Social AI agents interact with members of a community, thereby changing the behavior of the community. For example, in online learning, an AI social assistant may connect learners and thereby enhance social interaction. These social AI assistants too need to explain themselves in order to enhance ...
aShow a dialog to change the types of the tools in the current process 显示对话改变工具的种类在当前过程中[translate] a得到VP的批准 Is approved VP[translate] a六毛五 Six wools five[translate] awords can't explain how much I miss you... 词不可能说明多少我想念您…[translate]...
In autonomous multiagent or multirobotic systems, the ability to quickly and accurately respond to threats and uncertainties is important for both mission outcomes and survivability. Such systems are never truly autonomous, often operating as part of a human-agent team. Artificial intelligent agents (...
The scientists are now experimenting with whether phantom costs play into humans’ interactions withrobotsand artificial intelligence. “If AI is overly generous, will people imagine phantom costs?” Vonasch says. “People tend to anthropomorphize and treat [AI agents] as if they have a mind, w...
Similar models reconstruct the structure of other systems, such as protein interaction networks37 and the World Wide Web38. However, the model of Jackson and Rogers27 (like most other models in this family) is based on a constantly growing network with no death or emigration of agents and ...
In practice, the everyday speech recognition we encounter in things like automated call centers, computer dictationsoftware, or smartphone "agents" (like Siri and Cortana) combines a variety of different approaches. For the purposes of understanding clearly how things work, however, it's best to ...
Word of mouth, electronic word of mouth, and recommendations of travel agents are considered the most powerful communication channels in this industry. Travelers use the internet in all phases of their decision-making processes: 1) before the experience (to get information and decide on the ...
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Participants' prioritization of climate policy was highest when both the political ingroup and the outgroup prioritized climate policy. Ingroup norms had a strong influence on personal policy prioritization whereas outgroup norms did not. These findings demonstrate that, beyond personal beliefs and other...
This study proposes a multi-agent market model to derive the microfoundations of the GARCH model, incorporating three types of agents: noise traders, fundamental traders, and AI traders. By mathematically aggregating the micro-structure of these agents, we establish the microfoundations of the GARCH ...