The past decade has seen a significant breakthrough in research on solving partially
observable Markov decision processes (POMDPs). Where past solvers could not scale …

Generalized planning studies the representation, computation and evaluation of solutions
that are valid for multiple planning instances. These are topics studied since the early days …

Past work on plan explanations primarily involved the AI system explaining the correctness
of its plan and the rationale for its decision in terms of its own model. Such soliloquy is …

Conformant planning is the problem of finding a sequence of actions for achieving a goal in
the presence of uncertainty in the initial state or action effects. The problem has been …

Many AI applications involve the interaction of multiple autonomous agents, requiring those
agents to reason about their own beliefs, as well as those of other agents. However …

Single-agent planning in partially observable settings is a well understood problem and
existing planners can represent and solve a wide variety of meaningful instances. In the …

The objective of goal recognition is to infer a goal that accounts for the observed behavior of
an actor. In this work, we introduce and formalize the notion of active goal recognition in …

This paper deals with the problem of classical planning for multiple cooperative agents who
have private information about their local state and capabilities they do not want to reveal …

Abstract Model reconciliation has been proposed as a way for an agent to explain its
decisions to a human who may have a different understanding of the same planning …

Generalized Planning: Synthesizing Plans that Work for Multiple Environments Page 1
Generalized Planning: Synthesizing Plans that Work for Multiple Environments Toby Hu (University …