Titlebook: Distributed Artificial Intelligence; Third International Jie Chen,Jér?me Lang,Dengji Zhao Conference proceedings 2022 Springer Nature Swit

outset

The Positive Effect of User Faults over Agent Perception in Collaborative Settings and Its Use in A Through a series of extensive experiments we find that user faults make the user more tolerant to agent faults, and consequently more satisfied with the collaboration, in particular compared to the case where the user is performing faultlessly. This finding can be utilized for improving the design

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Behavioral Stable Marriage Problems,dent doctors to hospitals and students to schools. Several preference models have been considered in the context of SMPs including orders with ties, incomplete orders, and orders with uncertainty, but none have yet captured behavioral aspects of human decision making, e.g., contextual effects of cho

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FUN-Agent: A HUMAINE Competitor,s have been pushing the frontier of new modalities of peer-level and ad-hoc human agent collaboration?[., .]. We are particularly interested in research on agents representing human users in negotiating deals with other human and autonomous agents?[., ., .]. Here we present the design for the conver

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Madrigal

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,Combining M-MCTS and?Deep Reinforcement Learning for?General Game Playing,ed on game rules without human intervention. Most recent work has successfully applied deep reinforcement learning to GGP. This paper continues this line of work by integrating the Memory-Augmented Monte Carlo Tree Search algorithm (M-MCTS) with deep reinforcement learning for General Game Playing.

割公牛膨脹

,A Two-Step Method for?Dynamics of?Abstract Argumentation,tion framework by the intersection of expansions of the conflict-free labellings of argumentation frameworks before updating. Then we select the conflict-free labellings which have the least illegally labelled arguments when restricted to part of both argumentation frameworks in the update process.

領(lǐng)巾

New Introduction by David Lowenthal (2003)y selectively revealing information to players in order to influence their actions. Most previous studies have focused on the . question of designing optimal signaling schemes. This work departs from previous research by considering a . question, and looks to quantitatively characterize the . (.), i

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Frederick Steiner,Gerald Young,Ervin Zubeent fields, the underlying structure and learning dynamics of value function, especially with complex function approximation, are not fully understood. In this paper, we report that decreasing rank of .-matrix widely exists during learning process across a series of continuous control tasks for diff

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