Stackelberg planning is a recent framework where a leader and a follower each choose a
plan in the same planning task, the leader's objective being to maximize plan cost for the …

We present a novel approach to hierarchical reinforcement learning for linearly-solvable
Markov decision processes. Our approach assumes that the state space is partitioned, and …

Stochastic shortest-path problems (SSP) are an important subclass of MDPs for which
heuristic search algorithms exist since over a decade. Yet most known heuristic functions …

Conflict-directed learning is ubiquitous in constraint satisfaction problems like SAT, but has
been elusive for state space search on reachability problems like classical planning. Almost …

The merge-and-shrink framework is a powerful tool to construct state space abstractions
based on factored representations. One of its core applications in classical planning is the …

The increase in robotic capabilities and the number of such systems being used has
resulted in opportunities for robots to work alongside humans in an increasing number of …

We introduce a state space search method that identifies dead-end states, analyzes the
reasons for failure, and learns to avoid similar mistakes in the future. Our work is placed in …

We present a novel approach to fully-observable nondeterministic planning (FOND) that
attempts to bridge the gap between symbolic fix-point computation and recent approaches …

Maximizing goal probability is an important objective in probabilistic planning, yet algorithms
for its optimal solution are severely underexplored. There is scant evidence of what the …

Computing a good policy in stochastic uncertain environments with unknown dynamics and
reward model parameters is a challenging task. In a number of domains, ranging from space …