We achieved a new milestone in the difficult task of enabling agents to learn about their
environment autonomously. Our neuro-symbolic architecture is trained end-to-end to …

Current domain-independent, classical planners require symbolic models of the problem
domain and instance as input, resulting in a knowledge acquisition bottleneck. Meanwhile …

Macro-operators, macros for short, are a well-known technique for enhancing performance
of planning engines by providing “short-cuts” in the state space. Existing macro learning …

Finding high quality plans for large planning problems is hard. Although some current
anytime planners are often able to improve plans quickly, they tend to reach a limit at which …

Plan deordering aids several tasks such as plan reuse, modification, and plan
decomposition by preserving only the necessary orderings among the actions in a plan …

Plan deordering removes unnecessary ordering constraints between actions in a plan,
facilitating plan execution flexibility and several other tasks, such as plan reuse …

Partial-order plans in AI planning facilitate execution flexibility due to their less-constrained
nature. Maximizing plan flexibility has been studied through the notions of plan deordering …

In the field of automated planning, the central research focus is on domain‐independent
planning engines that accept planning tasks (domain models and problem descriptions) in a …

Macro-operators (macros) are a well-known technique for enhancing performance of
planning engines by providing “short-cuts” in the state space. Existing macro learning …

A macro operator is a planning operator that is generated from a sequence of actions.
Macros have mostly been used for macro planning, where the planner considers the macro …