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We study the problem of online learning in two-sided non-stationary matching markets, where the objective is to converge to a stable match. In particular, we consider the setting where one side of the market, the arms, has fixed known set of preferences over the other side, the players. While this problem has been studied when the players have fixed but unknown preferences, in this work we study the problem of how to learn when the preferences of the players are time varying and unknown. Our contribution is a methodology that can handle any type of preference structure and variation scenario. We show that, with the proposed algorithm, each player receives a uniform sub-linear regret of {O˜(𝐿1/2𝑇𝑇1/2)} up to the number of changes in the underlying preferences of the agents, 𝐿𝑇. Therefore, we show that the optimal rates for single-agent learning can be achieved in spite of the competition up to a difference of a constant factor. We also discuss extensions of this algorithm to the case where the number of changes need not be known a priori. 

Recent advances in Augmented Reality (AR) devices and their maturity as a technology offers new modalities for interaction between learners and their learning environments. Such capabilities are particularly important for learning that involves hands-on activities where there is a compelling need to: (a) make connections between knowledge-elements that have been taught at different times, (b) apply principles and theoretical knowledge in a concrete experimental setting, (c) understand the limitations of what can be studied via models and via experiments, (d) cope with increasing shortages in teaching-support staff and instructional material at the intersection of disciplines, and (e) improve student engagement in their learning. AR devices that are integrated into training and education systems can be effectively used to deliver just-in-time informatics to augment physical workspaces and learning environments with virtual artifacts. We present a system that demonstrates a solution to a critical registration problem and enables a multi-disciplinary team to develop the pedagogical content without the need for extensive coding. The most popular approach for developing AR applications is to develop a game using a standard game engine such as UNITY or UNREAL. These engines offer a powerful environment for developing a large variety of games and an exhaustive library of digital assets. In contrast, the framework we offer supports a limited range of human environment interactions that are suitable and effective for training and education. Our system offers four important capabilities – annotation, navigation, guidance, and operator safety. These capabilities are presented and described in detail. The above framework motivates a change of focus – from game development to AR content development. While game development is an intensive activity that involves extensive programming, AR content development is a multi-disciplinary activity that requires contributions from a large team of graphics designers, content creators, domain experts, pedagogy experts, and learning evaluators. We have demonstrated that such a multi-disciplinary team of experts working with our framework can use popular content creation tools to design and develop the virtual artifacts required for the AR system. These artifacts can be archived in a standard relational database and hosted on robust cloud-based backend systems for scale up. The AR content creators can own their content and Non-fungible Tokens to sequence the presentations either to improve pedagogical novelty or to personalize the learning. 

Recent advances in Augmented Reality (AR) devices and their maturity as a technology offers new modalities for interaction between learners and their learning environments. Such capabilities are particularly important for learning that involves hands-on activities where there is a compelling need to: (a) make connections between knowledge-elements that have been taught at different times, (b) apply principles and theoretical knowledge in a concrete experimental setting, (c) understand the limitations of what can be studied via models and via experiments, (d) cope with increasing shortages in teaching-support staff and instructional material at the intersection of disciplines, and (e) improve student engagement in their learning. AR devices that are integrated into training and education systems can be effectively used to deliver just-in-time informatics to augment physical workspaces and learning environments with virtual artifacts. We present a system that demonstrates a solution to a critical registration problem and enables a multi-disciplinary team to develop the pedagogical content without the need for extensive coding. The most popular approach for developing AR applications is to develop a game using a standard game engine such as UNITY or UNREAL. These engines offer a powerful environment for developing a large variety of games and an exhaustive library of digital assets. In contrast, the framework we offer supports a limited range of human environment interactions that are suitable and effective for training and education. Our system offers four important capabilities – annotation, navigation, guidance, and operator safety. These capabilities are presented and described in detail. The above framework motivates a change of focus – from game development to AR content development. While game development is an intensive activity that involves extensive programming, AR content development is a multi-disciplinary activity that requires contributions from a large team of graphics designers, content creators, domain experts, pedagogy experts, and learning evaluators. We have demonstrated that such a multi-disciplinary team of experts working with our framework can use popular content creation tools to design and develop the virtual artifacts required for the AR system. These artifacts can be archived in a standard relational database and hosted on robust cloud-based backend systems for scale up. The AR content creators can own their content and Non-fungible Tokens to sequence the presentations either to improve pedagogical novelty or to personalize the learning. 

We study the problem of online learning in competitive settings in the context of two-sided matching markets. In particular, one side of the market, the agents, must learn about their preferences over the other side, the firms, through repeated interaction while competing with other agents for successful matches. We propose a class of decentralized, communication- and coordination-free algorithms that agents can use to reach to their stable match in structured matching markets. In contrast to prior works, the proposed algorithms make decisions based solely on an agent’s own history of play and requires no foreknowledge of the firms’ preferences.Our algorithms are constructed by splitting up the statistical problem of learning one’s preferences, from noisy observations, from the problem of competing for firms. We show that under realistic structural assumptions on the underlying preferences of the agents and firms, the proposed algorithms incur a regret which grows at most logarithmically in the time horizon. However, we note that in the worst case, it may grow exponentially in the size of the market. 

How can a social planner adaptively incentivize selfish agents who are learning in a strategic environment to induce a socially optimal outcome in the long run? We propose a two-timescale learning dynamics to answer this question in games. In our learning dynamics, players adopt a class of learning rules to update their strategies at a faster timescale, while a social planner updates the incentive mechanism at a slower timescale. In particular, the update of the incentive mechanism is based on each player’s externality, which is evaluated as the difference between the player’s marginal cost and the society’s marginal cost in each time step. We show that any fixed point of our learning dynamics corresponds to the optimal incentive mechanism such that the corresponding Nash equilibrium also achieves social optimality. We also provide sufficient conditions for the learning dynamics to converge to a fixed point so that the adaptive incentive mechanism eventually induces a socially optimal outcome. Finally, as an example, we demonstrate that the sufficient conditions for convergence are satisfied in Cournot competition with finite players. 

Decentralized planning for multi-agent systems, such as fleets of robots in a search-and-rescue operation, is often constrained by limitations on how agents can communicate with each other. One such limitation is the case when agents can communicate with each other only when they are in line-of-sight (LOS). Developing decentralized planning methods that guarantee safety is difficult in this case, as agents that are occluded from each other might not be able to communicate until it’s too late to avoid a safety violation. In this paper, we develop a decentralized planning method that explicitly avoids situations where lack of visibility of other agents would lead to an unsafe situation. Building on top of an existing Rapidly exploring Random Tree (RRT)-based approach, our method guarantees safety at each iteration. Simulation studies show the effectiveness of our method and compare the degradation in performance with respect to a clairvoyant decentralized planning algorithm where agents can communicate despite not being in LOS of each other.