Difference between revisions of "Agent-Human Collaboration"
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| + | January 19 Talk on Agent-Human Collaboration with ACM Athena Lecturer Sarit Kraus | ||
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| + | Register now for the next free ACM TechTalk, "Agent-Human Collaboration and Learning for Improving Human Satisfaction," presented on Tuesday, January 19, at 11:00 AM ET/8:00 AM PT by Sarit Kraus, Professor at Bar-Ilan University and 2020-2021 ACM Athena Lecturer. | ||
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| + | Leave your comments and questions with our speaker now and any time before the live event on ACM's Discourse Page. And check out the page after the webcast for extended discussion with your peers in the computing community, as well as further resources on agent-human collaboration. | ||
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| + | (If you'd like to attend but can't make it to the virtual event, you still need to register to receive a recording of the TechTalk when it becomes available.) | ||
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| + | Note: You can stream this and all ACM TechTalks on your mobile device, including smartphones and tablets. | ||
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| + | We consider environments where a set of human workers needs to handle a large set of tasks while interacting with human users. The arriving tasks vary: they may differ in their urgency, their difficulty, and the required knowledge and time duration in which to perform them. Our goal is to decrease the number of workers, which we refer to as operators that are handling the tasks while increasing the users’ satisfaction. We present automated intelligent agents that will work together with the human operators in order to improve the overall performance of such systems and increase both operators’ and users’ satisfaction. Examples include: home hospitalization environment where remote specialists will instruct and supervise treatments that are carried out at the patients' homes; operators that tele-operate autonomous vehicles when human intervention is needed, and bankers that provide online service to customers. The automated agents could support the operators: the machine learning-based agent follows the operator’s work and makes recommendations, helping him interact proficiently with the users. The agents can also learn from the operators and eventually replace the operators in many of their tasks. | ||
==Reverences== | ==Reverences== | ||
[[Category:User Experience]] | [[Category:User Experience]] | ||
Revision as of 16:29, 5 January 2021
Contents
Full Title or Meme
Improved User Experience of the interactions of humans with agents that handle some tasks normally assigned to Humans.
Context
Human time is getting more valuable, computer time less valuable.
Case where Agent works on behalf of a User
Case where Agent works on behalf of a Service
January 19 Talk on Agent-Human Collaboration with ACM Athena Lecturer Sarit Kraus
Register now for the next free ACM TechTalk, "Agent-Human Collaboration and Learning for Improving Human Satisfaction," presented on Tuesday, January 19, at 11:00 AM ET/8:00 AM PT by Sarit Kraus, Professor at Bar-Ilan University and 2020-2021 ACM Athena Lecturer.
Leave your comments and questions with our speaker now and any time before the live event on ACM's Discourse Page. And check out the page after the webcast for extended discussion with your peers in the computing community, as well as further resources on agent-human collaboration.
(If you'd like to attend but can't make it to the virtual event, you still need to register to receive a recording of the TechTalk when it becomes available.)
Note: You can stream this and all ACM TechTalks on your mobile device, including smartphones and tablets.
We consider environments where a set of human workers needs to handle a large set of tasks while interacting with human users. The arriving tasks vary: they may differ in their urgency, their difficulty, and the required knowledge and time duration in which to perform them. Our goal is to decrease the number of workers, which we refer to as operators that are handling the tasks while increasing the users’ satisfaction. We present automated intelligent agents that will work together with the human operators in order to improve the overall performance of such systems and increase both operators’ and users’ satisfaction. Examples include: home hospitalization environment where remote specialists will instruct and supervise treatments that are carried out at the patients' homes; operators that tele-operate autonomous vehicles when human intervention is needed, and bankers that provide online service to customers. The automated agents could support the operators: the machine learning-based agent follows the operator’s work and makes recommendations, helping him interact proficiently with the users. The agents can also learn from the operators and eventually replace the operators in many of their tasks.
