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

Recent advances in reinforcement learning (RL) have led to a growing interest in applying
RL to classical planning domains or applying classical planning methods to some complex …

Best-first search algorithms such as A* need to apply tie-breaking strategies in order to
decide which node to expand when multiple search nodes have the same evaluation score …

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 …

Macro actions have been demonstrated to be beneficial for the learning processes of an
agent and have encouraged a variety of techniques to be developed for constructing more …

Abstract Agents that use Multi-Agent Forward Search (MAFS) todo privacy-preserving
planning, often repeatedly develop similar paths. We describe a simple technique for online …

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 …

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

The difficulty of deterministic planning increases exponentially with search-tree depth. Black-
box planning presents an even greater challenge, since planners must operate without an …

The use of AI planning beyond demonstration examples has proven to be challenging for
expressive problems with numerous components. This happens primarily because the state …