Social commitment protocols regulate interactions of agents in multiagent systems. Several methods have been developed to analyze properties of commitment protocols. However, analysis of an agent's behavior in a commitment protocol, which should take into account the agent's goals and beliefs, has received less attention. In this paper we present ProMoca framework to address this issue. Firstly, we develop an expressive formal language to model agents with respect to their commitments. Our language provides dedicated elements to define commitment protocols, and model agents in terms of their goals, behaviors, and beliefs. Furthermore, our language provides probabilistic and non-deterministic elements to model uncertainty in agents' beliefs. Secondly, we identify two essential properties of an agent with respect to a commitment protocol, namely compliance and goal satisfaction. We formalize these properties using a probabilistic variant of linear temporal logic. Thirdly, we adapt a probabilistic model checking algorithm to automatically analyze compliance and goal satisfaction properties. Finally, we present empirical results about efficiency and scalability of ProMoca.

The aim of this paper is to investigate the interplay between knowledge shared by a group of agents and its coalition ability. We investigate this relation in the standard context of imperfect information concurrent game. We assume that whenever a set of agents form a coalition to achieve a goal, they share their knowledge before acting. Based on this assumption, we propose a new semantics for alternating-time temporal logic with imperfect information and perfect recall. It turns out that this semantics is sufficient to preserve all the desirable properties of coalition ability in traditional coalitional logics. Meanwhile, we investigate how knowledge sharing within a group of agents contributes to its coalitional ability through the interplay of epistemic and coalition modalities. This work provides a partial answer to the question: which kind of group knowledge is required for a group to achieve their goals in the context of imperfect information.

Artificial Intelligence (AI) can help us in many ways: it can perform hard, dangerous or boring work for us, can help us to save lives and cope with disasters, can entertain us and make our daily life more comfortable. Advances in AI are occurring at high speed. The potential risks and problems of AI technology are filling newspapers (e.g. However, rather than being a threat to our existence or plotting to take over the rule of the world, AI is already changing our daily lives, almost entirely in ways that improve human health, safety, and productivity. In the coming years we can expect AI systems to be used increasingly in domains such as transportation, service robots, healthcare, education, low-resource communities, public safety and security, employment and workplace, and entertainment (100 Year AI report).

Autonomous AI agents have begun to take over entire tasks start to finish. And this is just the beginning. We expect to see a plethora of such agents in the next half decade, and these will take on all sorts of tasks that humans currently perform, if unhappily. In many ways the machines will be better at these tasks than we are. Self-driving cars have superhuman sensors, reaction time and true multitasking, and they will always apply their full attention to the task at hand, driving!

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.