Automated planning is a prominent area of Artificial Intelligence and an important
component for intelligent autonomous agents. A cornerstone of domain-independent …

Partial-order plans (POPs) are attractive because of their least-commitment nature, which
provides enhanced plan flexibility at execution time relative to sequential plans. Current …

Blocks can contain blocks, so the definition is recursive BDPO plans can express more
linearizations than POCL plans:• In Blocks World with one gripper, there can't be parallelism• …

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 …

Capturing and exploiting structural knowledge of planning problems has shown to be a
successful strategy for making the planning process more ef-ficient. Plans can be …

This work investigates the problem of optimising a partial-order plan's (POP) flexibility
through the simultaneous transformation of its action ordering and variable binding …

While seminal work has studied the problem of relaxing the ordering of a plan's actions, less
attention has been given to the problem of relaxing and modifying a plan's variable bindings …

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 …

A partial-order plan (POP) compactly encodes a set of sequential plans that can be
dynamically chosen by an agent at execution time. One natural measure of the quality of a …

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