Abstract: The present invention provides a method for learning a policy used by a computing system to perform a task, such delivery of one or more objects by the computing system. During a first time interval, the computing system determines a first state, a first action and a first reward value. As the computing system determines different states, actions and reward values during subsequent time intervals, a state description identifying the current sate, the current action, the current reward and a predicted action is stored. Responsive to a variance of a stored state description falling below a threshold value, the stored state description is used to modify one or more weights in the policy associated with the first state.

Abstract: A system includes a server processor coupled to the Internet. The server processor is configured to receive a problem statement from a user and automatically generate a search query based on the problem statement. The server processor is configured to use the search query to perform a database search of a plurality of databases that are stored in a machine readable storage media accessible via the Internet and/or in house data sources available within the internal computer network. The server processor is configured to generate and output an identification of a ranked set of documents and/or information to the user in response to the search query. The server processor is configured to receive from the user an identification of a subset of the ranked set, and automatically extract a set of names of experts from the subset.

Abstract: An action is performed in a spoken dialog system in response to a user's spoken utterance. A policy which maps belief states of user intent to actions is retrieved or created. A belief state is determined based on the spoken utterance, and an action is selected based on the determined belief state and the policy. The action is performed, and in one embodiment, involves requesting clarification of the spoken utterance from the user. Creating a policy may involve simulating user inputs and spoken dialog system interactions, and modifying policy parameters iteratively until a policy threshold is satisfied. In one embodiment, a belief state is determined by converting the spoken utterance into text, assigning the text to one or more dialog slots associated with nodes in a probabilistic ontology tree (POT), and determining a joint probability based on probability distribution tables in the POT and on the dialog slot assignments.

Abstract: The present invention provides a method for tracking entities, such as people, in an environment over long time periods. A region-based model is generated to model beliefs about entity locations. Each region corresponds to a discrete area representing a location where an entity is likely to be found. Each region includes one or more positions which more precisely specify the location of an entity within the region so that the region defines a probability distribution of the entity residing at different positions within the region. A region-based particle filtering method is applied to entities within the regions so that the probability distribution of each region is updated to indicate the likelihood of the entity residing in a particular region as the entity moves.

Abstract: An application service includes a device interface manager, an acquisition manager, and a job manager. The device interface is configured to receive, from a device, a print instruction defining a context. The acquisition manager is configured to use the context to acquire context compatible content. The job manager is configured to communicate a reference to a print service for accessing cached context compatible print data for the print instruction if the context compatible print data is available in a cache, and to otherwise communicate to the print service, context compatible print data for context compatible content acquired by the acquisition manager.

Abstract: An apparatus includes a builder, a template engine, and an assembler. The template engine is operable to identify a reference template associated with application selection. The assembler is configured to assemble a request from the identified reference template and state data indicated by the reference template. The builder is configured to process a user interface description retrieved using the request to cause the display of a user interface having one or more objects each associated with an application available from a network service, the applications having been selected according to the state data.

Abstract: The present invention provides a method for learning a policy used by a computing system to perform a task, such delivery of one or more objects by the computing system. During a first time interval, the computing system determines a first state, a first action and a first reward value. As the computing system determines different states, actions and reward values during subsequent time intervals, a state description identifying the current sate, the current action, the current reward and a predicted action is stored. Responsive to a variance of a stored state description falling below a threshold value, the stored state description is used to modify one or more weights in the policy associated with the first state.

Abstract: The present invention provides a method for tracking entities, such as people, in an environment over long time periods. A region-based model is generated to model beliefs about entity locations. Each region corresponds to a discrete area representing a location where an entity is likely to be found. Each region includes one or more positions which more precisely specify the location of an entity within the region so that the region defines a probability distribution of the entity residing at different positions within the region. A region-based particle filtering method is applied to entities within the regions so that the probability distribution of each region is updated to indicate the likelihood of the entity residing in a particular region as the entity moves.

Abstract: An input/output hub includes an inbound ordering queue (IOQ) to receive inbound transactions. All read and write transactions have a transaction completion. Peer-to-peer transactions are not permitted to reach a destination until after all prior writes in the IOQ have been completed. A write in a peer-to-peer transaction does not permit subsequent accesses to proceed until the write is guaranteed to be in an ordered domain of the destination. An IOQ read bypass buffer is provided to receive read transactions pushed from the IOQ to permit posted writes and read/write completions to progress through the IOQ. An outbound ordering queue (OOQ) stores outbound transactions and completions of the inbound transactions. The OOQ also issues write completions for posted writes. An OOQ read bypass buffer is provided to receive read transactions pushed from the OOQ to permit posted writes and read/write completions to progress through the OOQ.

Abstract: An input/output hub includes an inbound ordering queue (IOQ) to receive inbound transactions. All read and write transactions have a transaction completion. Peer-to-peer transactions are not permitted to reach a destination until after all prior writes in the IOQ have been completed. A write in a peer-to-peer transaction does not permit subsequent accesses to proceed until the write is guaranteed to be in an ordered domain of the destination. An IOQ read bypass buffer is provided to receive read transactions pushed from the IOQ to permit posted writes and read/write completions to progress through the IOQ. An outbound ordering queue (OOQ) stores outbound transactions and completions of the inbound transactions. The OOQ also issues write completions for posted writes. An OOQ read bypass buffer is provided to receive read transactions pushed from the OOQ to permit posted writes and read/write completions to progress through the OOQ.