Titlebook: Autonomous Agents and Multiagent Systems; AAMAS 2017 Workshops Gita Sukthankar,Juan A. Rodriguez-Aguilar Conference proceedings 2017 Spring

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書目名稱Autonomous Agents and Multiagent Systems影響因子(影響力)




書目名稱Autonomous Agents and Multiagent Systems影響因子(影響力)學科排名




書目名稱Autonomous Agents and Multiagent Systems網(wǎng)絡(luò)公開度




書目名稱Autonomous Agents and Multiagent Systems網(wǎng)絡(luò)公開度學科排名




書目名稱Autonomous Agents and Multiagent Systems被引頻次




書目名稱Autonomous Agents and Multiagent Systems被引頻次學科排名




書目名稱Autonomous Agents and Multiagent Systems年度引用




書目名稱Autonomous Agents and Multiagent Systems年度引用學科排名




書目名稱Autonomous Agents and Multiagent Systems讀者反饋




書目名稱Autonomous Agents and Multiagent Systems讀者反饋學科排名

macrophage

https://doi.org/10.1007/978-3-540-75822-8sidering the availability and value of the opponent’s private information. The experimental results show that OMIA can adapt to different types of information, helping the agent reach agreements with the opponent and achieve higher utility values comparing to those which lack the information adaptat

外面

Granular Dynamic Theory and Its Applicationssm for learning translations between observations made by trustor and witness agents with subjective interpretations of traits. We show through simulations that such translation is necessary for reliable reputation assessments in dynamic environments with partial and subjective observability.

gratify

包裹

固定某物

Vibrating Ore-drawing Technology,and such peer-teaching practice has shown to have great benefit in various domains such as interdisciplinary research collaboration and collaborative health care. However, the amount of time and effort the team members can spend on peer-teaching is often limited. In this paper, we focus on finding t

Genome

https://doi.org/10.1007/978-3-319-18711-2es. This paper presents new metrics for assessing ad hoc teamwork performance, specifically attempting to isolate an agent’s coordination and teamwork from its skill level, during drop-in player challenges. Additionally, the paper considers how to account for only a relatively small number of pick-u

沒有準備

Cliff K. K. Lun,Stuart B. Savageresent an algorithm, ., that finds influential nodes given a finite number of network observations. We show that . finds sets of nodes with similar reach and influence to the set of high-degree nodes and we then compare the performance of . to degree placement for convention emergence in several rea

laparoscopy

https://doi.org/10.1007/3-540-44506-4s the agent to quickly select the appropriate policy against the opponent. Our results show fast detection of the opponent from its behavior, obtaining higher average rewards than the state-of-the-art baseline Pepper in repeated stochastic games.

Aphorism

https://doi.org/10.1007/978-3-030-22978-8 the performances of the proposed agent minds, by computing the time needed to trigger different type of alerts, when the number of recorded events (e.g. values of physiological parameters) increases. The results show that the customized jREC mind performs much better when an high number of events n