Elicitation and Aggregation of Crowd Information

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In this paper, we introduce a new class of potential fields, i.e., meta navigation functions (MNFs) to coordinate multi-agent systems. Thanks to the MNF formulation, agents can contribute to each other's coordination via partial and/or total associations, ...

Can artificial agents benefit from human conventions? Human societies manage to successfully self-organize and resolve the tragedy of the commons in common-pool resources, in spite of the bleak prediction of non-cooperative game theory. On top of that, rea ...

We consider the problem of making a multi-agent system (MAS) resilient to Byzantine failures through replication. We consider a very general model of MAS, where randomness can be involved in the behavior of each agent. We propose the first universal scheme ...

In this paper we study the problem of social learning under multiple true hypotheses and self-interested agents. In this setup, each agent receives data that might be generated from a different hypothesis (or state) than the data other agents receive. In c ...

We examine the problem of regret minimization when the learner is involved in a continuous game with other optimizing agents: in this case, if all players follow a no-regret algorithm, it is possible to achieve significantly lower regret relative to fully ...

Research problem: Housing is responsible for a considerable part of global resource consumption. In Switzerland, it accounts for almost one third of the country's CO2 emissions and half of the total energy demand. Thus, housing plays an important role in t ...

Intelligence involves processing sensory experiences into representations useful for prediction. Understanding sensory experiences and building these contextual representations without prior knowledge of sensor models and environment is a challenging unsup ...

Aerial robot swarms have the potential to perform time-critical and dangerous tasks such as disaster response without compromising human safety. However, their reliance on external infrastructure such as global positioning for localization and wireless net ...

While Reinforcement Learning (RL) aims to train an agent from a reward function in a given environment, Inverse Reinforcement Learning (IRL) seeks to recover the reward function from observing an expert’s behavior. It is well known that, in general, variou ...

Elicitation and Aggregation of Crowd Information

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Meta Navigation Functions: Adaptive Associations for Coordination of Multi-Agent Systems

Exploiting environmental signals to enable policy correlation in large-scale decentralized systems

Byzantine-Resilient Multi-Agent System

Improved Cooperation by Exploiting a Common Signal

Social Learning with Disparate Hypotheses

No-regret learning in games with noisy feedback: Faster rates and adaptivity via learning rate separation

Towards sufficiency in housing: Agent-based model and transition scenarios

Mechanical intelligence for learning embodied sensor-object relationships

Vision-based Flocking in Aerial Robot Swarms

Identifiability and Generalizability from Multiple Experts in Inverse Reinforcement Learning