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1. Multi-agent goal delegation

   Gogineni, Venkatsampath R.; Kondrakunta, Sravya; Cox, Michael T. (November 2021, Proceedings of the 9th Goal Reasoning Workshop)

   Autonomous agents in a multi-agent system work with each other to achieve their goals. However, In a partially observable world, current multi-agent systems are often less effective in achieving their goals. This limitation is due to the agents’ lack of reasoning about other agents and their mental states. Another factor is the agents’ inability to share required knowledge with other agents. This paper addresses the limitations by presenting a general approach for autonomous agents to work together in a multi-agent system. In this approach, an agent applies two main concepts: goal reasoning- to determine what goals to pursue and share; Theory of mind-to select an agent(s) for sharing goals and knowledge. We evaluate the performance of our multi-agent system in a Marine Life Survey Domain and compare it to another multi-agent system that randomly selects agent(s) to delegates its goals. 

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2. What is the Solution for State-Adversarial Multi-Agent Reinforcement Learning?

   Han, Songyang; Su, Sanbao; He, Sihong; Han, Shuo; Yang, Haizhao; Zou, Shaofeng; Miao, Fei (February 2024, Transactions on Machine Learning Research)

   Various methods for Multi-Agent Reinforcement Learning (MARL) have been developed with the assumption that agents’ policies are based on accurate state information. However, policies learned through Deep Reinforcement Learning (DRL) are susceptible to adversarial state perturbation attacks. In this work, we propose a State-Adversarial Markov Game (SAMG) and make the first attempt to investigate different solution concepts of MARL under state uncertainties. Our analysis shows that the commonly used solution concepts of optimal agent policy and robust Nash equilibrium do not always exist in SAMGs. To circumvent this difficulty, we consider a new solution concept called robust agent policy, where agents aim to maximize the worst-case expected state value. We prove the existence of robust agent policy for finite state and finite action SAMGs. Additionally, we propose a Robust Multi-Agent Adversarial Actor-Critic (RMA3C) algorithm to learn robust policies for MARL agents under state uncertainties. Our experiments demonstrate that our algorithm outperforms existing methods when faced with state perturbations and greatly improves the robustness of MARL policies. Our code is public on https://songyanghan.github.io/what_is_solution/. 

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3. What is the Solution for State-Adversarial Multi-Agent Reinforcement Learning?

   Han, Songyang; Su, Sanbao; He, Sihong; Han, Shuo; Yang, Haizhao; Zou, Shaofeng; Miao, Fei (February 2024, Transactions on machine learning research)

   Various methods for Multi-Agent Reinforcement Learning (MARL) have been developed with the assumption that agents' policies are based on accurate state information. However, policies learned through Deep Reinforcement Learning (DRL) are susceptible to adversarial state perturbation attacks. In this work, we propose a State-Adversarial Markov Game (SAMG) and make the first attempt to investigate different solution concepts of MARL under state uncertainties. Our analysis shows that the commonly used solution concepts of optimal agent policy and robust Nash equilibrium do not always exist in SAMGs. To circumvent this difficulty, we consider a new solution concept called robust agent policy, where agents aim to maximize the worst-case expected state value. We prove the existence of robust agent policy for finite state and finite action SAMGs. Additionally, we propose a Robust Multi-Agent Adversarial Actor-Critic (RMA3C) algorithm to learn robust policies for MARL agents under state uncertainties. Our experiments demonstrate that our algorithm outperforms existing methods when faced with state perturbations and greatly improves the robustness of MARL policies. Our code is public on https://songyanghan.github.io/what_is_solution/. 

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4. Improving Grounded Natural Language Understanding through Human-Robot Dialog

   Thomason, J.; Padmakumar, A.; Sinapov, J.; Walker, N.; Jiang, Y.; Yedidsion, H.; Hart, J.; Stone, P.; Mooney, R. (May 2019, Proceedings of the International Conference on Robotics and Automation (ICRA))

   Natural language understanding for robotics can require substantial domain- and platform-specific engineering. For example, for mobile robots to pick-and-place objects in an environment to satisfy human commands, we can specify the language humans use to issue such commands, and connect concept words like red can to physical object properties. One way to alleviate this engineering for a new domain is to enable robots in human environments to adapt dynamically -- continually learning new language constructions and perceptual concepts. In this work, we present an end-to-end pipeline for translating natural language commands to discrete robot actions, and use clarification dialogs to jointly improve language parsing and concept grounding. We train and evaluate this agent in a virtual setting on Amazon Mechanical Turk, and we transfer the learned agent to a physical robot platform to demonstrate it in the real world. 

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5. Design Interactions between Robot Surfaces and Human Designers

   https://doi.org/10.1145/3294109.3302962  

   Wang, Yixiao (March 2019, Thirteenth International Conference on Tangible, Embedded, and Embodied Interaction (TEI ’19).)

   This paper presents the rationale and current progress of my Ph.D. dissertation: "design interactions between robot surfaces and human designers." This specific topic serves as a case study trying to explore the question of how to design an interactive and partially intelligent space. We proposed the concept of "space agent" defined as "interactive and intelligent environments perceived by users as human agents" based on communication theories. Built upon this concept, we proposed a design framework for interactive environments. Then we further explored literatures about what space agent could contribute to human users specifically for the case of interior designers' work space. Research questions and research designs are introduced in this paper, followed by the discussions of experiments design. 

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