Abstract: A computer-implemented method for generating a policy for performing a goal and including a plurality of intra-option policies includes the following operations. A planning domain including lifted action models is received. A Markov Decision Process (MDP) distribution is received. A mapping function between MDP states of a MDP within the MDP distribution and planning states of the planning domain is generated. Using the mapping function, a parameterized option for each of the lifted action models is defined. Using reinforcement learning, an intra-option policy for each of the parameterized options is trained.

Abstract: Automated improved computer mechanisms are provided for improving the way in which a lifted successor generation (LSG) solution to an artificial intelligence (AI) planning problem is processed. An artificial intelligence (AI) planning problem is received that includes definitions for a plurality of operators. An initial label set, which defines an initial version of an action space, is created, with each label corresponding to an operator. A label reduction is performed on the label set to obtain a reduced label set (seed set) that defines a reduced action space. The AI planning problem is represented as a LSG problem comprising a set of tables and a join query. A LSG module is executed on the LSG problem using the seed set to process the join query and generate applicable action(s) as a solution to the AI planning problem which are then output for further AI operations.

Abstract: Technology for: (i) receiving a domain-dependent artificial intelligence planning problem including definitions for a plurality of operators; (ii) creating an initial version of a label set, which defines an initial version of an action space, with the label set including a plurality of labels, and with each label of the plurality of labels respectively corresponding to the operators of the plurality of operators; (iii) performing, automatically and by machine logic, a label reduction on the initial version of the label set to obtain a reduced version of the label set that defines a reduced action space; and (iv) recasting the artificial planning problem as a first Markov decision process using the reduced version of label set.