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Human expectations arise from their understanding of others and the world. In the context of human-AI interaction, this understanding may not align with reality, leading to the AI agent failing to meet expectations and compromising team performance. Explicable planning, introduced as a method to bridge this gap, aims to reconcile human expectations with the agent's optimal behavior, facilitating interpretable decision-making. However, an unresolved critical issue is ensuring safety in explicable planning, as it could result in explicable behaviors that are unsafe. To address this, we propose Safe Explicable Planning (SEP), which extends the prior work to support the specification of a safety bound. The goal of SEP is to find behaviors that align with human expectations while adhering to the specified safety criterion. Our approach generalizes the consideration of multiple objectives stemming from multiple models rather than a single model, yielding a Pareto set of safe explicable policies. We present both an exact method, guaranteeing finding the Pareto set, and a more efficient greedy method that finds one of the policies in the Pareto set. Additionally, we offer approximate solutions based on state aggregation to improve scalability. We provide formal proofs that validate the desired theoretical properties of these methods. Evaluation through simulations and physical robot experiments confirms the effectiveness of our approach for safe explicable planning. 

In this paper, we introduce Max Markov Chain (MMC), a novel model for sequential data with sparse correlations among the state variables.It may also be viewed as a special class of approximate models for High-order Markov Chains (HMCs).MMC is desirable for domains where the sparse correlations are long-term and vary in their temporal stretches.Although generally intractable, parameter optimization for MMC can be solved analytically.However, based on this result,we derive an approximate solution that is highly efficient empirically.When compared with HMC and approximate HMC models, MMCcombines better sample efficiency, model parsimony, and an outstanding computational advantage.Such a quality allows MMC to scale to large domainswhere the competing models would struggle to perform.We compare MMC with several baselines with synthetic and real-world datasets to demonstrate MMC as a valuable alternative for stochastic modeling.