13 accepted papers
The objective of this study is to advance the optimization of hybrid electricity markets using multi-agent reinforcement learning (MARL). The transition from centralized systems to public–private models introduces significant challenges, including the emergence of independent market players and the
Generating behaviors that align with human expectations is a key requirement for human-robot collaboration. Potential behavior misalignment could lead to the robot performing actions with unanticipated, potentially dangerous side effects even while pursuing human goals. In this paper, we introduce a
Robotic agents operating in dynamic, partially observable environments often benefit from teammate assistance. We address the challenge of determining when and how to assist in multi-robot systems where agents can modify the physical environment, such as moving obstacles that block perception or man…
For multi-robot teams with limited communication, the ability to rapidly recognize the intention of a teammate via its exhibited behavior is key to achieving effective collaboration. While current research on plan and goal recognition provide powerful tools, most of them rely on a high-level abstrac
Recent studies show that deep reinforcement learning (DRL) agents tend to overfit to the task on which they were trained and fail to adapt to minor environment changes. To expedite learning when transferring to unseen tasks, we propose a novel approach to representing the current task using reward m…
Most past research aimed at increasing the capabilities of AI methods has focused exclusively on the AI agent itself, i.e., given some input, what are the improvements to the agent’s reasoning that will yield the best possible output. In my research, I take a novel approach to increasing the capabil…
In many real-world settings, an autonomous agent may not have sufficient information or sensory capabilities to accomplish its goals, even when they are achievable. In some cases, the needed information can be provided by another agent, but information sharing might be costly due to limited communic…
We present a novel multi-agent RL approach, Selective Multi-Agent Prioritized Experience Relay, in which agents share with other agents a limited number of transitions they observe during training. The intuition behind this is that even a small number of relevant experiences from other agents could…
Mobile robotic agents often suffer from localization uncertainty which grows with time and with the agents' movement. This can hinder their ability to accomplish their task. In some settings, it may be possible to perform assistive actions that reduce uncertainty about a robot's location. For exampl…
Understanding emerging behaviors of reinforcement learning (RL) agents may be difficult since such agents are often trained in complex environments using highly complex decision making procedures. This has given rise to a variety of approaches to explainability in RL that aim to reconcile discrepanc…
In Goal Recognition Design (GRD), the objective is to modify a domain to facilitate early detection of the goal of a subject agent. Most previous work studies this problem in the offline setting, in which the observing agent performs its interventions before the subject begins acting. In this pape…
Designing robots capable of generating interpretable behavior is essential for effective human-robot collaboration. This requires robots to be able to generate behavior that aligns with human expectations but exhibiting such behavior in arbitrary environments could be quite expensive for robots, and…
Goal recognition is the task of recognizing the objective of agents based on online observations of their behavior. Goal recognition design (GRD), the focus of this survey, facilitates goal recognition by the analysis and redesign of goal recognition models. In a nutshell, given a model of a domain…