Improving Type Recognition in Stochastic Environments using Redesign
نویسندگان
چکیده
We consider a stochastic environment with multiple possible agent types. In this context we focus on the problem of type recognition, which aims at quickly identifying the type of agent acting in the system. We offer a framework for assessing type recognition models and ways to redesign the models for improved recognition. Allowing the definition of arbitrary system cost functions for unrecognized behavior, we offer tools for calculating the maximal expected system cost caused by an agent whose type is not revealed. We then present ways of redesigning the model to minimize the proposed measure by modifying the stochastic environment. This approach can combine an offline preprocessing stage and an online analysis, generalizing previous work on deterministic goal recognition design.
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