International Business Machines Corporation

Akihiro Kishimoto

Adi Ionel Botea

Bei Chen

Beat Buesser

Radu Marinescu

Elizabeth Daly

Oznur Alkan

Yufang Hou

Vanessa Lopez Garcia

Hiroshi Kajino

Biplav Srivastava

Spyros Kotoulas

Charles Arthur Jochim

Yunfeng Zhang

Matthew Scott Davis

Kartik Talamadupula

Joe Naoum-Sawaya

Vera Liao

John Savage

Mattia Chiari

Larisa Shwartz

Stefano Braghin

Marco Luca Sbodio

Aliza Rivka Heching

Randall Lee Cogill

Seshu Tirupathi

Bradley Eck

Theodora Brisimi

Martin Wistuba

Parikshit Ram

Djallel Bouneffouf

Elaine M Branagh

Christian Muise

Bissan Ghaddar

Takahiko Koyama

Pitipong J Lin

Ambrish Rwat

Nicole L Lechelt

Fabrizio Cucci

Yin Long

Surya S K Sajja

Carlos Alzate

Ernesto Diaz-Aviles

Various embodiments are provided for intelligent resolution of conflicting information in a dialog system in a computing environment by a processor. Conflicting information relating to one or more queries may be detected between one or more users using the dialog system. One or more responses may be provided to resolve the conflicting information according to a knowledge domain.

Intelligent resolution of conflicting information in adaptive dialog systems

Embodiments for intelligent application scenario testing and error detection by a processor. One or more modified application scenarios may be automatically generated from an initial application scenario having configuration data and a plurality of operations relating to an error. The one or more modified application scenarios are variations of the initial application. The one or more modified application scenarios may be executed to detect the existence or non-existence of the error in the one or more modified application scenarios.

Intelligent application scenario testing and error detection

Various embodiments are provided for intelligent application of operational rules to operational data in a computing environment by a processor. One or more operational rules may be extracted and formalized from a knowledge graph, a domain knowledge, or a combination thereof describing one or more operational policies and conditions. The one or more operational rules may be applied to operational data to identify and filter non-compliant operational data.

Intelligent learning and application of operational rules

In an approach for discourse-level text optimization, a processor receives an initial text in a first language. A processor applies one or more operators to modify the initial text. A processor evaluates the modified text using a scoring function. A processor determines whether a score generated from the scoring function on the modified text is above a predefined threshold. In response to determining the score is above the predefined threshold, a processor outputs the modified text.

Discourse-level text optimization based on artificial intelligence planning

Embodiments for intelligent dynamic parking for autonomous vehicles by a processor. A dynamic parking plan may be determined for an autonomous vehicle to travel from a selected location and returning to the selected location within a selected time window according to a transportation network and one or more a variable parking functions in lieu of waiting at the selected location.

Intelligent dynamic parking for autonomous vehicles

Embodiments for intelligent discovery of preferred chemical reaction pathways to a synthesized target compound by a processor. Target compound data of a target compound a user intends to synthesize may be received. A preference score for one or more chemical reaction pathways to the target compound may be calculated based on one or more chemical reactions. One or more preferred chemical reaction pathways to the targeted compound may be determined using a modified retrosynthetic analysis according to the preference score for one or more chemical reactions.

Intelligent personalized chemical synthesis planning

In an approach to predictive contact tracing, a computer receives a query associated with contact tracing of a person with an infection. A computer retrieves timestamped location data associated with the person over a period of time. Based on the retrieved data, a computer creates a contact graph associated with the person, where the contact graph depicts one or more other people that were in contact with the person over the period of time. A computer retrieves medical data associated with the person and the one or more other people that were in contact with the person over the period of time. Based on the retrieved data, a computer builds a probabilistic model. A computer runs the probabilistic model to provide a prediction of a probability of infection of the one or more other people over the period of time as a result of being in contact with the person.

Predictive contact tracing

Embodiments for detection of energy consumption anomalies in one or more energy consumption systems in a cloud computing environment by a processor. Energy consumption anomalies associated with a selected facility, a selected object, or a combination thereof may be detected and diagnosed according to feedback and simulation data generated from a scenario-based simulation operation.

Operational energy consumption anomalies in intelligent energy consumption systems

Embodiments describe an approach for coordinating the travel of multiple vehicles traveling to a target destination, the embodiments describe generating a travel group, receiving travel group parameters from travel group members, synced GPS applications, and a weather application, generating a course of travel for the travel group members to reach a destination, and tracking each travel group member according to locations identified by the synced GPS applications. Additionally, embodiments describe determining that a subgroup of the travel group is no longer traveling within a pre-determined range of other travel group members; calculating an optimized course of travel for the subgroup to reunite with the other travel group members, adjusting the course of travel to include the optimized course of action for the subgroup, and causing each GPS application to direct the subgroup to travel according to the optimized course of travel.

Coordination management of multiple vehicles

Systems, computer-implemented methods and/or computer program products that facilitate answering questions that involve mathematical computations are provided. In one embodiment, a computer-implemented method comprises: transforming, by a system operatively coupled to a processor, a natural language query into a first logical representation and extrinsic knowledge into a second logical representation relevant to the natural language query; merging, by the system, the first logical representation and the second logical representation into a third logical representation; and generating, by the system, answers for the natural language query based on processing of the third logical representation.

Facilitating answering questions involving reasoning over quantitative information

A historical log of a plurality of a type of Internet-connected appliances is collected. a dynamic probabilistic graphical model of the time-lined interactions between subsystems and components of the appliances is generated. The probabilistic graphical model is trained by applying machine learning techniques and applying expert knowledge. A natural language processing (NLP) conversational user interface for diagnostic queries regarding operational errors is provided, and responsive to receive a diagnostic query in a conversational format from a user regarding a particular type, make, and model of an appliance of the plurality of the type of Internet-connected appliances, a diagnostic response is provided to the user interface using the natural language processing conversational format.

Interactive diagnostics of connected appliance devices

A computer generates a formal planning domain description. The computer receives a first text-based description of a domain in an AI environment. The domain includes an action and an associated attribute, and the description is written in natural language. The computer receives the first text-based description of the domain and extracts a first set of domain actions and associated action attributes. The computer receives audio-visual elements depicting the domain, generates a second text-based description, and extracts a second set of domain actions and associated action attributes. The computer constructs finite state machines corresponding to the extracted actions and attributes. The computer converts the FSMs into a symbolic model, written in a formal planning language, that describes the domain.

Generating symbolic domain models from multimodal data

Systems, computer-implemented methods, and computer program products to facilitate automated generation of a machine learning pipeline based on a pipeline grammar are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a pipeline structure generator component that generates a machine learning pipeline structure based on a pipeline grammar. The computer executable components can further comprise a pipeline optimizer component that selects one or more machine learning modules that achieve a defined objective to instantiate a machine learning pipeline based on the machine learning pipeline structure.

Automated generation of a machine learning pipeline

A method incrementally solves a current journey planning request from a user. The solving step performs a current search for at least one journey plan that satisfies the request by accessing a database storing journey planning information derived from results to a plurality of previous requests. The solving step stores, in the database, information discovered during the current search for responding to a subsequent request. The information discovered during the current search for responding to the request includes a reusable portion of a search graph, pairs of a state and a lower bound on a best arrival time and pairs of a state and an exact value for the arrival time. The lower bound is employed to increase an accuracy of a pre-computer heuristic function which guides the search based on state dominance in search spaces in which heuristic values are back propagated and stored in the database.

Incremental search based multi-modal journey planning

Embodiments for using an intelligent transaction optimization assistant by a processor. One or more actions to enhance a transaction experience of one or more users may be provided according to one or more selected constraints learned via a machine learning operation from previous transaction experiences, user behavior relating to the one or more previous transaction experiences, transaction experiences shared amongst entities associated with a social network, or a combination thereof.

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Dublin, Ireland

Akihiro Kishimoto