System could help prevent robots from overwhelming human teammates with information
Autonomous robots performing a joint task send each other continual updates: “I’ve passed through a door and am turning 90 degrees right.” “After advancing 2 feet I’ve encountered a wall. I’m turning 90 degrees right.” “After advancing 4 feet I’ve encountered a wall.” And so on.
Computers, of course, have no trouble filing this information away until they need it. But such a barrage of data would drive a human being crazy.
At the annual meeting of the Association for the Advancement of Artificial Intelligence last weekend, researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) presented a new way of modeling robot collaboration that reduces the need for communication by 60 percent. They believe that their model could make it easier to design systems that enable humans and robots to work together — in, for example, emergency-response teams.
“We haven’t implemented it yet in human-robot teams,” says Julie Shah, an associate professor of aeronautics and astronautics and one of the paper’s two authors. “But it’s very exciting, because you can imagine: You’ve just reduced the number of communications by 60 percent, and presumably those other communications weren’t really necessary toward the person achieving their part of the task in that team.”
The work could have also have implications for multirobot collaborations that don’t involve humans. Communication consumes some power, which is always a consideration in battery-powered devices, but in some circumstances, the cost of processing new information could be a much more severe resource drain.
In a multiagent system — the computer science term for any collaboration among autonomous agents, electronic or otherwise — each agent must maintain a model of the current state of the world, as well as a model of what each of the other agents takes to be the state of the world. These days, agents are also expected to factor in the probabilities that their models are accurate. On the basis of those probabilities, they have to decide whether or not to modify their behaviors.
Communication costs
In some scenarios, a robot’s decision to broadcast a new item of information could force its fellows to update their models and churn through all those probabilities again. If the information is inessential, broadcasting it could introduce serious delays, to no purpose. And the MIT researchers’ work suggests that 60 percent of communications in multiagent systems may be inessential.
The state-of-the-art method for modeling multiagent systems is called a decentralized partially observable Markov decision process, or Dec-POMDP. A Dec-POMDP factors in several types of uncertainty; not only does it consider whether an agent’s view of the world is correct and whether its estimate of its fellows’ worldviews is correct, it also considers whether any action it takes will be successful. The robot may plan, for instance, to move forward 20 feet but find that crosswinds blow it off course.
Dec-POMDPs generally assume some prior knowledge about the environment in which the agents will be operating. Because Shah and Vaibhav Unhelkar, a graduate student in aeronautics and astronautics and first author on the new paper, were designing a system with emergency-response applications in mind, they couldn’t make that assumption. Emergency-response teams will usually be entering unfamiliar environments, and the very nature of the emergency could render the best prior information obsolete.
Adding the requirement of mapping the environment on the fly, however, makes the problem of computing a multiagent plan prohibitively time consuming. So Shah and Unhelkar’s system ignores uncertainty about actions’ effectiveness and assumes that whatever an agent attempts to do, it will do.
Balancing act
When an agent acquires a new item of information — that, for instance, a given passage through a building is blocked — it has three choices: it can ignore the information; it can use it but not broadcast it; or it can use it and broadcast it.
Each of these choices has benefits but imposes costs. In Shah and Unhelkar’s model, communication is a cost. But if an agent incorporates new information into its own model of the world and doesn’t broadcast it, it also incurs a cost, as its worldview becomes more difficult for its fellows to estimate correctly. For every new item of information an agent acquires, Shah and Unhelkar’s system performs that cost-benefit analysis, based on the agent’s model of the world, its expectations of its fellows’ actions, and the likelihood of accomplishing the joint goal more efficiently.
The researchers tested their system on more than 300 computer simulations of rescue tasks in unfamiliar environments. A version of their system that permitted extensive communication completed the tasks at a rate between 2 and 10 percent higher than the version that reduced communication by 60 percent.
In the experiments, however, all the agents were electronic. “What I’d be willing to bet, although we have to wait until we do the human-subject experiments, is that the human-robot team will fail miserably if the system is just telling the person all sorts of spurious information all the time,” Shah says. “For human-robot teams, I think that this algorithm is going to make the difference between a team that can function effectively versus a team that just plain can’t.”
In a separate research project, members of Shah’s group have asked teams of human subjects to execute similar virtual rescue missions that computer systems did in the experiments reported in the new paper. Using machine-learning algorithms, the researchers have mined the results for statistics on human communication patterns, which can be incorporated into the new model to more explicitly accommodate human-robot teams.
“It is well-understood that in human teams, when one team member gains new information, broadcasting this new information to all team members is generally not a good solution, especially when the cost of communication is high,” says Tim Miller, an assistant professor of computing and information systems at the University of Melbourne in Australia. “This work has applications outside of multiagent systems, reaching into the critical area of human-agent collaboration, where communication can be costly, but more importantly, human team members are quickly overloaded if presented with too much information.”
Learn more: Enabling human-robot rescue teams
The Latest on: Human-agent collaboration
[google_news title=”” keyword=”human-agent collaboration” num_posts=”10″ blurb_length=”0″ show_thumb=”left”]
via Google News
The Latest on: Human-agent collaboration
- ServiceNow showcases generative AI service agents using NVIDIA AI Enterprise softwareon May 8, 2024 at 9:29 am
At Knowledge 2023 last year, NVIDIA and ServiceNow partnered to develop powerful enterprise‑grade generative AI capabilities. These capabilities leverage NVIDIA infrastructure for custom LLMs trained ...
- ServiceNow Adds Powerful New Solutions to the Now Platform to Transform the Employee Experience and Simplify Work Across the Enterpriseon May 8, 2024 at 9:29 am
ServiceNow Project RaptorDB, based on Postgres, the world's most advanced open-source database, acts as a foundational data layer that will allow ServiceNow customers to process massive volumes of ...
- 3 ways to craft a culture of collaboration in remote work modelson May 8, 2024 at 5:15 am
If done right, remote work can foster productivity and efficiency while promoting inclusivity, sustainability and employee wellbeing.
- How a decentralized AI movement is shaping a fairer futureon May 7, 2024 at 5:00 pm
The Morpheus Network, an open-source network building decentralized AI smart agents, announced its official launch on May 8, with over $350 million in total locked value (TVL), along with its ...
- Atlassian Rovo Enterprise Knowledge Tool Smartens Human-AI Collaborationon May 1, 2024 at 2:55 pm
Agents aren’t just some souped-up version ... Not because data-centric human-AI collaboration platforms are not advanced, they clearly already are. Many of the functions on offer here are ...
- Sanctuary AI announces Microsoft collaboration to accelerate AI development for general purpose robotson May 1, 2024 at 9:00 am
"We're excited to be working with Sanctuary AI to accelerate AI model innovation and embodied AI research in areas like reasoning, planning, and human-agent collaboration," says Ashley Llorens ...
- Sanctuary AI announces Microsoft collaboration to accelerate AI development for general purpose robotson May 1, 2024 at 9:00 am
Sanctuary AI, a company on a mission to create the world's first human-like intelligence in general purpose robots, is collaborating with Microsoft on the development of AI models for general purpose ...
- Sanctuary AI announces Microsoft collaboration to accelerate AI development for general purpose robotson May 1, 2024 at 4:59 am
VANCOUVER, BC, May 1, 2024 /PRNewswire/ - Sanctuary AI, a company on a mission to create the world's first human-like intelligence in general purpose robots, is collaborating with Microsoft on the ...
- Deadly human smuggling through Mexico thrives in ‘perfect cycle of impunity’on April 30, 2024 at 3:03 pm
A new collaboration from ICIJ and media partners in Latin America, Europe and the United States documents nearly 19,000 migrants’ journeys to the U.S. border under dangerous conditions.
- “The future of healthcare is in AI and human collaboration”on April 28, 2024 at 5:00 pm
42% of patient interactions occurred outside regular office hours, showcasing the strength of 24/7 availability which is not possible through human-only ... is one where collaboration between ...
via Bing News