It is commonly claimed that AI will replace most manual labor in the future; however, is this really the case? AI technologies do have higher image recognition accuracy compared to humans in some limited contexts, and have consistently outperformed humans in classical games such as Go and chess. Nonetheless, we believe that even advanced future developments based on current technology will not lead to robots replacing humans. AI systems' fundamental lack of ability to communicate naturally and effectively with humans is among the most significant reasons that they cannot replace human labor. Here, one may believe that such communication could be achieved via the development of natural language processing (NLP) technology [4]; however, NLP technologies are systems for estimating the content of human statements and their meanings; they do not constitute communication. That is, humans do not feel that robots using such systems truly understand and respond to them appropriately. Therefore, if effective communication is not achieved, robots will continue to function only as tools to assist humans. Advancements improving the accuracy or effectiveness of various specific tasks do not indicate that robots are equivalent to human beings. Under this scenario, how can we enable robots to communicate with humans?

Game theory is known to be a useful tool in the study of Machine Learning (ML) and Artificial Intelligence (AI) Multi-Agent interactions. One basic component of these ML and AI systems is the exploration-exploitation trade-off, a fundamental dilemma between taking a risk with new actions in the quest for more information about the environment (exploration) and repeatedly selecting actions that result in the current maximum reward or (exploitation). However, the outcome of the exploration-exploitation process is often unpredictable in practice and the reasons behind its volatile performance have been a long-standing open question in the ML and AI communities. Dr Stefanos Leonardos and Assistant Professor Georgios Piliouras, researchers from the Singapore University of Technology and Design (SUTD), applied analytical tools from the theory of dynamical systems in the study of multi-agent systems and established a deep connection between exploration-exploitation and Catastrophe Theory (Figures 1 and 2). The latter is a branch of mathematics that formally explains phase transitions in all kinds of natural systems ranging from the transition from water to ice and disease outbreaks to collapses of financial markets.

In the present paper we present the potential of Explainable Artificial Intelligence methods for decision-support in medical image analysis scenarios. With three types of explainable methods applied to the same medical image data set our aim was to improve the comprehensibility of the decisions provided by the Convolutional Neural Network (CNN). The visual explanations were provided on in-vivo gastral images obtained from a Video capsule endoscopy (VCE), with the goal of increasing the health professionals' trust in the black box predictions. We implemented two post-hoc interpretable machine learning methods LIME and SHAP and the alternative explanation approach CIU, centered on the Contextual Value and Utility (CIU). The produced explanations were evaluated using human evaluation. We conducted three user studies based on the explanations provided by LIME, SHAP and CIU. Users from different non-medical backgrounds carried out a series of tests in the web-based survey setting and stated their experience and understanding of the given explanations. Three user groups (n=20, 20, 20) with three distinct forms of explanations were quantitatively analyzed. We have found that, as hypothesized, the CIU explainable method performed better than both LIME and SHAP methods in terms of increasing support for human decision-making as well as being more transparent and thus understandable to users. Additionally, CIU outperformed LIME and SHAP by generating explanations more rapidly. Our findings suggest that there are notable differences in human decision-making between various explanation support settings. In line with that, we present three potential explainable methods that can with future improvements in implementation be generalized on different medical data sets and can provide great decision-support for medical experts.

Artificial intelligence (AI) has witnessed a substantial breakthrough in a variety of Internet of Things (IoT) applications and services, spanning from recommendation systems to robotics control and military surveillance. This is driven by the easier access to sensory data and the enormous scale of pervasive/ubiquitous devices that generate zettabytes (ZB) of real-time data streams. Designing accurate models using such data streams, to predict future insights and revolutionize the decision-taking process, inaugurates pervasive systems as a worthy paradigm for a better quality-of-life. The confluence of pervasive computing and artificial intelligence, Pervasive AI, expanded the role of ubiquitous IoT systems from mainly data collection to executing distributed computations with a promising alternative to centralized learning, presenting various challenges. In this context, a wise cooperation and resource scheduling should be envisaged among IoT devices (e.g., smartphones, smart vehicles) and infrastructure (e.g. edge nodes, and base stations) to avoid communication and computation overheads and ensure maximum performance. In this paper, we conduct a comprehensive survey of the recent techniques developed to overcome these resource challenges in pervasive AI systems. Specifically, we first present an overview of the pervasive computing, its architecture, and its intersection with artificial intelligence. We then review the background, applications and performance metrics of AI, particularly Deep Learning (DL) and online learning, running in a ubiquitous system. Next, we provide a deep literature review of communication-efficient techniques, from both algorithmic and system perspectives, of distributed inference, training and online learning tasks across the combination of IoT devices, edge devices and cloud servers. Finally, we discuss our future vision and research challenges.

Understanding human driving behaviors quantitatively is critical even in the era when connected and autonomous vehicles and smart infrastructure are becoming ever more prevalent. This is particularly so as that mixed traffic settings, where autonomous vehicles and human driven vehicles co-exist, are expected to persist for quite some time. Towards this end it is necessary that we have a comprehensive modeling framework for decision-making within which human driving preferences can be inferred statistically from observed driving behaviors in realistic and naturalistic traffic settings. Leveraging a recently proposed computational framework for smart vehicles in a smart world using multi-agent based simulation and optimization, we first recapitulate how the forward problem of driving decision-making is modeled as a state space model. We then show how the model can be inverted to estimate driver preferences from naturalistic traffic data using the standard Kalman filter technique. We explicitly illustrate our approach using the vehicle trajectory data from Sugiyama experiment that was originally meant to demonstrate how stop-and-go shockwave can arise spontaneously without bottlenecks. Not only the estimated state filter can fit the observed data well for each individual vehicle, the inferred utility functions can also re-produce quantitatively similar pattern of the observed collective behaviors. One distinct advantage of our approach is the drastically reduced computational burden. This is possible because our forward model treats driving decision process, which is intrinsically dynamic with multi-agent interactions, as a sequence of independent static optimization problems contingent on the state with a finite look ahead anticipation. Consequently we can practically sidestep solving an interacting dynamic inversion problem that would have been much more computationally demanding.

Building human-like agent, which aims to learn and think like human intelligence, has long been an important research topic in AI. To train and test human-like agents, we need an environment that imposes the agent to rich multimodal perception and allows comprehensive interactions for the agent, while also easily extensible to develop custom tasks. However, existing approaches do not support comprehensive interaction with the environment or lack variety in modalities. Also, most of the approaches are difficult or even impossible to implement custom tasks. In this paper, we propose a novel VR-based toolkit, VECA, which enables building fruitful virtual environments to train and test human-like agents. In particular, VECA provides a humanoid agent and an environment manager, enabling the agent to receive rich human-like perception and perform comprehensive interactions. To motivate VECA, we also provide 24 interactive tasks, which represent (but are not limited to) four essential aspects in early human development: joint-level locomotion and control, understanding contexts of objects, multimodal learning, and multi-agent learning. To show the usefulness of VECA on training and testing human-like learning agents, we conduct experiments on VECA and show that users can build challenging tasks for engaging human-like algorithms, and the features supported by VECA are critical on training human-like agents.

We study the problem of minimizing the resource capacity of autonomous agents cooperating to achieve a shared task. More specifically, we consider high-level planning for a team of homogeneous agents that operate under resource constraints in stochastic environments and share a common goal: given a set of target locations, ensure that each location will be visited infinitely often by some agent almost surely. We formalize the dynamics of agents by consumption Markov decision processes. In a consumption Markov decision process, the agent has a resource of limited capacity. Each action of the agent may consume some amount of the resource. To avoid exhaustion, the agent can replenish its resource to full capacity in designated reload states. The resource capacity restricts the capabilities of the agent. The objective is to assign target locations to agents, and each agent is only responsible for visiting the assigned subset of target locations repeatedly. Moreover, the assignment must ensure that the agents can carry out their tasks with minimal resource capacity. We reduce the problem of finding target assignments for a team of agents with the lowest possible capacity to an equivalent graph-theoretical problem. We develop an algorithm that solves this graph problem in time that is \emph{polynomial} in the number of agents, target locations, and size of the consumption Markov decision process. We demonstrate the applicability and scalability of the algorithm in a scenario where hundreds of unmanned underwater vehicles monitor hundreds of locations in environments with stochastic ocean currents.

This paper envisions a multi-agent system for detecting the presence of hate speech in online social media platforms such as Twitter and Facebook. We introduce a novel framework employing deep learning techniques to coordinate the channels of textual and im-age processing. Our experimental results aim to demonstrate the effectiveness of our methods for classifying online content, training the proposed neural network model to effectively detect hateful instances in the input. We conclude with a discussion of how our system may be of use to provide recommendations to users who are managing online social networks, showcasing the immense potential of intelligent multi-agent systems towards delivering social good.

AI agent and the human coexist, and have partial observability of each other's activities. There are several real-world This paper addresses the problem of synthesizing workspaces like factory floors, warehouses, restaurants, nursing the behavior of an AI agent that provides proactive homes for elderly, disaster response areas, etc., where this task assistance to a human in settings like factory problem of providing proactive task assistance to the involved floors where they may coexist in a common humans is important. Our formulation considers a scenario environment. Unlike in the case of requested assistance, where the AI agent is aware of the tasks being allocated to the human may not be expecting proactive the human by the ecosystem and may also know the rules and assistance and hence it is crucial for the agent to protocols of the ecosystem. We assume that the agent has ensure that the human is aware of how the assistance access to an input that captures the human's planning process affects her task. This becomes harder when for her goals. For instance, prior works that study the there is a possibility that the human may neither problem of action model acquisition [Zhuo and Yang, 2014; have full knowledge of the AI agent's capabilities Zhuo and Kambhampati, 2013] can be used to derive the human's nor have full observability of its activities.

Many collective decision-making settings feature a strategic tension between agents acting out of individual self-interest and promoting a common good. These include wearing face masks during a pandemic, voting, and vaccination. Networked public goods games capture this tension, with networks encoding strategic interdependence among agents. Conventional models of public goods games posit solely individual self-interest as a motivation, even though altruistic motivations have long been known to play a significant role in agents' decisions. We introduce a novel extension of public goods games to account for altruistic motivations by adding a term in the utility function that incorporates the perceived benefits an agent obtains from the welfare of others, mediated by an altruism graph. Most importantly, we view altruism not as immutable, but rather as a lever for promoting the common good. Our central algorithmic question then revolves around the computational complexity of modifying the altruism network to achieve desired public goods game investment profiles. We first show that the problem can be solved using linear programming when a principal can fractionally modify the altruism network. While the problem becomes in general intractable if the principal's actions are all-or-nothing, we exhibit several tractable special cases.