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Strategy Extraction in Single-Agent Games
As a first step toward this goal, we develop a method for extracting strategies from an agent's existing knowledge that can be applied in multiple contexts.
Sampling from Pre-Images to Learn Heuristic Functions for Classical Planning
We introduce a new algorithm, Regression based Supervised Learning (RSL), for learning per instance Neural Network (NN) defined heuristic functions for classical planning problems.
Efficient Multi-agent Epistemic Planning: Teaching Planners About Nested Belief
Many AI applications involve the interaction of multiple autonomous agents, requiring those agents to reason about their own beliefs, as well as those of other agents.
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