Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Conflict-directed learning is ubiquitous in constraint satisfaction problems like SAT, but has been elusive for state space search on reachability problems like classical planning. Almost all existing approaches learn nogoods relative to a fixed solution-length bound, in which case planning/reachability reduces to a constraint satisfaction problem. Here  introduce an approach to learning more porful nogoods, that are sound regardless of solution length, i.e., that identify dead-end states for which no solution exists.#R##N##R##N#The key technique  build on are critical-path heuristics  hChC, relative to a set C of conjunctions. These recognize a dead-end state s  , returning hC(s)=∞hC(s)=∞, if s has no solution even when allowing to break up conjunctive subgoals into the elements of C. Our key idea is to learn C during search. Whenever forward search has identified an unrecognized dead-end s  , where hC(s)<∞hC(s)<∞,  analyze the situation at s, and add new conjunctions into C   in a way guaranteeing to obtain hC(s)=∞hC(s)=∞. Thus  learn to recognize s, as ll as similar dead-ends search may encounter in the future.  furthermore learn clauses ϕ   where s′⊭ϕs′⊭ϕ implies hC(s′)=∞hC(s′)=∞, to avoid the overhead of computing hChC on every search state. Arranging these techniques in a depth-first search,  obtain an algorithm approaching the elegance of nogood learning in constraint satisfaction, learning to refute search subtrees.#R##N##R##N# run comprehensive experiments on solvable and unsolvable planning benchmarks. In cases where forward search can identify dead-ends, and where hChC dead-end detection is effective, our techniques reduce the depth-first search space size by several orders of magnitude, and often result in state-of-the-art performance.
['Marcel Steinmetz', 'Jörg Hoffmann']
I am writing a research paper on a idea State space search nogood learning: Online refinement of critical-path dead-end detectors in planning
[{"from": "human", "value": "I am writing a research paper on a idea State space search nogood learning: Online refinement of critical-path dead-end detectors in planning"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Conflict-directed learning is ubiquitous in constraint satisfaction problems like SAT, but has been elusive for state space search on reachability problems like classical planning. Almost all existing approaches learn nogoods relative to a fixed solution-length bound, in which case planning/reachability reduces to a constraint satisfaction problem. Here  introduce an approach to learning more porful nogoods, that are sound regardless of solution length, i.e., that identify dead-end states for which no solution exists.#R##N##R##N#The key technique  build on are critical-path heuristics \u00a0hChC, relative to a set C of conjunctions. These recognize a dead-end state s \u00a0, returning hC(s)=\u221ehC(s)=\u221e, if s has no solution even when allowing to break up conjunctive subgoals into the elements of C. Our key idea is to learn C during search. Whenever forward search has identified an unrecognized dead-end s \u00a0, where hC(s)<\u221ehC(s)<\u221e,  analyze the situation at s, and add new conjunctions into C \u00a0 in a way guaranteeing to obtain hC(s)=\u221ehC(s)=\u221e. Thus  learn to recognize s, as ll as similar dead-ends search may encounter in the future.  furthermore learn clauses \u03d5 \u00a0 where s\u2032\u22ad\u03d5s\u2032\u22ad\u03d5 implies hC(s\u2032)=\u221ehC(s\u2032)=\u221e, to avoid the overhead of computing hChC on every search state. Arranging these techniques in a depth-first search,  obtain an algorithm approaching the elegance of nogood learning in constraint satisfaction, learning to refute search subtrees.#R##N##R##N# run comprehensive experiments on solvable and unsolvable planning benchmarks. In cases where forward search can identify dead-ends, and where hChC dead-end detection is effective, our techniques reduce the depth-first search space size by several orders of magnitude, and often result in state-of-the-art performance."}]