmental conditions in the directions predicted by supply and demand shifts in Microeconomics. We also demonstrate settings where the agents' emergent prices for goods vary over space, reflecting the local abundance of goods.

Newsletter #73 -- DeepMind's 600 task AI agent


The company has raised $100 million in round C funding with the aim of becoming the "GitHub of machine learning". Inflection -- is an AI-first company aiming to redefine human-computer interaction. It is led by LinkedIn and DeepMind co-founders and was referenced in our Newsletter #68. The company has now raised $225 million in venture funding to use AI to help humans "talk" to computers. Unlearn -- aims to accelerate clinical trials by using AI, digital twins, and novel statistical methods to "enable smaller control groups while maintaining power and generating evidence suitable for supporting regulatory decisions".

Research Papers based on Multi Agent Systems


This brief aims to sensitize the reader to EGT based issues, results and prospects, which are accruing in importance for the modeling of minds with machines and the engineering of prosocial behaviours in dynamical MAS, with impact on our understanding of the emergence and stability of collective behaviours.

Avaya rolls out a turnkey virtual agent


Zeus Kerravala is founder and principal analyst with ZK Research. He spent 10 years at Yankee Group and prior to that held a number of corporate IT positions. Cloud communications provider Avaya has announced an update to its Avaya OneCloud platform, in which its Virtual Agent is available as a ready-to-deploy, turnkey, configurable service. This would let customers quickly deploy an artificial intelligence (AI)-powered virtual agent that could be used immediately. This complements Avaya's current Google Dialogflow-based agent that requires developers to create a virtual agent from scratch. There has been an intense focus on customer experience (CX) improvement during the past few years, because CX is now the top brand differentiator outweighing price, product quality and all other factors.

Features of a smart city


A smart city is a city that uses technology to provide services and solve city problems. The main goals of a smart city are to improve policy efficiency, reduce waste and inconvenience, improve social and economic quality, and maximize social inclusion. Due to the breadth of technologies that have been implemented under the smart city label, it is difficult to distill a precise definition of a smart city. As the world's population continues to urbanize – by 2050, 66% of the world's population is expected to be urban – there is a global trend toward the creation of smart cities. This tendency not only causes many physical, social, behavioural, economic, and infrastructure issues, but it also creates many opportunities.

GitHub - google-research/recsim_ng: RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystems


RecSimNG is a scalable, modular, differentiable simulator implemented in Edward2 and TensorFlow. It offers: a powerful, general probabilistic programming language for agent-behavior specification; an XLA-based vectorized execution model for running simulations on accelerated hardware; and tools for probabilistic inference and latent-variable model learning, backed by automatic differentiation and tracing. We describe RecSim NG and illustrate how it can be used to create transparent, configurable, end-to-end models of a recommender ecosystem. Specifically, we present a collection of use cases that demonstrate how the functionality described above can help both researchers and practitioners easily develop and train novel algorithms for recommender systems. Please cite the paper if you use the code from this repository in your work. This is not an officially supported Google product.

Embracing AWKWARD! Real-time Adjustment of Reactive Planning Using Social Norms


This paper presents the AWKWARD agent architecture for the development of agents in Multi-Agent Systems. AWKWARD agents can have their plans re-configured in real time to align with social role requirements under changing environmental and social circumstances. The proposed hybrid architecture makes use of Behaviour Oriented Design (BOD) to develop agents with reactive planning and of the well-established OperA framework to provide organisational, social, and interaction definitions in order to validate and adjust agents' behaviours. Together, OperA and BOD can achieve real-time adjustment of agent plans for evolving social roles, while providing the additional benefit of transparency into the interactions that drive this behavioural change in individual agents. We present this architecture to motivate the bridging between traditional symbolic- and behaviour-based AI communities, where such combined solutions can help MAS researchers in their pursuit of building stronger, more robust intelligent agent teams.

Three Intelligent Agent State Representation in Artificial Intelligence


Each state of the world is a black box with no internal structure. Each state has a fixed set of variables or attributes that holds a value. We do not share your email to any 3rd party companies! Be ready to learn AI and ML. Save my name, email, and website in this browser for the next time I comment.