A review of symbolic, subsymbolic and hybrid methods for sequential decision making
In the field of Sequential Decision Making (SDM), two paradigms have historically vied for
supremacy: Automated Planning (AP) and Reinforcement Learning (RL). In the spirit of …
supremacy: Automated Planning (AP) and Reinforcement Learning (RL). In the spirit of …
Learning domain-independent planning heuristics with hypergraph networks
We present the first approach capable of learning domain-independent planning heuristics
entirely from scratch. The heuristics we learn map the hypergraph representation of the …
entirely from scratch. The heuristics we learn map the hypergraph representation of the …
Learning generalized policies without supervision using gnns
S Ståhlberg, B Bonet, H Geffner - ar** flexible AI systems is the split between data-based
learners and model-based solvers. Solvers such as classical planners are very flexible and …
learners and model-based solvers. Solvers such as classical planners are very flexible and …
Learning general planning policies from small examples without supervision
Generalized planning is concerned with the computation of general policies that solve
multiple instances of a planning domain all at once. It has been recently shown that these …
multiple instances of a planning domain all at once. It has been recently shown that these …
Generalized planning with deep reinforcement learning
A hallmark of intelligence is the ability to deduce general principles from examples, which
are correct beyond the range of those observed. Generalized Planning deals with finding …
are correct beyond the range of those observed. Generalized Planning deals with finding …
Learning generalized relational heuristic networks for model-agnostic planning
Computing goal-directed behavior is essential to designing efficient AI systems. Due to the
computational complexity of planning, current approaches rely primarily upon hand-coded …
computational complexity of planning, current approaches rely primarily upon hand-coded …