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Goal recognition design involves the offline analysis of goal recognition models by formulating measures that assess the ability to perform goal recognition within a model and finding efficient ways to compute and optimize them. In this work we present goal recognition design for non-optimal agents, which extends previous work by accounting for agents that(More)
In this work we present the solution to the DEBS'2013 Grand Challenge, as crafted by the joint effort of teams from the Technion and TU Dortmund. The paper describes the architecture, details the queries, shows throughput and latency evaluation, and offers our observations regarding the appropriate way to trade-off high-level processing with time(More)
Big brother is watching but his eyesight is not all that great, since he only has partial observability of the environment. In such a setting agents may be able to preserve their privacy by hiding their true goal, following paths that may lead to multiple goals. In this work we present a framework that supports the offline analysis of goal recognition(More)
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(More)
We present the Equi-Reward Utility Maximizing Design (ER-UMD) problem for redesigning stochastic environments to maximize agent performance. ER-UMD fits well contemporary applications that require offline design of environments where robots and humans act and cooperate. To find an optimal modification sequence we present two novel solution techniques: a(More)
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