We use artificial intelligence (AI) every day without knowing it. Alexa's speech recognition, Netflix's movie recommendations and Gmail's type-ahead suggestions are all examples of data you share being fed into deep learning AI models to improve your experience. At work, the same technology is increasingly used to deliver smart services -- rooms that book themselves, thermostats that don't cool empty floors and outages that are restored before anyone knows they exist. Achieving Amazon-quality AI with "small data" from your organization is a more complex technical problem. To get enterprise AI right at scale requires thinking differently about how applications and services are deployed and managed.

Security games are an example of a successful real-world application of game theory. The paper defines blameworthiness of the defender and the attacker in security games using the principle of alternative possibilities and provides a sound and complete logical system for reasoning about blameworthiness in such games. Introduction In this paper we study the properties of blameworthiness in security games (von Stackelberg 1934). Security games are used for canine airport patrol (Pita et al. 2008; Jain et al. 2010), airport passenger screening (Brown et al. 2016), protecting endangered animals and fish stocks (Fang, Stone, and Tambe 2015), U.S. Coast Guard port patrol (Sinha et al. 2018; An, Tambe, and Sinha 2016), and randomized deployment of U.S. air marshals (Sinha et al. 2018). Defender \Attacker Terminal 1 Terminal 2 Terminal 1 20 120 Terminal 2 200 16 Figure 1: Expected Human Losses in Security Game G 1. As an example, consider a security game G 1 in which a defender is trying to protect two terminals in an airport from an attacker. Due to limited resources, the defender can patrol only one terminal at a given time. If the defender chooses to patrol Terminal 1 and the attacker chooses to attack Terminal 2, then the human losses at Terminal 2 are estimated at 120, see Figure 1. However, if the defender chooses to patrol Terminal 2 while the attacker still chooses to attack Terminal 2, then the expected number of the human losses at Terminal 2 is only 16, see Figure 1. Generally speaking, the goal of the defender is to minimize human losses, while the goal of the attacker is to maximize them. However, the utility functions in security games usually take into account not only the human losses, but also the cost to protect and to attack the target to the defender and the attacker respectively.

Algorithms, software and smart technologies have a growing presence in cities around the world. Artificial intelligence (AI), agent-based modelling, the internet of things and machine learning can be found practically everywhere now – from lampposts to garbage bins, traffic lights and cars. Not only that, these technologies are also influencing how cities are planned, guiding big decisions about new buildings, transport and infrastructure projects. City-dwellers tend to accept the presence of these technologies passively – if they notice it at all. Yet this acceptance is punctuated by intermittent panic over privacy – take, for example, Transport for London's latest plans to track passenger journeys across the transport network using wifi, which drew criticism from privacy experts.

Researchers from USC and the University of Denver created a simulation in which a three-person team was supported by a virtual agent avatar on screen in a mission that was designed to ensure failure and elicit conflict. The study was designed to look at virtual agents as potential mediators to improve team collaboration during conflict mediation. But in the heat of the moment, will we listen to virtual agents? While some of researchers (Gale Lucas and Jonathan Gratch of the USC Viterbi School Engineering and the USC Institute for Creative Technologies who contributed to this study), had previously found that one-on-one human interactions with a virtual agent therapist yielded more confessions, in this study "Conflict Mediation in Human-Machine Teaming: Using a Virtual Agent to Support Mission Planning and Debriefing," team members were less likely to engage with a male virtual agent named "Chris" when conflict arose. Participating members of the team did not physically accost the device (as we have seen humans attack robots in viral social media posts), but rather were less engaged and less likely to listen to the virtual agent's input once failure ensued and conflict arose among team members. The study was conducted in a military academy environment in which 27 scenarios were engineered to test how the team that included a virtual agent would react to failure and the ensuring conflict.

Generating complex multi-turn goal-oriented dialogue agents is a difficult problem that has seen a considerable focus from many leaders in the tech industry, including IBM, Google, Amazon, and Microsoft. This is in large part due to the rapidly growing market demand for dialogue agents capable of goal-oriented behaviour. Due to the business process nature of these conversations, end-to-end machine learning systems are generally not a viable option, as the generated dialogue agents must be deployable and verifiable on behalf of the businesses authoring them. In this work, we propose a paradigm shift in the creation of goal-oriented complex dialogue systems that dramatically eliminates the need for a designer to manually specify a dialogue tree, which nearly all current systems have to resort to when the interaction pattern falls outside standard patterns such as slot filling. We propose a declarative representation of the dialogue agent to be processed by state-of-the-art planning technology. Our proposed approach covers all aspects of the process; from model solicitation to the execution of the generated plans/dialogue agents. Along the way, we introduce novel planning encodings for declarative dialogue synthesis, a variety of interfaces for working with the specification as a dialogue architect, and a robust executor for generalized contingent plans. We have created prototype implementations of all components, and in this paper, we further demonstrate the resulting system empirically.

P .B. Sujit IIIT Delhi sujit@iiitd.ac.in Abstract --In this paper, we consider a territory guarding game involving pursuers, evaders and a target in an environment that contains obstacles. The goal of the evaders is to capture the target, while that of the pursuers is to capture the evaders before they reach the target. All the agents have limited sensing range and can only detect each other when they are in their observation space. We focus on the challenge of effective cooperation between agents of a team. Finding exact solutions for such multi-agent systems is difficult because of the inherent complexity. We present Multi-Agent Pursuer-Evader Learning (MAPEL), a class of algorithms that use spatiotemporal graph representation to learn structured cooperation. The key concept is that the learning takes place in a decentralized manner and agents use situation report updates to learn about the whole environment from each others' partial observations. We use Recurrent Neural Networks (RNNs) to parameterize the spatiotemporal graph. An agent in MAPEL only updates all the other agents if an opponent or the target is inside its observation space by using situation report. We present a detailed analysis of how these two cooperation methods perform when the number of agents in the game are increased. We provide empirical results to show how agents cooperate under these two methods.

Analyst Forrester defines robotic process automation (RPA) as a technology that provisions software agents – bots – that can mimic human interactions with software systems. These bots run predictable tasks, and act either in concert with humans (attended RPA) or mostly autonomously (unattended RPA). Increasingly, RPA is adding artificial intelligence (AI)-based capabilities, such as reading unstructured data. IT research firm Computer Economics says in its April 2019 Technology trends report that bots are typically taught by human example to respond to various triggers. For example, when an employee submits a change of address form to the human resources (HR) department, the bot could then be used to trigger an update to the records in payroll, benefits systems, expense reporting and accounts payable, just as a human clerical worker might do.

Microsoft is rolling out two new software solutions today to help brick-and-mortar retailers track customers and improve service. Dynamics 365 Connected Store will utilize a combination of computer vision, cameras, and IoT sensors to track customers inside stores and personalize recommendations based on their browsing and buying behavioral data. Events detected by Connected Store will trigger web app notifications or email alerts for in-store staff with predictions to improve sales and store efficiency. Connected Store can do things like sense a need for more staff at checkout counters, identify issues with store equipment, and surface long-term trends for managers. Also new is Dynamics 365 Commerce, software that pulls insights from businesses that have ecommerce and brick-and-mortar retail operations. Dynamics 365 Commerce will also use call center and back office data to power customer personalization.

From Gary Kasparov to Elon Musk, the list of those who say AI needs to be applied such that it augments us, not compete with us, is long. Yet the supply of reports warning that AI threatens jobs doesn't seem to have an end. On the other hand, a new report looking at a technology called Swarm AI may provide a much more benign fix. Speaking at a recent conference, chess legend, Gary Kasparov, said that the public perception of AI has been overly influenced by Hollywood: the reality is far more positive -- Kasparov's take on AI is a reason for optimism Swarms can be intelligent-- there is no great insight here. Those who study Emergence understand this, from ant colonies to cities, great things can be achieved from simpler entities working together.

In this paper, we propose a gamification approach as a novel framework for smart building infrastructure with the goal of motivating human occupants to reconsider personal energy usage and to have positive effects on their environment. Human interaction in the context of cyber-physical systems is a core component and consideration in the implementation of any smart building technology. Research has shown that the adoption of human-centric building services and amenities leads to improvements in the operational efficiency of these cyber-physical systems directed towards controlling building energy usage. We introduce a strategy in form of a game-theoretic framework that incorporates humans-in-the-loop modeling by creating an interface to allow building managers to interact with occupants and potentially incentivize energy efficient behavior. Prior works on game theoretic analysis typically rely on the assumption that the utility function of each individual agent is known a priori. Instead, we propose novel utility learning framework for benchmarking that employs robust estimations of occupant actions towards energy efficiency. To improve forecasting performance, we extend the utility learning scheme by leveraging deep bi-directional recurrent neural networks. Using the proposed methods on data gathered from occupant actions for resources such as room lighting, we forecast patterns of energy resource usage to demonstrate the prediction performance of the methods. The results of our study show that we can achieve a highly accurate representation of the ground truth for occupant energy resource usage. We also demonstrate the explainable nature on human decision making towards energy usage inherent in the dataset using graphical lasso and granger causality algorithms. Finally, we open source the de-identified, high-dimensional data pertaining to the energy game-theoretic framework.