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Traffic congestion is a major challenge in modern urban settings. The industry-wide development of autonomous and automated vehicles (AVs) motivates the question of how can AVs contribute to congestion reduction. Past research has shown that in small ...
While reinforcement learning (RL) has helped artificial agents solve challenging tasks, high sample complexity is still a major concern. Inter-agent teaching -- endowing agents with the ability to respond to instructions from others -- has been ...
A series of reports promises the general public a technologically accurate view of the state of AI and its societal implications.
Motivated by the desire to give vehicles better information about their drivers, we explore human intent inference in the setting of a human driver riding in a moving vehicle. Specifically, we consider scenarios in which the driver intends to go to or ...
Each individual bird in a flock of birds updates its behavior based on the behaviors of its neighbors. Previous work has considered how a small set of algorithmically controlled influencing agents, or robot birds, can influence the flock to behave in a ...
In the multi-robot human guidance problem, a centralized controller makes use of multiple robots to provide navigational assistance to a human in order to reach a goal location. Previous work used Markov Decision Processes (MDPs) to construct a ...
Agents can achieve effective interaction with previously unknown other agents by maintaining beliefs over a set of hypothetical behaviours, or types, that these agents may have. A current limitation in this method is that it does not recognise ...
The Standard Platform League is one of the main competitions at the annual RoboCup world championships. In this competition, teams of five humanoid robots play soccer against each other. In 2013, the league began a new competition which serves as a ...
Multirobot symbolic planning (MSP) aims at computing plans, each in the form of a sequence of actions, for a team of robots to achieve their individual goals while minimizing overall cost. Solving MSP problems requires modeling limited domain resources (...
Due to Cremer and McLean (1985), it is well known that in a setting where bidders' values are correlated, an auction designer can extract the full social surplus as revenue. However, this result strongly relies on the assumption of a common prior ...
With the efforts of moving to sustainable and reliable energy supply, electricity markets are undergoing far-reaching changes. Due to the high-cost of failure in the real-world, it is important to test new market structures in simulation. This is the ...
Many different animals, including birds and fish, exhibit a collective behavior known as flocking. Flocking behavior is believed by biologists to emerge from relatively simple local control rules utilized by each individual in a flock. Specifically, ...
Transfer learning in reinforcement learning has been an active area of research over the past decade. In transfer learning, training on a source task is leveraged to speed up or otherwise improve learning on a target task. This paper presents the more ...
In order to achieve long-term autonomy in the real world, fully autonomous agents need to be able to learn, both to improve their behaviors in a complex, dynamically changing world, and to enable interaction with previously unfamiliar agents. This talk ...
Reinforcement learning (RL) is a well-established paradigm for enabling autonomous agents to learn from experience. To enable RL to scale to any but the smallest domains, it is necessary to make use of abstraction and generalization of the state-action ...
The Standard Platform League is a soccer league at the annual RoboCup world championships in which teams of five humanoid robots play against each other. In 2014, the Drop-in Player Competition was added to the league to serve as a testbed for ...
Recently, there has been an increase in interest in applying game theoretic approaches to domains involving frequent adversary interactions, such as wildlife and fishery protection. In these domains, the law enforcement agency faces adversaries who ...
Reinforcement learning (RL) is a well-established paradigm for enabling autonomous agents to learn from experience. To enable RL to scale to any but the smallest domains, it is necessary to make use of abstraction and generalization of the state-action ...
In a reinforcement learning setting, the goal of transfer learning is to improve performance on a target task by re-using knowledge from one or more source tasks. A key problem in transfer learning is how to choose appropriate source tasks for a given ...
In recent years, there has been growing focus on the study of automated recommender systems. Music recommendation systems serve as a prominent domain for such works, both from an academic and a commercial perspective. A fundamental aspect of music ...