AI planning based quasi-montecarlo simulation method for probabilistic planning
First Claim
1. A computer-implemented method for artificial intelligence (AI) planning based quasi-Monte Carlo simulation for probabilistic planning, comprising:
- using a computer processor, storing into a computer memory an initial state of a system and a description of a target domain;
generating a set of possible actions for the initial state;
for each action in the set of the possible actions, performing a sequence of actions, comprising;
generating by an AI planner a set of sample future outcomes for the initial state;
generating by a quasi-Monte Carlo simulation module probabilities of solutions for each of the sample future outcomes;
evaluating future outcome solutions that are either highest probability, or lowest probability and highest-impact, relative to the solutions generated by the AI planner, wherein the AI planner searches a probabilistic planning tree for harmful sequences of actions which are either highest probability, or lowest probability and highest-impact, relative to the solutions generated by the AI planner for focused evaluation thereof;
aggregating the evaluated solutions with future outcome solutions generated by the quasi-Monte Carlo simulation module, each of the aggregated solutions indicating a state of the system after a corresponding outcome occurs; and
analyzing the aggregated set of future outcome solutions;
automatically selecting a best action based at least partially on the analysis of the aggregated set of future outcome solutions; and
outputting the selected best action to computer memory for probabilistic planning.
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Abstract
A computer-based method and system for AI planning based quasi-Monte Carlo simulation for probabilistic planning are provided. The method includes generating a set of possible actions for an initial state, generating a set of sample future outcomes, generating solutions for each of the sample future outcomes, using an AI planner, generating a set of future outcome solutions that are low probability and high-impact, combining the solutions generated from each of the sample future outcomes with the future outcome solutions generated by the AI Planner into an aggregated set of future outcome solutions, analyzing the aggregated set of future outcome solutions, selecting a best action based at least partially on the analysis of the aggregated set of future outcome solutions, and outputting the selected best action to computer memory.
32 Citations
23 Claims
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1. A computer-implemented method for artificial intelligence (AI) planning based quasi-Monte Carlo simulation for probabilistic planning, comprising:
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using a computer processor, storing into a computer memory an initial state of a system and a description of a target domain; generating a set of possible actions for the initial state; for each action in the set of the possible actions, performing a sequence of actions, comprising; generating by an AI planner a set of sample future outcomes for the initial state; generating by a quasi-Monte Carlo simulation module probabilities of solutions for each of the sample future outcomes; evaluating future outcome solutions that are either highest probability, or lowest probability and highest-impact, relative to the solutions generated by the AI planner, wherein the AI planner searches a probabilistic planning tree for harmful sequences of actions which are either highest probability, or lowest probability and highest-impact, relative to the solutions generated by the AI planner for focused evaluation thereof; aggregating the evaluated solutions with future outcome solutions generated by the quasi-Monte Carlo simulation module, each of the aggregated solutions indicating a state of the system after a corresponding outcome occurs; and analyzing the aggregated set of future outcome solutions; automatically selecting a best action based at least partially on the analysis of the aggregated set of future outcome solutions; and outputting the selected best action to computer memory for probabilistic planning. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)
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14. A computer-based system for artificial intelligence (AI) planning based quasi-Monte Carlo simulation for probabilistic planning, comprising:
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an AI planner; and a quasi-Monte Carlo simulation module adapted to; store an initial state of a system and a description of a target domain into a computer memory; generate a set of possible actions for the initial state for a desired initial state; for each action in the set of the possible actions, perform a sequence of actions, comprising; generating by the AI planner a set of sample future outcomes for the initial state; generating probabilities of solutions for each of the sample future outcomes; evaluating a set of future outcome solutions that are either highest probability, or lowest probability and highest-impact, relative to the solutions generated by the AI planner, wherein the AI planner searches a probabilistic planning tree for harmful sequences of actions which are either highest probability, or lowest probability and highest-impact, relative to the solutions generated by the AI planner for focused evaluation thereof; combining the evaluated solutions with future outcome solutions generated by the quasi-Monte Carlo simulation module into an aggregated set of future outcome solutions, each of the aggregated solutions indicating a state of the system after a corresponding outcome occurs; and analyzing the aggregated set of future outcome solutions; automatically selecting a best action based at least partially on the analysis of the aggregated set of future outcome solutions; and outputting the selected best action to computer memory for probabilistic planning. - View Dependent Claims (15, 16, 17, 18, 19, 20, 21, 22, 23)
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Specification