Balancing Explicability and Explanation in Human-Aware Planning
  Human aware planning requires an agent to be aware of the intentions,
capabilities and mental model of the human in the loop during its decision
process. This can involve generating plans that are explicable to a human
observer as well as the ability to provide explanations when such plans cannot
be generated. This has led to the notion "multi-model planning" which aim to
incorporate effects of human expectation in the deliberative process of a
planner - either in the form of explicable task planning or explanations
produced thereof. In this paper, we bring these two concepts together and show
how a planner can account for both these needs and achieve a trade-off during
the plan generation process itself by means of a model-space search method
MEGA. This in effect provides a comprehensive perspective of what it means for
a decision making agent to be "human-aware" by bringing together existing
principles of planning under the umbrella of a single plan generation process.
We situate our discussion specifically keeping in mind the recent work on
explicable planning and explanation generation, and illustrate these concepts
in modified versions of two well known planning domains, as well as a
demonstration on a robot involved in a typical search and reconnaissance task
with an external supervisor.
