Learned human-agent decision-making, communication and joint action in a virtual reality environment
Humans make decisions and act alongside other humans to pursue both short-term and long-term goals. As a result of ongoing progress in areas such as computing science and automation, humans now also interact with non-human agents of varying complexity as part of their day-to-day activities; substantial work is being done to integrate increasingly intelligent machine agents into human work and play. With increases in the cognitive, sensory, and motor capacity of these agents, intelligent machinery for human assistance can now reasonably be considered to engage in joint action with humans---i.e., two or more agents adapting their behaviour and their understanding of each other so as to progress in shared objectives or goals. The mechanisms, conditions, and opportunities for skillful joint action in human-machine partnerships is of great interest to multiple communities. Despite this, human-machine joint action is as yet under-explored, especially in cases where a human and an intelligent machine interact in a persistent way during the course of real-time, daily-life experience. In this work, we contribute a virtual reality environment wherein a human and an agent can adapt their predictions, their actions, and their communication so as to pursue a simple foraging task. In a case study with a single participant, we provide an example of human-agent coordination and decision-making involving prediction learning on the part of the human and the machine agent, and control learning on the part of the machine agent wherein audio communication signals are used to cue its human partner in service of acquiring shared reward. These comparisons suggest the utility of studying human-machine coordination in a virtual reality environment, and identify further research that will expand our understanding of persistent human-machine joint action.
Code (0)
등록된 구현이 없습니다.
Tasks
Decision MakingSimilar Papers 제목 키워드 기반
The Frost Hollow Experiments: Pavlovian Signalling as a Path to Coordination and Communication Between Agents
Learned communication between agents is a powerful tool when approaching decision-making problems that are hard to overcome by any single agent in isolation. However, continual coordination and communication learning bet…
Decision Makingreinforcement-learningReinforcement Learning (RL)Sequential Communication in Multi-Agent Reinforcement Learning
Coordination is one of the essential problems in multi-agent reinforcement learning. Communication provides an alternative for agents to obtain information about others so that better coordinated behavior can be learned.…
Decision MakingMulti-agent Reinforcement Learningreinforcement-learningReinforcement Learning+1Pavlovian Signalling with General Value Functions in Agent-Agent Temporal Decision Making
In this paper, we contribute a multi-faceted study into Pavlovian signalling -- a process by which learned, temporally extended predictions made by one agent inform decision-making by another agent. Signalling is intimat…
Decision Makingreinforcement-learningReinforcement Learning (RL)Multi-Agent Coordination via Multi-Level Communication
The partial observability and stochasticity in multi-agent settings can be mitigated by accessing more information about others via communication. However, the coordination problem still exists since agents cannot commun…
Decision MakingThree Different Ways Synchronization Can Cause Contagion in Financial Markets
We introduce tools to capture the dynamics of three different pathways, in which the synchronization of human decision-making could lead to turbulent periods and contagion phenomena in financial markets. The first pathwa…
Decision Making