Researchers from BYU (Brigham Young University) were successful in giving common sense to the artificial intelligence agents with the help of Wikipedia. Walk into a room, see a chair, and your brain will tell you that you can sit in it, tip it over or lift it up, but you wouldn't even consider drinking it, promoting it or unlocking it. As humans, explains BYU computer science professor David Wingate, we know intuitively that certain verbs pair naturally with certain nouns, and we also know that most verbs don't make sense when paired with random nouns. "Consider the monitor on your desk: you can look at it, you can turn it on, you can even pick it up or throw it, but you cannot impeach it, transpose it, justify it or correct it," said Wingate. "You can dethrone a king or worship him or obey him, but you cannot unlock him or calendar him or harvest him." That intuition, for the most part, doesn't exist with computer artificial intelligence agents, who are good at identifying objects but less so in knowing what to do with them.

Different agents may have different points of view. Following a popular approach in the artificial intelligence literature, this can be modelled by means of different abstract argumentation frameworks, each consisting of a set of arguments the agent is contemplating and a binary attack-relation between them. A question arising in this context is whether the diversity of views observed in such a profile of argumentation frameworks is consistent with the assumption that every individual argumentation framework is induced by a combination of, first, some basic factual attack-relation between the arguments and, second, the personal preferences of the agent concerned regarding the moral or social values the arguments under scrutiny relate to. We treat this question of rationalisability of a profile as an algorithmic problem and identify tractable and intractable cases. In doing so, we distinguish different constraints on admissible rationalisations, e.g., concerning the types of preferences used or the number of distinct values involved. We also distinguish two different semantics for rationalisability, which differ in the assumptions made on how agents treat attacks between arguments they do not report. This research agenda, bringing together ideas from abstract argumentation and social choice, is useful for understanding what types of profiles can reasonably be expected to occur in a multiagent system.

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In the smart grid, the intent is to use flexibility in demand, both to balance demand and supply as well as to resolve potential congestion. A first prominent example of such flexible demand is the charging of electric vehicles, which do not necessarily need to be charged as soon as they are plugged in. The problem of optimally scheduling the charging demand of electric vehicles within the constraints of the electricity infrastructure is called the charge scheduling problem. The models of the charging speed, horizon, and charging demand determine the computational complexity of the charge scheduling problem. For about 20 variants, we show, using a dynamic programming approach, that the problem is either in P or weakly NP-hard. We also show that about 10 variants of the problem are strongly NP-hard, presenting a potentially significant obstacle to their use in practical situations of scale.

Abstract-- Swarm systems constitute a challenging problem for reinforcement learning (RL) as the algorithm needs to learn decentralized control policies that can cope with limited local sensing and communication abilities of the agents. Although there have been recent advances of deep RL algorithms applied to multi-agent systems, learning communication protocols while simultaneously learning the behavior of the agents is still beyond the reach of deep RL algorithms. However, while it is often difficult to directly define the behavior of the agents, simple communication protocols can be defined more easily using prior knowledge about the given task. In this paper, we propose a number of simple communication protocols that can be exploited by deep reinforcement learning to find decentralized control policies in a multi-robot swarm environment. The protocols are based on histograms that encode the local neighborhood relations of the agents and can also transmit task-specific information, such as the shortest distance and direction to a desired target. In our framework, we use an adaptation of Trust Region Policy Optimization to learn complex collaborative tasks, such as formation building, building a communication link, and pushing an intruder. We evaluate our findings in a simulated 2D-physics environment, and compare the implications of different communication protocols. I. INTRODUCTION Nature provides many examples where the performance of a collective of limited beings exceeds the capabilities of one individual. Ants transport prey of the size no single ant could carry, termites build nests of up to nine meters in height, and bees are able to regulate the temperature of a hive.

Drones and other autonomous robots require mobile and efficient solutions to real-life issues, from mundane package transportation to urgent search and rescue missions. Using machine learning and a vector-based navigation system inspired by insects, agents could navigate to key locations without relying on a GPS -- becoming truly autonomous. Robots could learn to navigate independently to wildfires based on environmental sensory cues, using information from cameras and other sensors. Since vectors are represented in a geocentric context, multiple agents could communicate locations with each other, which could, for example, speed up efforts to perform rescues and put out fires. Such flexibility and speed of coordination would largely improve the success and efficiency of rescue missions during natural disasters -- and save lives.

This paper considers a discrete-time opinion dynamics model in which each individual's susceptibility to being influenced by others is dependent on her current opinion. We assume that the social network has time-varying topology and that the opinions are scalars on a continuous interval. We first propose a general opinion dynamics model based on the DeGroot model, with a general function to describe the functional dependence of each individual's susceptibility on her own opinion, and show that this general model is analogous to the Friedkin-Johnsen model, which assumes a constant susceptibility for each individual. We then consider two specific functions in which the individual's susceptibility depends on the \emph{polarity} of her opinion, and provide motivating social examples. First, we consider stubborn positives, who have reduced susceptibility if their opinions are at one end of the interval and increased susceptibility if their opinions are at the opposite end. A court jury is used as a motivating example. Second, we consider stubborn neutrals, who have reduced susceptibility when their opinions are in the middle of the spectrum, and our motivating examples are social networks discussing established social norms or institutionalized behavior. For each specific susceptibility model, we establish the initial and graph topology conditions in which consensus is reached, and develop necessary and sufficient conditions on the initial conditions for the final consensus value to be at either extreme of the opinion interval. Simulations are provided to show the effects of the susceptibility function when compared to the DeGroot model.

This paper sets out to resolve how agents ought to act in the Sleeping Beauty problem and various related anthropic (self-locating belief) problems, not through the calculation of anthropic probabilities, but through finding the correct decision to make. It creates an anthropic decision theory (ADT) that decides these problems from a small set of principles. By doing so, it demonstrates that the attitude of agents with regards to each other (selfish or altruistic) changes the decisions they reach, and that it is very important to take this into account. To illustrate ADT, it is then applied to two major anthropic problems and paradoxes, the Presumptuous Philosopher and Doomsday problems, thus resolving some issues about the probability of human extinction.

Our two previous blog entries implied that there is a role games can play in driving the development of Reinforcement Learning algorithms. As the world's most popular creation engine, Unity is at the crossroads between machine learning and gaming. It is critical to our mission to enable machine learning researchers with the most powerful training scenarios, and for us to give back to the gaming community by enabling them to utilize the latest machine learning technologies. As the first step in this endeavor, we are excited to introduce Unity Machine Learning Agents. Machine Learning is changing the way we expect to get intelligent behavior out of autonomous agents.

Augment today announced it has raised $5 million for an AI platform that assists customer service agents at large companies. The startup had operated in stealth for 10 months prior to launch. The company joins competitors like Mattersight, DigitalGenius, LivePerson, and others in its efforts to train AI using conversations between customers and businesses in order to better guide customer service agents. The money will be used to bolster the Augment AI platform, which is trained by an aggregated dataset made up of 100 million conversational interactions at large companies, including Dyson. Augment makes no attempt to replace human agents, only to make them more efficient.