Intelligent robots and machines are becoming pervasive in human populated environments. A desirable capability of these agents is to respond to goal-oriented commands by autonomously constructing task plans. However, such autonomy can add significant cognitive load and potentially introduce safety risks to humans when agents behave unexpectedly. Hence, for such agents to be helpful, one important requirement is for them to synthesize plans that can be easily understood by humans. While there exists previous work that studied socially acceptable robots that interact with humans in "natural ways", and work that investigated legible motion planning, there lacks a general solution for high level task planning. To address this issue, we introduce the notions of plan {\it explicability} and {\it predictability}. To compute these measures, first, we postulate that humans understand agent plans by associating abstract tasks with agent actions, which can be considered as a labeling process. We learn the labeling scheme of humans for agent plans from training examples using conditional random fields (CRFs). Then, we use the learned model to label a new plan to compute its explicability and predictability. These measures can be used by agents to proactively choose or directly synthesize plans that are more explicable and predictable to humans. We provide evaluations on a synthetic domain and with human subjects using physical robots to show the effectiveness of our approach

This paper presents a solution to Autonomous Underwater Vehicles (AUVs) large scale route planning and task assignment joint problem. Given a set of constraints (e.g., time) and a set of task priority values, the goal is to find the optimal route for underwater mission that maximizes the sum of the priorities and minimizes the total risk percentage while meeting the given constraints. Making use of the heuristic nature of genetic and swarm intelligence algorithms in solving NP-hard graph problems, Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) are employed to find the optimum solution, where each individual in the population is a candidate solution (route). To evaluate the robustness of the proposed methods, the performance of the all PS and GA algorithms are examined and compared for a number of Monte Carlo runs. Simulation results suggest that the routes generated by both algorithms are feasible and reliable enough, and applicable for underwater motion planning. However, the GA-based route planner produces superior results comparing to the results obtained from the PSO based route planner.

We study the relationship between the sentiment levels of Twitter users and the evolving network structure that the users created by @-mentioning each other. We use a large dataset of tweets to which we apply three sentiment scoring algorithms, including the open source SentiStrength program. Specifically we make three contributions. Firstly we find that people who have potentially the largest communication reach (according to a dynamic centrality measure) use sentiment differently than the average user: for example they use positive sentiment more often and negative sentiment less often. Secondly we find that when we follow structurally stable Twitter communities over a period of months, their sentiment levels are also stable, and sudden changes in community sentiment from one day to the next can in most cases be traced to external events affecting the community. Thirdly, based on our findings, we create and calibrate a simple agent-based model that is capable of reproducing measures of emotive response comparable to those obtained from our empirical dataset.

As I learned from my work in flight dynamics, to keep an airplane flying safely, you have to predict the likelihood of equipment failure. And today we do that by combining various data sets with real-world knowledge, such as the laws of physics. Integrating these two sets of information -- data and human knowledge -- automatically is a relatively new idea and practice. It involves combining human knowledge with a multitude of data sets via data analytics and artificial intelligence to potentially answer critical questions (such as how to cure a specific type of cancer). As a systems scientist who has worked in areas such as robotics and distributed autonomous systems, I see how this integration has changed many industries.

Microsoft made headlines when Tay, the chatbot designed to engage with millennials, was exploited and began tweeting what the company described as "wildly inappropriate and reprehensible words and images." But other companies are quietly implementing similar artificial intelligence technology to interact with current and future customers. Atom Bank, a startup, mobile-only bank based in the U.K. recently announced that it is incorporating WDS Virtual Agent software from Xerox into its mobile app. The machine learning software will give customers an agent-like option for assisted self-service on the app. The software was introduced two year ago to diagnose and solve customer queries by analyzing data and learning from the ways in which human agents diagnosed and solved customer problems.

The new Initiative is global, open and inclusive, welcoming all individuals or representatives of organizations dedicated to ethical considerations in the design of autonomous systems.

It has been humorously said that any discipline that ends with the word "science" isn't a science. Social science, military science, computer science, and library science are such oxymorons. The same joke applies to the word "intelligence." AI is no longer artificial. In a number of ways, an interaction with an automated agent has become indistinguishable from human to human engagement.

Botego develops software solutions based on its proprietary language processing technology. We're an R&D partner in various EU funded projects with offices in New York City, Dubai and Istanbul. Our mission is to increase efficiency and customer satisfaction by automating various processes. Our vision is to make world a better place by creating seamless interaction experience for brands and individuals.

For those of you who don't know who or what Amelia is, she is IPsoft's cognitive agent or, in other words, an Artificial Intelligence agent that can converse with people and act as an electronic call center agent. She can do what I would say is at least 30 percent or more of the work currently performed in today's call centers. When I met Amelia, she read a Wikipedia article and had a conversation about it with us. She effectively operated similar to an eighth-grader's ability to synthesize what was in that article and answer questions. She went on to show how she could converse with us to open bank accounts, help us file insurance claims, or sell us a homeowner's or car insurance policy.

Intelligent agents working in real-time domains need to adapt to changing circumstance so that they can improve their performance and avoid their mistakes. AI agents designed for interactive games, however, typically lack this ability. Game agents are traditionally implemented using static, hand-authored behaviors or scripts that are brittle to changing world dynamics and cause a break in player experience when they repeatedly fail. Furthermore, their static nature causes a lot of effort for the game designers as they have to think of all imaginable circumstances that can be encountered by the agent. The problem is exacerbated as state-of-the-art computer games have huge decision spaces, interactive users, and real-time performance that make the problem of creating AI approaches for these domains harder.