Abstract: An embodiment includes configuring a grounding based application (GBA) structure comprising a plurality of layers, where the plurality of layers comprises a first layer and a second layer, the first layer having a first node that receives an input associated with a child node responsive to an input query, and the second layer having a second node that outputs a response to the input query. The embodiment also includes evaluating, using the GBA structure, a logical inference based on the input query, where the evaluating comprises generating a first truth table associated with the first node, where the generating of the first truth table comprises retaining truth values resulting from a downward inference pass on the GBA structure and discarding truth values resulting from an upward inference pass on the GBA structure. The embodiment also includes outputting, responsive to the evaluating, an output truth value representative of the logical inference.

Abstract: An embodiment may include a processor that identifies a plurality of weights from the propositional logical neural network. The embodiment may convert the plurality of weights into a sparse matrix. The embodiment may convert a training set into a plurality of bound vectors. The embodiment may update the sparse matrix using a graphical processing unit (GPU). The embodiment may compute a loss parameter and based on determining the loss function is below threshold, update the plurality of weights of the propositional neural network.

Abstract: Methods, systems, and computer program products for cold-start service placement over on-demand resources are provided herein. A computer-implemented method includes obtaining a performance requirement profile comprising performance requirements of a service that vary over time; determining a plurality of incarnations for the service, wherein each incarnation is associated with a level of performance provided by the incarnation for the service, resource requirements of the incarnation, and a type of computing node the incarnation is configured to execute on; identifying computing nodes having different types and different resource capacities; jointly scheduling (i) the computing nodes and (ii) one or more of the incarnations on the computing nodes over a time interval such that a cumulative level of performance of the incarnations scheduled at each timepoint in the time interval satisfies the performance requirement profile of the service.