In 1950, Alan Turing anticipated the rise of Artificial Intelligence (AI) with his "Turing Test", which imagined a conversation between a computer and a human and declared that if the human couldn't tell if they were talking to a computer then it must be exhibiting intelligent behaviour. In 2014 the Turing test was finally declared "passed for the first time". Here at Intelligent Environments we've seen this opportunity emerge as increasing numbers of our clients have come to us to discuss how to capitalise on chat and instant messaging to improve their customer service offering. Live chat now delivers the highest satisfaction levels for any customer service channel at nearly 75%. The next few years is likely to see a boom in the use of chat bots in commerce and Gartner predicts that by 2020 autonomous software agents will participate in 5% of all economic transactions.

Major AI researchers and textbooks define the field as "the study and design of intelligent agents", where an intelligent agent is a system that perceives its environment and takes actions that maximize its chances of success. John McCarthy, who coined the term in 1955, defines it as "The science and engineering of making intelligent machines". AI research is highly technical and specialized, deeply divided into subfields that often fail to communicate with each other. Some of the division is due to social and cultural factors: subfields have grown up around particular institutions and the work of individual researchers. AI research is also divided by several technical issues.

Social insects provide us with a powerful metaphor to create decentralized systems of simple interacting, and often mobile, agents. The emergent collective intelligence of social insects - swarm intelligence - resides not in complex individual abilities but rather in networks of interactions that exist among individuals and between individuals and their environment. The study of ant colonies behavior and of their self-organizing capabilities is of interest to knowledge retrieval/ management and decision support systems sciences, because it provides models of distributed adaptive organization which are useful to solve difficult optimization, classification, and distributed control problems, among others. In the present work we overview some models derived from the observation of real ants, emphasizing the role played by stigmergy as distributed communication paradigm, and we present a novel strategy (ACLUSTER) to tackle unsupervised data exploratory analysis as well as data retrieval problems. Moreover and according to our knowledge, this is also the first application of ant systems into digital image retrieval problems.

Border control agencies are already using self-service kiosks to manage the crowds of international travelers entering their countries, but a high-tech type of kiosk in development can do more than just scan passports. The AVATAR--which stands for Automated Virtual Agent for Truth Assessments in Real-Time--can detect travelers trying to lie their way through customs, according to Vocativ. The self-service kiosks, created by the National Center for Border Security and Immigration at the University of Arizona in partnership with the Department of Homeland Security [PDF], scan travelers' passports and ask the kinds of questions posed by human agents, such as "Do you have any fruits or vegetables?" Sensors can identify body cues like facial expression, vocal tics, pupil dilation--and even cues that human agents can't see, like cardiorespiratory data--which could indicate that the person is lying and should be subject to additional screening. They can even see that you're curling your toes, according to a press statement from AVATAR researcher Aaron Elkins of San Diego State University, a professor who studies deception. The kiosks can be programmed to display several virtual agents, choosing from a woman or a man and a stern or a friendly face.

Automotive supplier Delphi has made a of a habit of showing off its self-driving and other research vehicles at CES in recent years, and 2017 is no different. Except now it's ready to commit to a 2019 launch date for its self-driving suite for automakers. I got to take a ride in a specially outfitted Audi on the streets of Las Vegas and walked away impressed. There's no shortage of autonomous systems being developed by automakers. Each uses a slightly different strategy to unlock the complex puzzle of a car driving down the road on its own without putting the occupants and those around it in danger.

Long before Skynet released the Terminator on our unsuspecting world, the idea of artificially intelligent robots taking over the world was a common recurrence in pop culture. Despite us being many years away from that grim probability, current artificial intelligence (AI) developments have many practical uses in today's world, especially when it comes to customer engagement.

Travelers in the US and Canada may soon be forced to undergo a lie detector test as a standard part of airport security. The Automated Virtual Agent for Truth Assessments in Real Time (AVATAR) is currently being tested by the Canadian Border Services Agency and the US Department of Homeland Security. The robot -- programmed to look for physiological changes that indicate lying through eye-detection software and other sensors -- could help border agents catch terrorists or drug traffickers, according to San Diego State University researchers. "AVATAR is a kiosk, much like an airport check-in or grocery store self-checkout kiosk," San Diego State University management information systems professor Aaron Elkins told SDSU's News Center. "However, this kiosk has a face on the screen that asks questions of travelers and can detect changes in physiology and behavior during the interview. The system can detect changes in the eyes, voice, gestures and posture to determine potential risk. It can even tell when you're curling your toes," he added.

Major natural or man-made disasters such as Hurricane Katrina or the 9/11 terror attacks pose significant challenges for emergency responders. First, they have to develop an understanding of the unfolding event either using their own resources or through third-parties such as the local population and agencies. Second, based on the information gathered, they need to deploy their teams in a flexible manner, ensuring that each team performs tasks in The most effective way. Third, given the dynamic nature of a disaster space, and the uncertainties involved in performing rescue missions, information about the disaster space and the actors within it needs to be managed to ensure that responders are always acting on up-to-date and trusted information. Against this background, this paper proposes a novel disaster response system called HAC-ER. Thus HAC-ER interweaves humans and agents, both robotic and software, in social relationships that augment their individual and collective capabilities. To design HAC-ER, we involved end-users including both experts and volunteers in a several participatory design workshops, lab studies, and field trials of increasingly advanced prototypes of individual components of HAC-ER as well as the overall system. This process generated a number of new quantitative and qualitative results but also raised a number of new research questions. HAC-ER thus demonstrates how such Human-Agent Collectives (HACs) can address key challenges in disaster response. Specifically, we show how HAC-ER utilises crowdsourcing combined with machine learning to obtain most important situational awareness from large streams of reports posted by members of the public and trusted organisations. We then show how this information can inform human-agent teams in coordinating multi-UAV deployments, as well as task planning for responders on the ground. Finally, HAC-ER incorporates an infrastructure and the associated intelligence for tracking and utilising the provenance of information shared across the entire system to ensure its accountability. We individually validate each of these elements of HAC-ER and show how they perform against standard (non-HAC) baselines and also elaborate on the evaluation of the overall system.

Many tasks in AI require the collaboration of multiple agents. Typically, the communication protocol between agents is manually specified and not altered during training. In this paper we explore a simple neural model, called CommNet, that uses continuous communication for fully cooperative tasks. The model consists of multiple agents and the communication between them is learned alongside their policy. We apply this model to a diverse set of tasks, demonstrating the ability of the agents to learn to communicate amongst themselves, yielding improved performance over non-communicative agents and baselines. In some cases, it is possible to interpret the language devised by the agents, revealing simple but effective strategies for solving the task at hand.

Planned experiments are the gold standard in reliably comparing the causal effect of switching from a baseline policy to a new policy. One critical shortcoming of classical experimental methods, however, is that they typically do not take into account the dynamic nature of response to policy changes. For instance, in an experiment where we seek to understand the effects of a new ad pricing policy on auction revenue, agents may adapt their bidding in response to the experimental pricing changes. Thus, causal effects of the new pricing policy after such adaptation period,the long-term causal effects, are not captured by the classical methodology eventhough they clearly are more indicative of the value of the new policy. Here, we formalize a framework to define and estimate long-term causal effects of policy changes in multiagent economies. Central to our approach is behavioral game theory, which we leverage to formulate the ignorability assumptions that are necessary for causal inference. Under such assumptions we estimate long-term causal effects through a latent space approach, where a behavioral model of how agents act conditional on their latent behaviors is combined with a temporal model of how behaviors evolve over time.