Honeywell International Inc.

Smart Information Flow Technologies, LLC

Robert Prescott Goldman

Christopher William Geib

Scott Ehrlich Friedman

Jeffrey Mathew Rye

David John Musliner

Michael Pelican

Pavan Kantharaju

Richard Gabriel Freedman

Ugur Kuter

A computing machine receives a plurality of observations. The computing machine generates an observation data structure. The computing machine extends, in accordance with the causal structures and hierarchical relationships, the observation data structure to include predicted states or predicted actions that are not from the plurality of observations. The computing machine reduces, in accordance with consistency rules stored in a memory of the computing machine, the extended observation data structure. The computing machine provides an output associated with the reduced observation data structure.

Computer architecture for plan recognition

A computing machine accesses a directed graph representing one or more sequences of actions. The directed graph comprises nodes and edges between the nodes. Each node is either a beginning node, an intermediate node, or an end node. Each intermediate is downstream from at least one beginning node and upstream from at least one end node. Each beginning node in at least a subset of the beginning nodes has an explainability value vector. The computing machine computes, for each first node from among a plurality of first nodes that are intermediate nodes or end nodes, a provenance value representing dependency of an explainability value vector of the first node on the one or more nodes upstream from the first node. The computing machine computes, for each first node, the explainability value vector. The computing machine provides a graphical output representing at least an explainability value vector of an end node.

Machine architecture for computerized plan analysis with provenance

A system and method for recognizing a goal of an actor in an environment by considering hypothesized unobserved actions. The method includes providing a plan library that includes a list of possible goals and corresponding plans. Actions of the actor are observed, and the occurrence of hypothesized unobserved actions are considered. Finally, one or more plans consistent with the observed actions and the consideration of hypothesized unobserved actions are identified.

Probabilistic goal recognition system and method incorporating inferred unobserved actions

Methods and apparatus for performing formal verification of a system defined by a set of automata are useful in facilitating computing efficiencies during the verification of an incremental system design. The various embodiments permit computing efficiencies by saving information generated during a verification of the system for use in subsequent verification runs. The saved information includes calculation results pertaining to instances or elements of the system that do not require modification for the next subsequent verification.

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Robert Prescott Goldman