Predicting Human Decision-Making: From Prediction to Action

Research output: Contribution to journalArticlepeer-review


Automated agents that interact proficiently with people can be
useful in supporting, training or replacing people in complex tasks.
The inclusion of people presents novel problems for the design of
automated agents’ strategies. People do not necessarily adhere to
the optimal, monolithic strategies that can be derived analytically.
Their behavior is affected by a multitude of social and psychological
factors. In this talk I will show how combining machine learning
techniques for human modeling, human behavioral models, formal
decision-making and game theory approaches enables agents to
interact well with people. Applications include intelligent agents
that help drivers reduce energy consumption, agents that support
rehabilitation, employer-employee negotiation and agents that support a human operator in managing a team of low-cost mobile
robots in search and rescue tasks.
Original languageEnglish
Number of pages1
JournalProceedings of the 6th International Conference on Human-Agent Interaction,
StatePublished - Dec 2018


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