AI-based automated decisions are increasingly used as part of new services being deployed
to the general public. This approach to building services presents significant potential …

Justifying a plan to a user requires answering questions about the space of possible plans.
Recent work introduced a framework for doing so via planproperty dependencies, where …

The trade-offs between different desirable plan properties--eg PDDL temporal plan
preferences--are often difficult to understand. Recent work addresses this by iterative …

Algorithms play a crucial role in many technological systems that control or affect various
aspects of our lives. As a result, providing explanations for their decisions to address the …

Algorithms play a key role nowadays in many technological systems that control or affect
various aspects of our lives. As a result, providing explanations to address the needs of …

The development of robotics and AI agents has enabled their wider usage in human
surroundings. AI agents are more trusted to make increasingly important decisions with …

The trade-offs between different desirable plan properties–eg PDDL temporal plan
preferences–are often difficult to understand. Recent work proposes to address this by …

In a variety of application settings, the user preference for a planning task-the precise
optimization objective-is difficult to elicit. One possible remedy is planning as an iterative …

Global explanations of a reinforcement learning (RL) agent's expected behavior can make it
safer to deploy. However, such explanations are often difficult to understand because of the …

In a variety of application settings, the user preference for a planning task–the precise
optimization objective–is difficult to elicit. One possible remedy, suggested by prior work, is …