Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Despite the intractability of generic optimal partially observable Markov decision process planning, there exist important problems that have highly structured models. Previous researchers have used this insight to construct more efficient algorithms for factored domains, and for domains with topological structure in the flat state dynamics model. In our work, motivated by findings from the education community relevant to automated tutoring,  consider problems that exhibit a form of topological structure in the factored dynamics model. Our Reachable Anytime Planner for Imprecisely-sensed Domains (RAPID) leverages this structure to efficiently compute a good initial envelope of reachable states under the optimal MDP policy in time linear in the number of state variables. RAPID performs partially-observable planning over the limited envelope of states, and slowly expands the state space considered as time allows. RAPID performs ll on a large tutoring-inspired problem simulation with 122 state variables, corresponding to a flat state space of over 1030 states.
['Emma Brunskill', 'Stuart J. Russell']
I am writing a research paper on a idea RAPID: a reachable anytime planner for imprecisely-sensed domains
[{"from": "human", "value": "I am writing a research paper on a idea RAPID: a reachable anytime planner for imprecisely-sensed domains"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Despite the intractability of generic optimal partially observable Markov decision process planning, there exist important problems that have highly structured models. Previous researchers have used this insight to construct more efficient algorithms for factored domains, and for domains with topological structure in the flat state dynamics model. In our work, motivated by findings from the education community relevant to automated tutoring,  consider problems that exhibit a form of topological structure in the factored dynamics model. Our Reachable Anytime Planner for Imprecisely-sensed Domains (RAPID) leverages this structure to efficiently compute a good initial envelope of reachable states under the optimal MDP policy in time linear in the number of state variables. RAPID performs partially-observable planning over the limited envelope of states, and slowly expands the state space considered as time allows. RAPID performs ll on a large tutoring-inspired problem simulation with 122 state variables, corresponding to a flat state space of over 1030 states."}]