The immense technological advancements in the past decade have enabled robots to enjoy high levels of autonomy, paving their way into our society. The recent catastrophic accidents involving autonomous systems (e.g., Tesla fatal car accident), however, show that sole engineering progress in the technology is not enough to guarantee a safe and productive partnership between a human and a robot. In this paper we argue that we also need to advance our understanding of the role of social trust within human-robot relationships, and formulate a theory for expressing and reasoning about trust in the context of decisions affecting collaboration or competition between humans and robots. Therefore, we call for cross-disciplinary collaborations to study the formalization of social trust in the context of human-robot relationship. We lay the groundwork for such a study in this paper.

This paper aims to shed light on the cross-disciplinary challenges involved in the development of autonomous systems from a practice standpoint. To that end, the paper examines what aspects of human-centered autonomous systems capabilities may be difficult to achieve using a machine-centered development process, the common practice. The paper concludes with suggestions for what more may be done to enable human-centered design considerations to be more effectively infused in the development process.

We propose a formal design framework to automatically synthesize coordination and control schemes for cooperative multi-agent systems by combining a top-down mission planning with a bottom-up motion planning. The multi-agent system is assigned a global mission, specified as regular languages over all the agents’ capabilities, whereas basic motion controllers for each agent shall be designed with respect to given environment description. On one hand, a mission planning layer sits on the top of the proposed framework, decomposing the global mission into local tasks that are in consistency with each agent’s individual capabilities, and compositionally verifying the joint effort of the agents via an assume guarantee paradigm. On the other hand, corresponding to these local missions, motion plans associated with each agent are synthesized by composing basic motion primitives, which are verified safe by differential dynamic logic (dL), through a Satisfiability Modulo Theories (SMT) solver that searches feasible solutions in face of constraints due to local task requirements and the environment description. It is shown that the proposed framework can handle changing environments as the motion primitives are reactive in nature, making the motion planning adaptive to local environmental changes. Furthermore, on-line mission reconfiguration can be triggered by the motion planning layer once no feasible solutions can be found through the SMT solver. The effectiveness of the overall design framework is demonstrated by an automated warehouse case study.

When the artist Thomas Jackson began working on "Emergent Behavior," in 2011, he started with found objects. He collected fallen leaves in the Catskills and picked junk off the street in New York, then moved on to purchasing hundreds of cups and cheese balls, construction fences, glow necklaces, hula hoops, and balloons. He assembles these objects on outdoor frameworks, then photographs the installations. The resulting pictures show inanimate objects caught up in restless movement: some circle, some gather, some dip. In the color palette of a birthday party, Jackson's bits of plastic and rubber evoke schools of fish that move like ink in the water, or birds streaking the sky.

An exciting opportunity is available for a PhD candidate to undertake a research project in modelling autonomous behaviours, testing and verification of agent designs, explaining autonomous behaviour, or designing reusable simulation models. This scholarship is valued at up to $5000 per annum for up to 3 years. Students with an approved Australian Postgraduate Award (APA) or other postgraduate stipend are eligible for the top-up scholarship. Applicants should contact Associate Professor John Thangarajah to discuss their eligibility and the topic/area of prospective research (see further information below). These scholarships are most suited for those who plan to apply for an APA.

This paper considers the problem of adaptively searching for an unknown target using multiple agents connected through a time-varying network topology. Agents are equipped with sensors capable of fast information processing, and we propose a decentralized collaborative algorithm for controlling their search given noisy observations. Specifically, we propose decentralized extensions of the adaptive query-based search strategy that combines elements from the 20 questions approach and social learning. Under standard assumptions on the time-varying network dynamics, we prove convergence to correct consensus on the value of the parameter as the number of iterations go to infinity. The convergence analysis takes a novel approach using martingale-based techniques combined with spectral graph theory. Our results establish that stability and consistency can be maintained even with one-way updating and randomized pairwise averaging, thus providing a scalable low complexity method with performance guarantees. We illustrate the effectiveness of our algorithm for random network topologies.

Video games where people matter? If you're a video game fan of a certain age, you may remember Edge magazine's controversial review of the bloody sci-fi shooting game, Doom. Perhaps you enjoyed a good laugh, as many first-person shooter fans have, at the writer's much-mocked assertion: "if only you could talk to these creatures, then perhaps you could try and make friends with them, form alliances … Now that would be interesting." Of course, we all know what happened. There would be no room in the Doom series, nor any subsequent first-person blast-'em-up, for such socio-psychological niceties. Instead, we enjoyed 20 years of shooting, bludgeoning and stabbing, the ludicrous idea of diplomacy cast roughly aside. But during this era, something else was happening in game design, and in academic thinking around video games and artificial intelligence.

A major challenge in crowdsourcing evaluation tasks like labeling objects, grading assignments in online courses, etc., is that of eliciting truthful responses from agents in the absence of verifiability. In this paper, we propose new reward mechanisms for such settings that, unlike many previously studied mechanisms, impose minimal assumptions on the structure and knowledge of the underlying generating model, can account for heterogeneity in the agents' abilities, require no extraneous elicitation from them, and furthermore allow their beliefs to be (almost) arbitrary. These mechanisms have the simple and intuitive structure of an output agreement mechanism: an agent gets a reward if her evaluation matches that of her peer, but unlike the classic output agreement mechanism, this reward is not the same across evaluations, but is inversely proportional to an appropriately defined popularity index of each evaluation. The popularity indices are computed by leveraging the existence of a large number of similar tasks, which is a typical characteristic of these settings. Experiments performed on MTurk workers demonstrate higher efficacy (with a $p$-value of $0.02$) of these mechanisms in inducing truthful behavior compared to the state of the art.

So if you want to learn more about machine learning, how do you start? For me, my first introduction is when I took an Artificial Intelligence class when I was studying abroad in Copenhagen. My lecturer is a full-time Applied Math and CS professor at the Technical University of Denmark, in which his research areas are logic and artificial, focusing primarily on the use of logic to model human-like planning, reasoning and problem solving. The class was a mix of discussion of theory/core concepts and hands-on problem solving. The textbook that we used is one of the AI classics: Peter Norvig's Artificial Intelligence -- A Modern Approach, in which we covered major topics including intelligent agents, problem-solving by searching, adversarial search, probability theory, multi-agent systems, social AI, philosophy/ethics/future of AI.

IBM Watson Virtual Agent is a set of preconfigured cognitive components based on the IBM Watson Conversation service. By configuring the virtual agent with your company's information, you can quickly implement an automated chat bot that enables your customers to achieve their goals. The established model of creating a digital or virtual agent requires experienced developers with a highly specific skill set to create complex systems that rely on custom – and often cumbersome – rules. Watson Virtual Agent allows businesses to simply build and deploy conversational agents. Watson Virtual Agent helps accelerate users' ability to deploy bots, including pre-trained cross-industry content, with minimal configuration, simplifying the process for both seasoned developers or users without formal technical training.