An artificial intelligence agent named Tala may open the door on a new way of gathering feedback from New Zealand's Samoan community. The Talanoa Project is a pilot project that uses IBM's artificial intelligence virtual agent solution, Watson, to interact in real time in Samoan for public consultation and community engagement. Developed and designed by Beca, business director Matthew Ensor said it was about consulting with'the silent majority' in the public on projects and community facilities. "We don't hear so much from the people where language is a barrier, where culturally there's no tradition of responding to public consultation. "We then created a conversational agent, it's like a chat-bot and what it does is it mimics the kind of conversation that you would have with a consultation expert," Mr Ensor said. "It will ask open questions about your thoughts on different things and really lets the person lead the conversation rather than a survey form where the questions are completely scripted." Steve O'Donnell from IBM New Zealand's Managing Partner for Global Business Services said this was the first time IBM Watson Assistant had been used for public consultation in New Zealand in a language other than English. "What we are seeing now is AI being able to scale down, and drive value in many industries," he said. "IBM Watson has already transformed the world of customer service, due largely to its ability to understand human sentiment and interact naturally with people and Tala is a promising first step towards that." The Talanoa Project, part funded by Callaghan Innovation, tested Tala among a few dozen Samoan speakers, asking them for their thoughts on their local community facilities. The focus group of Samoans ranged from 19-years of age to 77 being the oldest and included Samoan elders, law students, psychologists and sociologists. "It was overwhelmingly positive the response we got back from the Samoan community," Mr Ensor said. "We had a few people share that it was great to hear technology using their native language.

Such a disappearance is a fundamental consequence not of technology but of human psychology. Whenever people learn something sufficiently well, they cease to be aware of it. Thus, Weiser's vision is even broader: as this technology becomes truly embedded in human activity we won't be aware of it at all. As the field of ubiquitous computing has evolved, with computation embedded in walls, clothes, and so forth, the materiality to support it is often physically and intentionally hidden from the user. Indeed, this material disappearance is often considered evidence of good design. The "agent" metaphor, in particular in its early presentations such as the Knowledge Navigator and Starfire, is also another utopian vision. These virtual agents are typically accessible via peripherals such as screens or phones, doing the bidding of those they serve.

Transferring as fast as possible the functioning of our brain to artificial intelligence is an ambitious goal that would help advance the state of the art in AI and robotics. It is in this perspective that we propose to start from hypotheses derived from an empirical study in a human-robot interaction and to verify if they are validated in the same way for children as for a basic reinforcement learning algorithm. Thus, we check whether receiving help from an expert when solving a simple close-ended task (the Towers of Hano\"i) allows to accelerate or not the learning of this task, depending on whether the intervention is canonical or requested by the player. Our experiences have allowed us to conclude that, whether requested or not, a Q-learning algorithm benefits in the same way from expert help as children do.

Multi-agent learning is a challenging problem in machine learning that has applications in different domains such as distributed control, robotics, and economics. We develop a prescriptive model of multi-agent behavior using Markov games. Since in many multi-agent systems, agents do not necessary select their optimum strategies against other agents (e.g., multi-pedestrian interaction), we focus on models in which the agents play "exploration but near optimum strategies". We model such policies using the Boltzmann-Gibbs distribution. This leads to a set of coupled Bellman equations that describes the behavior of the agents. We introduce a set of conditions under which the set of equations admit a unique solution and propose two algorithms that provably provide the solution in finite and infinite time horizon scenarios. We also study a practical setting in which the interactions can be described using the occupancy measures and propose a simplified Markov game with less complexity. Furthermore, we establish the connection between the Markov games with exploration strategies and the principle of maximum causal entropy for multi-agent systems. Finally, we evaluate the performance of our algorithms via several well-known games from the literature and some games that are designed based on real world applications.

Modeling and simulation is certainly a vast discipline with a broad and complex body of knowledge having, beyond the surface, a large technical and theoretical background (Minsky, 1965) (Banks et al., 2009) (Zeigler et al., 2000) (Boccara, 2003) which consequently, is hard of being completely mastered from modelers coming from disperse domains like biology, ecology or even computer science. Among the existing formalisms, the agent-based or individual-based is increasing gradually the number of adepts in the recent years. The Individual-based modeling is a powerful methodology which is having more and more acceptance between researchers and practitioners of distinct branches from social to biological sciences, including specifically the modeling of ecological processes and microbial consortia studies. Certainly, one of the main reasons for the success of this approach is the relative simplicity for capturing micro-level properties, stochasticity and spatially complex phenomena without the requirement of a high level of mathematical background (Grimm and Railsback, 2005). But the counterpart of the ease for building complex and feature rich models, is the lack of a closed formal mathematical form of the model which implies that the study of these models cannot be attacked analytically.

To avoid the computational requirements of online methods, we can use reinforcement learning (RL) instead. In RL, In recent years, major progress has been made to deploy the agent interacts with a simulation environment many autonomous vehicles and improve safety. However, certain times prior to execution, and at each simulation episode common driving situations like merging in dense traffic are it improves its strategy. The resulting policy can then be still challenging for autonomous vehicles. Situations like deployed online and is often inexpensive to evaluate. RL the one illustrated in Figure 1 often involve negotiating with provides a flexible framework to automatically find good human drivers.

In this paper, we provide an overview of the existing methods for integrating human advice into a Reinforcement Learning process. We propose a taxonomy of different types of teaching signals, and present them according to three main aspects: how they can be provided to the learning agent, how they can be integrated into the learning process, and how they can be interpreted by the agent if their meaning is not determined beforehand. Finally, we compare the benefits and limitations of using each type of teaching signals, and propose a unified view of interactive learning methods.

We consider the optimization of distributed resource scheduling to minimize the sum of task latency and energy consumption for all the Internet of things devices (IoTDs) in a large-scale mobile edge computing (MEC) system. To address this problem, we propose a distributed intelligent resource scheduling (DIRS) framework, which includes centralized training relying on the global information and distributed decision making by each agent deployed in each MEC server. More specifically, we first introduce a novel multi-agent ensemble-assisted distributed deep reinforcement learning (DRL) architecture, which can simplify the overall neural network structure of each agent by partitioning the state space and also improve the performance of a single agent by combining decisions of all the agents. Secondly, we apply action refinement to enhance the exploration ability of the proposed DIRS framework, where the near-optimal state-action pairs are obtained by a novel L\'evy flight search. Finally, an imitation acceleration scheme is presented to pre-train all the agents, which can significantly accelerate the learning process of the proposed framework through learning the professional experience from a small amount of demonstration data. Extensive simulations are conducted to demonstrate that the proposed DIRS framework is efficient and outperforms the existing benchmark schemes.

The model simulates realistic production environments using a training model from Fischertechnik, the protocols MQTT, OPC UA, as well as Amazon Web Services and a programmable logic controller from Siemens. The AR apps were designed to be platform-independent, so that they run on iPadOS, iOS and Android, as well as HoloLens 1 and 2. The "SmARt Factory" continues to evolve: in the future, for example, a multi-agent system will enable adaptable production.

Below is a guest post from Aditya Baru, product marketing manager for our AI product group. He will be talking about a new partnership between MathWorks and Microsoft. I'm glad to announce that MathWorks is partnering with Microsoft to help engineers develop autonomous systems for industrial and manufacturing applications. This partnership focuses on the integration between Simulink, our platform for simulation and Model-Based Design and Project Bonsai, Microsoft's cloud-based platform for designing autonomous systems through a combination of machine teaching and reinforcement learning techniques. Project Bonsai enables engineers and domain experts to use machine teaching techniques to break down complex problems into smaller parts that can be solved faster using AI algorithms.