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Omer Tsimhoni

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2 papers

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AAAI Conference 2016 Conference Paper

Personalized Alert Agent for Optimal User Performance

Avraham Shvartzon
Amos Azaria
Sarit Kraus
Claudia Goldman
Joachim Meyer
Omer Tsimhoni

Preventive maintenance is essential for the smooth operation of any equipment. Still, people occasionally do not maintain their equipment adequately. Maintenance alert systems attempt to remind people to perform maintenance. However, most of these systems do not provide alerts at the optimal timing, and nor do they take into account the time required for maintenance or compute the optimal timing for a speciﬁc user. We model the problem of maintenance performance, assuming maintenance is time consuming. We solve the optimal policy for the user, i. e. , the optimal timing for a user to perform maintenance. This optimal strategy depends on the value of user’s time, and thus it may vary from user to user and may change over time. Based on the solved optimal strategy we present a personalized maintenance agent, which, depending on the value of user’s time, provides alerts to the user when she should perform maintenance. In an experiment using a spaceship computer game, we show that receiving alerts from the personalized alert agent signiﬁcantly improves user performance.

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AAMAS Conference 2012 Conference Paper

Giving Advice to People in Path Selection Problems

Amos Azaria
Zinovi Rabinovich
Sarit Kraus
Claudia Goldman
Omer Tsimhoni

We present a novel computational method for advice-generation in path selection problems which are difficult for people to solve. The advisor agent's interests may conflict with the interests of the people who receive the advice. Such optimization settings arise in many human-computer applications in which agents and people are self-interested but also share certain goals, such as automatic route-selection systems that also reason about environmental costs. This paper presents an agent that clusters people into one of several types, based on how their path selection behavior adheres to the paths presented to them by the agent who does not necessarily suggest their most preferred paths. It predicts the likelihood that people will deviate from these suggested paths and uses a decision theoretic approach to suggest paths to people which will maximize the agent's expected benefit given the people's deviations. This technique was evaluated empirically in an extensive study involving hundreds of human subjects solving the path selection problem in mazes. Results showed that the agent was able to outperform alternative methods that solely considered the benefit to the agent or the person, or did not provide any advice.

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