Abstract
This chapter introduces the Planning and Learning to Adapt Swiftly to Teammates to Improve Cooperation (PLASTIC) algorithms that enable an ad hoc team agent to cooperate with a variety of different teammates. One might think that the most appropriate thing for an ad hoc team agent to do is to “fit in” with its team by following the same behavior as its teammates. However, if the teammates’ behaviors are suboptimal, this approach will limit how much the ad hoc agent can help its team. Therefore, in this book, we adopt the approach of learning about different teammates and deciding how to act by leveraging this knowledge. This approach allows an ad hoc agent to reason about how well its knowledge of past teammates predicts its current teammates’ actions as well as to convert this knowledge into the actions it needs to take to accomplish its goals. If the knowledge of prior teammates accurately predicts the current teammates and the ad hoc agent is given enough time to plan, this approach will lead to optimal performance of the ad hoc agent, helping its team achieve the best possible outcome. Note that this may not be the optimal performance of any team, but it is optimal for the ad hoc agent given that the behaviors of its teammates are fixed.
This chapter contains material from three publications: [1–4]. Note that some of Sect. 5.2 is joint work with Sarit Kraus and Avi Rosenfeld in addition to Peter Stone [3].
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Barrett, S. (2015). The PLASTIC Algorithms. In: Making Friends on the Fly: Advances in Ad Hoc Teamwork. Studies in Computational Intelligence, vol 603. Springer, Cham. https://doi.org/10.1007/978-3-319-18069-4_5
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DOI: https://doi.org/10.1007/978-3-319-18069-4_5
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