Multiagent environments where agents compete for resources are stepping stones on the path to AGI. Multiagent environments have two useful properties: first, there is a natural curriculum -- the difficulty of the environment is determined by the skill of your competitors (and if you're competing against clones of yourself, the environment exactly matches your skill level). Second, a multiagent environment has no stable equilibrium: no matter how smart an agent is, there's always pressure to get smarter. These environments have a very different feel from traditional environments, and it'll take a lot more research before we become good at them. We've developed a new algorithm, MADDPG, for centralized learning and decentralized execution in multiagent environments, allowing agents to learn to collaborate and compete with each other.

Freelance workers enable the automation of their work -- ironically. AGI (artificial general intelligence) is the pursuit of machine cognition, largely still a work in progress. Pat Langley from Arizona State University, has an excellent essay highlighting the differences between most of what's labeled "AI", and what he refers to as the'Cognitive Systems Paradigm'. A cognitive system has the machinery to begin working with written language, use heuristics and other approaches to deal with incomplete data, make inferences from structured representations of information, and so on. Some current AGI frameworks can productively be part of this type of work, although unfortunately many projects are not yet well documented or openly available.

In a winning scenario, smiling can decrease your odds of success against the same opponent in subsequent matches, according to new research presented by the USC Institute for Creative Technologies and sponsored by the U.S. Army Research Laboratory. People who smiled during victory increased the odds of their opponent acting aggressively to steal a pot of money rather than share it in future gameplay, according to a paper presented in May at the International Conference on Autonomous Agents and Multiagent Systems by USC ICT research assistant Rens Hoegen, USC ICT research programmer Giota Stratou and Jonathan Gratch, director of virtual humans research at USC ICT and a professor of computer science at the USC Viterbi School of Engineering. Conversely, researchers found smiling during a loss tended to help the odds of success in the game going forward. The study is in line with previous research published by senior author Gratch, whose main interest lies both in how people express these tells -- an unconscious action that betrays deception -- and using this data to create artificial intelligence to discern and even express these same emotional cues as a person. "We think that emotion is the enemy of reason. But the truth is that emotion is our way of assigning value to things," said Gratch.

The dichotomy between machines and living things is narrowing. Today, artificial intelligence (AI) is embedded in all kinds of technology, from robots to social networks. This affects the youngest among us as we see the emergence of an "Internet of Toys." The trend is what prompted us to explore the impact of those "smart," interconnected playthings on children. We'll present our paper, "Hey Google, is it OK if I eat you?: Initial Explorations in Child-Agent Interaction," at the Interaction Design and Children conference at Stanford University on June 27.

Any denizen of these digital days can't help but feel surrounded by awesomely intelligent devices that seemingly know and control all. From artificially intelligently enabled mattresses that understand your sleep patterns better than you, to adaptively learning apps that anticipate your never-varying morning beverage order. When did this explosion of computer intelligence occur, and, really, how much easier is your life as a result? To be sure, artificial intelligence (AI) and machine learning (ML) have made, and continue to make enormous strides with an accelerating pace. Any industry or business that is not developing and embracing these technologies will certainly perish.

Enterprise applications vendor Infor recently showed a concept design for a similar agent, coincidentally also called Max. Project Max … uses intelligent automation across Infor's Sales Suite products to provide timely information, reminders and action prompts to sales reps as they're working out in the field. Now unbundling has reached the field of enterprise applications, due to the combined effect of integrated cloud platforms, smart mobile devices and intelligent agents. Expect to see new models of enterprise organization and teamwork to emerge as new functional bundles of automation enable new ways of working together to achieve business outcomes.

We develop a two-defender (Alice and Bob) invasion game using the method of projective simulation as an embodied model for artificial intelligence. We hope that it will be the first step towards the effect of perception on different actions in a given game. As a given perception of a given situation, the agent, say Alice, encounters some attack symbols coming from the right attacker where she can learn to prevent. However, some of these percepts are invisible for her. Instead, she perceives some other signs that are related to her partner's (Bob) task. We elaborate an example in which an agent perceives an equal portion of percepts from both attackers. Alice can choose to concentrate on her job, though she loses some attacks. Alternatively, she can have some sort of cooperation with Bob to get and give help. It follows that the maximum blocking efficiency in concentration is just the minimum blocking efficiency in cooperation. Furthermore, Alice would have a choice to select two different forgetting factors for blocking attacks and for helping task. Therefore, she can choose between herself and the other. Consequently, selfishness is discerned as an only Nash equilibrium in this game. It is a pure strategy and Pareto optimal and containing Shapley value in this superadditive coalition. Finally, we propose another perception for the same situation that can be tracked in the future regarding the present study.

Artificial Intelligence is poised to disrupt the entire eCommerce industry. An interesting convergence is taking place; one that will have enormous implications in the way retailers sell their products and services and the way consumers buy them. Artificial Intelligence capabilities and applications are attempting to solve real-world issues that eCommerce industry are facing. How Artificial Intelligence in e-commerce can play an important and game changing role, moving beyond customer segmentation to help them achieve the best possible results? The visual Search engine is one of the most exciting trends of Artificial Intelligence in eCommerce.

The recently proposed DeGroot-Friedkin model describes the dynamical evolution of individual social power in a social network that holds opinion discussions on a sequence of different issues. This paper revisits that model, and uses nonlinear contraction analysis, among other tools, to establish several novel results. First, we show that for a social network with constant topology, each individual's social power converges to its equilibrium value exponentially fast, whereas previous results only concluded asymptotic convergence. Second, when the network topology is dynamic (i.e., the relative interaction matrix may change between any two successive issues), we show that each individual exponentially forgets its initial social power. Specifically, individual social power is dependent only on the dynamic network topology, and initial (or perceived) social power is forgotten as a result of sequential opinion discussion. Last, we provide an explicit upper bound on an individual's social power as the number of issues discussed tends to infinity; this bound depends only on the network topology. Simulations are provided to illustrate our results.

Natural-language-facilitated human-robot cooperation (NLC), in which natural language (NL) is used to share knowledge between a human and a robot for conducting intuitive human-robot cooperation (HRC), is continuously developing in the recent decade. Currently, NLC is used in several robotic domains such as manufacturing, daily assistance and health caregiving. It is necessary to summarize current NLC-based robotic systems and discuss the future developing trends, providing helpful information for future NLC research. In this review, we first analyzed the driving forces behind the NLC research. Regarding to a robot s cognition level during the cooperation, the NLC implementations then were categorized into four types {NL-based control, NL-based robot training, NL-based task execution, NL-based social companion} for comparison and discussion. Last based on our perspective and comprehensive paper review, the future research trends were discussed.