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This paper studies the consensus problem of multiple agents with discrete-time second-order dynamics.
In this paper, the behavior of multiple agents with linear dynamics is investigated by the study of interaction topologies.
The unique hybrid and compartmentalized structures provide the great potential for co-delivery of multiple agents even the multiple agents with different physicochemical properties.
This idea has gained increasing interest in the artificial intelligence (AI) community, where it is used as a method to control a single agent in a system composed of multiple agents with unknown behaviours.
We focus on a game-theoretic regression learning setting where private information is elicited from multiple agents with different, possibly conflicting, views on how to label the points of an input space.
In reality, however, transactions are often extremely difficult and costly, especially when multiple agents with differing interests are involved.
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In this paradigm, one begins treatment with multiple agents or agents with broader coverage profiles based on local susceptibility data and then adjusts and/or discontinues anti-infectives as culture and sensitivity (C&S) results return.
Many of these scenarios are designed to test advanced problems that I will not discuss here—for instance, challenges dealing with multiple agents, or with continuous changes.
The idea is, there are multiple agents, each with an implicit knowledge operator, and there are justification terms, which each agent understands.
Optimal breast cancer chemotherapy regimens include multiple agents, some with known cardiac side effects.
Thus, the information-as-range stance most naturally highlights agency and the dynamics of information in settings with multiple agents that can interact with each other.
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