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

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helper-T-cells

Opponent Modeling with Information Adaptation (OMIA) in Automated Negotiations,sidering 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

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Working Together: Committee Selection and the Supermodular Degree,e exists an approximation algorithm with approximation guarantees that deteriorate gracefully with the amount of synergy between the candidates. This amount of synergy is measured by a natural extension of the supermodular degree [Feige and Izsak, ITCS 2013] that we introduce – the .. To the best of

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On the Deployment of Factor Graph Elements to Operate Max-Sum in Dynamic Ambient Environments,alized manner. But, when dealing with the dynamics of the environment (e.g. new sensed data which activates some rules, adding new devices, etc.) we cannot afford restarting the system or relying on a centralized solver. Thus, the system has to achieve on-line and local deployment adaptations. In th

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Evaluating Ad Hoc Teamwork Performance in Drop-In Player Challenges,es. 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

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Towards a Fast Detection of Opponents in Repeated Stochastic Games,s 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.

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Event Calculus Agent Minds Applied to Diabetes Monitoring, 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