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 …

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 …

This paper considers a capability construction problem of the C4ISR system under service-
oriented architecture. A capability construction model is first established and described in …

The manipulation of articulated objects is of primary importance in Robotics and can be
considered as one of the most complex manipulation tasks. Traditionally, this problem has …

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 …

Recent work on Neural-Symbolic systems that learn the discrete planning model from
images has opened a promising direction for expanding the scope of Automated Planning …

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

Domain independent planning engines accept a planning task description in a language
such as PDDL and return a solution plan. Performance of planning engines can be …