An inconsistent knowledge base can be abstracted as a set of arguments and a defeat relation among them. There can be more than one consistent way to evaluate such an argumentation graph. Collective argument evaluation is the problem of aggregating the opinions of multiple agents on how a given set of arguments should be evaluated. It is crucial not only to ensure that the outcome is logically consistent, but also satisfies measures of social optimality and immunity to strategic manipulation. This is because agents have their individual preferences about what the outcome ought to be. In the current paper, we analyze three previously introduced argument-based aggregation operators with respect to Pareto optimality and strategy proofness under different general classes of agent preferences. We highlight fundamental trade-offs between strategic manipulability and social optimality on one hand, and classical logical criteria on the other. Our results motivate further investigation into the relationship between social choice and argumentation theory. The results are also relevant for choosing an appropriate aggregation operator given the criteria that are considered more important, as well as the nature of agents' preferences.

In mid-January, the ongoing race for AI put Montreal-based Maluuba on our radar. Microsoft acquired the startup and its team of researchers to build better machine intelligence tools for analyzing unstructured text to enable more natural human computer interaction -- think bots that can actually respond with reasonable intelligence to a text you send. The team dropped its first paper since being acquired and it sheds light on what the group's priorities are. The paper outlines a method for multi-advisor reinforcement learning that breaks problems down to be simpler and more easily computable. In oversimplified terms, Maluuba is effectively trying to teach leadership to groups of machines working to solve problems.

AI assistants now surround us. You can summon them from your phone, from devices in your home, and from your inbox. But they are not all created equal. Some enable and coordinate other services. Others get a single job done exceptionally well, whether that's scheduling meetings or writing data-driven stories.

Last year, Alphabet's DeepMind division captured the world's attention by besting humanity's top player in the game of Go. The achievement, which many experts predicted was still a decade off, showed the rapid progress being made in the world of artificial intelligence. DeepMind subsequently announced that its next goal in gaming was mastering StarCraft, a classic PC game that is a staple of competitive e-sports. Facebook also threw its hat in the ring, creating an open-source framework so that developers could work on solving StarCraft using the social network's AI toolkit. Now a team from China's Alibaba has published a paper describing a system that learned to execute a number of strategies employed by high-level players without being given any specific instruction on how best to manage combat.

The Belief-Desire-Intention (BDI) architecture is a practical approach for modelling large-scale intelligent systems. In the BDI setting, a complex system is represented as a network of interacting agents - or components - each one modelled based on its beliefs, desires and intentions. However, current BDI implementations are not well-suited for modelling more realistic intelligent systems which operate in environments pervaded by different types of uncertainty. Furthermore, existing approaches for dealing with uncertainty typically do not offer syntactical or tractable ways of reasoning about uncertainty. This complicates their integration with BDI implementations, which heavily rely on fast and reactive decisions. In this paper, we advance the state-of-the-art w.r.t. handling different types of uncertainty in BDI agents. The contributions of this paper are, first, a new way of modelling the beliefs of an agent as a set of epistemic states. Each epistemic state can use a distinct underlying uncertainty theory and revision strategy, and commensurability between epistemic states is achieved through a stratification approach. Second, we present a novel syntactic approach to revising beliefs given unreliable input. We prove that this syntactic approach agrees with the semantic definition, and we identify expressive fragments that are particularly useful for resource-bounded agents. Third, we introduce full operational semantics that extend CAN, a popular semantics for BDI, to establish how reasoning about uncertainty can be tightly integrated into the BDI framework. Fourth, we provide comprehensive experimental results to highlight the usefulness and feasibility of our approach, and explain how the generic epistemic state can be instantiated into various representations.

Despite tremendous progress, artificial intelligence is still limited in many ways. For example, in computer games, if an AI agent is not pre-programmed with game rules, it must try millions of times before figuring out the right moves to win. Humans can accomplish the same feat in a much shorter time, because we are good at transferring past knowledge to new tasks by using language. In a game in which you must kill a dragon to win, an AI agent would need to try many other actions (firing at wall, a patch of flowers, etc) before understanding that it must kill the dragon. However, if the AI agent understood language, a human could simply use language to instruct it to: "kill the dragon to win the game."

This article originally appeared in the March 2017 issue of IEEE Robotics & Automation Magazine. We thank RAM and the authors for giving us permission to reproduce it here. Algorithms with learning abilities collect personal data that are then used without users' consent and even without their knowledge; autonomous weapons are under discussion in the United Nations; robots stimulating emotions are deployed with vulnerable people; research projects are funded to develop humanoid robots; and artificial intelligence-based systems are used to evaluate people. One can consider these examples of AI and autonomous systems (AS) as great achievements or claim that they are endangering human freedom and dignity. We need to make sure that these technologies are aligned to humans in terms of our moral values and ethical principles to fully benefit from the potential of them.

How to Cut Cake Fairly.

Poaching is considered a major driver for the population drop of key species such as tigers, elephants, and rhinos, which can be detrimental to whole ecosystems. While conducting foot patrols is the most commonly used approach in many countries to prevent poaching, such patrols often do not make the best use of the limited patrolling resources.

Baidu's artificial intelligence research team has achieved a significant milestone: teaching a virtual agent'living' in a 2D environment how to navigate its world using natural language commands, by first teaching it language through positive and negative reinforcement. The especially exciting thing, according to the scientists, is that the agent ended up developing a "zero-shot learning ability," which essentially means that the AI agent developed a basic sense of grammar. You probably don't remember it from personal experience because it happened when you were a baby, but this is basically how parents teach their kids when very young. You show them images, repeat words, and eventually, with enough positive reinforcement, the kid can associate those words with those images and voila – it knows the names of things. Baidu's big breakthrough, though, is that the agent within its system can apply commands it's learned to new situations – computers aren't great at taking knowledge acquired before and applying it to new things.