A number of algorithms in the field of artificial intelligence offer poorly interpretable
decisions. To disclose the reasoning behind such algorithms, their output can be explained …

In this paper, we provide a comprehensive outline of the different threads of work in
Explainable AI Planning (XAIP) that has emerged as a focus area in the last couple of years …

Reinforcement Learning (RL) has achieved tremendous development in recent years, but
still faces significant obstacles in addressing complex real-life problems due to the issues of …

In human-aware planning systems, a planning agent might need to explain its plan to a
human user when that plan appears to be non-feasible or sub-optimal. A popular approach …

Grounding navigational commands to linear temporal logic (LTL) leverages its unambiguous
semantics for reasoning about long-horizon tasks and verifying the satisfaction of temporal …

With the widespread use of Artificial Intelligence (AI), understanding the behavior of
intelligent agents and robots is crucial to guarantee successful human-agent collaboration …

Linear temporal logic (LTL) is a specification language for finite sequences (called traces)
widely used in program verification, motion planning in robotics, process mining, and many …

We address the problem of inferring descriptions of system behavior using Linear Temporal
Logic (LTL) from a finite set of positive and negative examples. Most of the existing …

Autonomous agents are increasingly being deployed amongst human end-users. Yet,
human users often have little knowledge of how these agents work or what they will do next …

End-users' trust in automated agents is important as automated decision-making and
planning is increasingly used in many aspects of people's lives. In real-world applications of …