Google Cloud is making Contact Center AI generally available for use today. The cloud service is built with Dialogflow of the conversational AI engine to automate interactions with customers in call centers. Contact Center AI contains Virtual Agent to automatically respond to customer inquiries with voice or text or transfer the conversation to a person when a bot cannot help a client. Agent Assist uses natural language processing to increase customer service agent interactions with customers when a bot cannot help a client. The news comes today when Google launched its rich communication services (RCS) to Android message users in the United States, and days after Google's experimental unit, the Area 120 CallJoy service to answer phone calls and questions Small business customers get an update.

A famous television series kicked off with its first episode titled "Winter is Coming." This phrase ended up carrying more weight than just the arrival of a season in the fictional series. In real life, some might say another monumental event is nearly upon us: Black Friday. References to Black Friday began as early as the 1950's in the United States. It wasn't until the 1980's that it came to refer to the retail shopping period following the Thanksgiving holiday, with one explanation suggesting the color indicated this being the time at which retail companies moved from operating at a loss (or "in the red") to profitability ("in the black").

Google has announced a notable upgrade to CallJoy, the automated, cloud-based telephone customer service agent first debuted back in May. CallJoy is the handiwork of Area 120, Google's "experimental" off-shoot, so it's not technically a full-fledged Google product yet. But the service is chargeable and available to all businesses across the U.S., so for all intents and purposes this really is an official Google SMB product. Over the past four months, businesses that signed up to CallJoy for $39 per month (per location) were given a virtual phone number that is capable of blocking spam calls; providing basic business information such as opening hours; and serving up analytics, including call recordings with automated transcriptions of each call. The automated agent could also send the caller an SMS link to book an appointment or place an order.

The Pommerman simulation was recently developed to mimic the classic Japanese game Bomberman, and focuses on competitive gameplay in a multi-agent setting. We focus on the 2$\times$2 team version of Pommerman, developed for a competition at NeurIPS 2018. Our methodology involves training an agent initially through imitation learning on a noisy expert policy, followed by a proximal-policy optimization (PPO) reinforcement learning algorithm. The basic PPO approach is modified for stable transition from the imitation learning phase through reward shaping, action filters based on heuristics, and curriculum learning. The proposed methodology is able to beat heuristic and pure reinforcement learning baselines with a combined 100,000 training games, significantly faster than other non-tree-search methods in literature. We present results against multiple agents provided by the developers of the simulation, including some that we have enhanced. We include a sensitivity analysis over different parameters, and highlight undesirable effects of some strategies that initially appear promising. Since Pommerman is a complex multi-agent competitive environment, the strategies developed here provide insights into several real-world problems with characteristics such as partial observability, decentralized execution (without communication), and very sparse and delayed rewards.

We consider the issue of multiple agents learning to communicate through reinforcement learning within partially observable environments, with a focus on information asymmetry in the second part of our work. We provide a review of the recent algorithms developed to improve the agents' policy by allowing the sharing of information between agents and the learning of communication strategies, with a focus on Deep Recurrent Q-Network-based models. We also describe recent efforts to interpret the languages generated by these agents and study their properties in an attempt to generate human-language-like sentences. We discuss the metrics used to evaluate the generated communication strategies and propose a novel entropy-based evaluation metric. Finally, we address the issue of the cost of communication and introduce the idea of an experimental setup to expose this cost in cooperative-competitive game.

De-An Huang 1, 2, Y u-Wei Chao, 2, Chris Paxton, 2, Xinke Deng 2, 3, Li Fei-Fei 1, Juan Carlos Niebles 1, Animesh Garg 2, 4, Dieter Fox 2, 5 Abstract -- We address goal-based imitation learning, where the aim is to output the symbolic goal from a third-person video demonstration. This enables the robot to plan for execution and reproduce the same goal in a completely different environment. The key challenge is that the goal of a video demonstration is often ambiguous at the level of semantic actions. The human demonstrators might unintentionally achieve certain subgoals in the demonstrations with their actions. Our main contribution is to propose a motion reasoning framework that combines task and motion planning to disambiguate the true intention of the demonstrator in the video demonstration. This allows us to robustly recognize the goals that cannot be disambiguated by previous action-based approaches. We evaluate our approach by collecting a dataset of 96 video demonstrations in a mockup kitchen environment. We show that our motion reasoning plays an important role in recognizing the actual goal of the demonstrator and improves the success rate by over 20%. We further show that by using the automatically inferred goal from the video demonstration, our robot is able to reproduce the same task in a real kitchen environment. I NTRODUCTION We are interested in allowing robots to learn new tasks from video demonstrations. Recently, there has been rapid progress in imitation learning [1-4], which even enables learning a new task from a single demonstration of the task [5-7]. By leveraging meta-learning [8], the robot learns to follow the actions in the demonstration.

Machine learning (ML) is changing our lives. We can instantly translate from one language to another, search entire libraries in a matter of seconds, and even prevent credit card fraud. ML's success is mostly due to the power of artificial neural networks -- a machine learning model inspired by how the brain works -- massive datasets, and a lot of computational power. However, while these ML applications are making our lives easier, we have not really solved the artificial intelligence (AI) agent. A true AI agent should be able to perform well in a broad range of tasks.

Books published in this series focus on the theory and computational foundations of artificial intelligence, ideally combining a mathematically rigorous treatment of a contemporary topic in artificial intelligence with an appreciation of the relevant computational issues such as algorithmic foundations or complexity theoretic analysis. Submitted proposals should be coherent monographs, rather than collections of articles. Authoritative surveys and expositions of advanced topics are welcomed. The intended readership is research students and researchers seeking an authoritative treatment of an advanced topic in the foundations of artificial intelligence. Topics considered include AI and operations research; constraint systems and satisfiability; knowledge representation and reasoning; machine learning and data mining; multi-agent systems and economic models in AI; planning and scheduling; preferences, utility, and decision-making; robotics and vision; search algorithms; and uncertainty handling.

This paper presents aplib, a Java library for programming intelligent agents, featuring BDI and multi agency, but adding on top of it a novel layer of tactical programming inspired by the domain of theorem proving. Aplib is also implemented in such a way to provide the fluency of a Domain Specific Language (DSL). Compared to dedicated BDI agent programming languages such as JASON, 2APL, or GOAL,aplib's embedded DSL approach does mean that \aplib\ programmers will still be limited by Java syntax, but on other hand they get all the advantages that Java programmers get: rich language features (object orientation, static type checking, $\lambda$-expression, libraries, etc), a whole array of development tools, integration with other technologies, large community, etc.

We consider Social Distance Games (SDGs), that is cluster formation games in which the utility of each agent only depends on the composition of the cluster she belongs to, proportionally to her harmonic centrality, i.e., to the average inverse distance from the other agents in the cluster. Under a non-cooperative perspective, we adopt Nash stable outcomes, in which no agent can improve her utility by unilaterally changing her coalition, as the target solution concept. Although a Nash equilibrium for a SDG can always be computed in polynomial time, we obtain a negative result concerning the game convergence and we prove that computing a Nash equilibrium that maximizes the social welfare is NP-hard by a polynomial time reduction from the NP-complete Restricted Exact Cover by 3-Sets problem. We then focus on the performance of Nash equilibria and provide matching upper bound and lower bounds on the price of anarchy of Θ(n), where n is the number of nodes of the underlying graph. Moreover, we show that there exists a class of SDGs having a lower bound on the price of stability of 6/5 − ε, for any ε > 0. Finally, we characterize the price of stability 5 of SDGs for graphs with girth 4 and girth at least 5, the girth being the length of the shortest cycle in the graph.